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deciqai\n\nSummary: Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't mov...\n\nTags: latest:1.0.5\n\nVersion history:\n\nv1.0.5 | 2026-07-16T18:09:35.782Z | user\n\nDescription tail link + agents machine-readable metadata line (deciqai.com/s/okr-goal-setting.json)\n\nv1.0.4 | 2026-07-13T07:01:39.113Z | auto\n\nokr-goal-setting 1.0.4\n\n- Adds examples/ai-native-org-okrs-2024-2026.md, providing OKR guidance for AI-native organizations through 2026.\n- Updates usage guidance: includes explicit support for OKRs on AI features and cautions against vanity/Goodhart metrics.\n- Expands the references and context to include a \"2026 lens\" for future-facing OKR examples.\n- SKILL.md now references 225 knowledge skills (up from 164).\n- Removes obsolete skill-card.md file.\n\nv1.0.3 | 2026-07-08T11:13:05.136Z | user\n\nFooter now uses /c/ short link (fixes UTM truncation when SKILL.md is read in a terminal)\n\nv1.0.2 | 2026-07-08T00:57:42.070Z | user\n\nRefreshed content + GitHub star link in footer\n\nv1.0.1 | 2026-07-07T22:31:19.867Z | user\n\nAdd catalog categories and topics\n\nv1.0.0 | 2026-07-01T08:17:19.355Z | user\n\nInitial publish\n\nArchive index:\n\nArchive v1.0.5: 7 files, 14427 bytes\n\nFiles: examples/ai-native-org-okrs-2024-2026.md (5327b), examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md (5091b), examples/bono-one-campaign-2002-2018.md (4761b), references/sources.md (1614b), skill-card.md (2437b), SKILL.md (7182b), _meta.json (135b)\n\nFile v1.0.5:SKILL.md\n\n---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible. More: deciqai.com/c/okr-goal-setting\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with `north-star-metric`, `first-principles`, `metacognition`, `mece`.\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n- Setting OKRs on AI features and the draft KR is \"ship AI\" / \"increase AI usage\" (vanity/Goodhart metric — replace with outcome KRs)\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.\n\n**Step 3 — Calibrate ambition.** >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.\n\n**Step 4 — Cascade and align.** Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.\n\n**Step 5 — Run the quarterly cycle.** Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.\n\n**Step 6 — Score honestly.** Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.\n\n## Output: Quarterly OKR Sheet\n\n```\n# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual\n```\n\n*→ Method in Action: [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md) · [Bono's ONE Campaign](examples/bono-one-campaign-2002-2018.md)*\n\n*→ 2026 lens: [OKRs in an AI-native org (2024–2026)](examples/ai-native-org-okrs-2024-2026.md)*\n\n## Pack: OKR Patterns\n\n| Domain | Good KR (outcome) | Bad KR (activity) |\n|---|---|---|\n| Engineering | \"Reduce P0 incidents from 4/qtr to 1\" | \"Implement new deployment pipeline\" |\n| Sales | \"Close 15 new accounts at >$50k ACV\" | \"Hire 3 mid-market AEs\" |\n| Product | \"Increase day-7 retention from 22% to 40%\" | \"Launch new onboarding flow\" |\n| Marketing | \"Reach 50k organic blog visits/mo\" | \"Publish 10 blog posts\" |\n\n## Applying It Well\n\nLimit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] \"Ship feature X\" as a KR | Activity, not outcome. What change in the world does shipping X cause? That's the KR. |\n| [D] All KRs hit at 100% | Sandbagged. Recalibrate next quarter. |\n| [D] 30% achievement is normal | Goals unreasonable. Target is 60-80%. Recalibrate. |\n| [D] 20 KRs across 5 Objectives | Lack of focus. Cut to 1-2 Objectives, 3-5 KRs each. |\n| [D] OKRs tied to performance evaluation | Couples stretch with punishment; sandbagging becomes rational. Decouple. |\n| [D] Quarterly OKRs match what you'd have done anyway | Then they're a to-do list. The KR must change what the team chooses to do. |\n| [D] No scoring at quarter end | Skipping retrospective destroys the learning loop. Score every KR. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- KRs that are activities (\"ship,\" \"launch,\" \"hire,\" \"complete\") — 100% achievement every quarter\n- More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams\n- OKR scores tied to compensation — no quarterly retrospective\n\n## Verification\n\n- [ ] Each Objective: qualitative, ambitious, one sentence\n- [ ] Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)\n- [ ] Cross-team dependencies explicit; OKRs visible org-wide\n- [ ] Quarterly cadence established; scoring honest and decoupled from performance reviews\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 227 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/c/okr-goal-setting** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\n*Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/okr-goal-setting.json*\n\nFile v1.0.5:_meta.json\n\n{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.5\",\n \"publishedAt\": 1784225375782\n}\n\nFile v1.0.5:references/sources.md\n\n# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n- Strathern, M. (1997). \"'Improving ratings': audit in the British University system.\" *European Review* 5(3):305–321. The canonical modern statement of Goodhart's law (\"when a measure becomes a target, it ceases to be a good measure\") — the mechanism behind vanity/gamed AI-usage KRs.\n- Goodhart, C. A. E. (1975/1984). \"Problems of Monetary Management: The U.K. Experience,\" in *Monetary Theory and Practice: The UK Experience.* Macmillan. The original observation later named Goodhart's law.\n\nFile v1.0.5:examples/ai-native-org-okrs-2024-2026.md\n\n# Method in Action: OKRs in an AI-Native Org (2024–2026)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nBy 2024–2026, \"add AI\" had become the default objective for nearly every product team. The trap is structural: an objective like *\"ship AI features\"* or *\"increase AI usage\"* invites exactly the vanity and Goodhart metrics OKRs exist to prevent. This is a composite, illustrative case — a small, fast product team (roughly 8 engineers plus a PM and a designer) putting an AI assistant into an existing SaaS product — chosen to show how the six Process steps discipline AI bets. The numbers below are illustrative targets, not measured results.\n\nThe team's first-draft OKR was the common one:\n\n- Objective: *\"Make our product AI-first.\"*\n- KR1: Ship the AI assistant to GA.\n- KR2: Get 50% of users to try the assistant.\n- KR3: Reach 1M AI messages/month.\n\nEvery one of these is a trap. \"Ship to GA\" is an activity. \"Try the assistant\" and \"messages/month\" are usage-of-a-feature metrics that go up whether or not the feature helps anyone — a chatbot that fails and makes users retry inflates message count. This is Goodhart's law (\"when a measure becomes a target, it ceases to be a good measure\") applied to AI: optimize AI usage and you can grow the number while destroying trust.\n\n**Step 1 — Write the Objective.** Rewrite around the value the AI is supposed to create, not the AI itself. Objective: *\"Users trust the assistant to finish real work for them.\"* Qualitative, aspirational, and it passes the test — if achieved, it objectively matters, because it is the difference between a demo and a product.\n\n**Step 2 — Write 3-5 outcome KRs.** The move is to measure completed value and cost, never activity or raw usage:\n\n- KR1 (task success): \"≥60% of assistant sessions end in a task the user accepts without editing or retrying\" — measured via accept/undo/retry telemetry, not a thumbs-up widget people ignore. This is the anti-vanity core: a session only counts if it produced accepted work.\n- KR2 (retention): \"Day-30 retention of assistant users rises from 22% to 38%\" — retention is Goodhart-resistant because a gamed, unhelpful feature does not bring people back.\n- KR3 (unit economics): \"Cost-per-resolved-task falls from ~$0.40 to ≤$0.18\" — an outcome KR that ties model/inference spend to delivered value, so \"more usage\" is only good if it is *cheaper* delivered value. (Approximate, illustrative figures.)\n- KR4 (trust/quality guardrail): \"Human-flagged incorrect answers stay ≤2% of sampled sessions\" — a counter-metric so the team cannot buy KR1–KR3 by shipping a confident-but-wrong assistant.\n\nNote what is absent: no \"messages sent,\" no \"features shipped,\" no \"% who tried it.\" Contrast with the skill's pattern table — \"Increase AI usage\" is the 2020s cousin of \"Launch new onboarding flow.\"\n\n**Step 3 — Calibrate ambition.** 22%→38% retention and 60% clean task-success on a new AI surface are genuine stretches (roughly 50–70% likelihood, not near-certain). The ≤2% error guardrail is deliberately *not* a stretch — guardrails should be safe by design, because you do not want the team betting on the ceiling of harm.\n\n**Step 4 — Cascade and align.** In a small fast team the cascade is short but the cross-team dependency is load-bearing: the assistant's cost-per-task (KR3) depends on the platform/infra team's inference and caching work, so that dependency must appear explicitly in *both* teams' OKRs. Without it, the AI team is scored on a number another team controls.\n\n**Step 5 — Run the quarterly cycle.** AI features degrade and drift faster than classic software (model updates, prompt regressions, changing user behavior), so the mid-quarter check matters more than usual — a KR1 that was 0.6 at mid-quarter can quietly fall as usage broadens beyond early adopters.\n\n**Step 6 — Score honestly.** The honest-scoring payoff is the same as in the classic cases: a stretched 0.6 on \"accepted task completions\" is worth far more than a sandbagged 1.0 on \"messages sent.\" If the team had shipped the first-draft OKR, it could have hit 1M messages/month, declared victory, and still have built something users quietly abandoned — the exact failure OKRs are designed to make visible.\n\nThe mapped steps:\n1. Write the Objective: value the AI delivers (\"trusted to finish real work\"), not the AI itself (\"AI-first\")\n2. Write outcome KRs: accepted task completions, retention, cost-per-resolved-task, plus an error-rate guardrail — never \"usage\" or \"shipped\"\n3. Calibrate: retention and task-success as real stretches; the safety guardrail deliberately not a stretch\n4. Cascade: inference-cost KR shared explicitly with the infra team it depends on\n5. Run the cycle: tighter mid-quarter checks because AI quality drifts\n6. Score honestly: a stretched 0.6 on accepted work beats a sandbagged 1.0 on message volume\n\n*Sources: Doerr, J. (2018). *Measure What Matters.* Portfolio (OKR outcome-vs-activity discipline). Goodhart, C. A. E. (1975), and Strathern, M. (1997), \"'Improving ratings': audit in the British University system,\" *European Review* 5(3):305–321 — the canonical \"when a measure becomes a target…\" formulation. AI-metric and drift framing is the author's illustrative synthesis, not a cited measured result.*\n\nFile v1.0.5:examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md\n\n# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. When properly designed and deployed, they're a vaccine against fuzzy thinking — and fuzzy execution. KEY RESULTS benchmark and monitor HOW we get to the objective. Effective KRs are specific and time-bound, aggressive yet realistic. Most of all, they are measurable and verifiable.\"\n>\n> — Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. p. 7.\n\nBy the time Google's IPO documents were filed in 2004, the company had been running on OKRs for five years. Page and Brin credited OKRs in their founders' letter, and the framework became the default operating system across much of Silicon Valley over the next 15 years.\n\nThree operational lessons from Intel/Google and from the many failed OKR implementations:\n\n**First, the discipline that defeats most teams is \"outcome, not activity.\"** Activity-disguised-as-outcome KRs (\"ship feature,\" \"hire team,\" \"complete project\") are the dominant failure mode. They feel measurable but they decouple completion from business impact. A team can hit 100% of activity KRs while moving the business 0%.\n\n**Second, the 70% target is a *cultural* commitment, not a procedural one.** Organizations that punish people for missing 100% will be told 100% goals — which will be sandbagged 100% goals. The framework requires explicit leadership commitment that 60-80% on stretch is the right outcome, and that 100% on conservative is a problem to be flagged.\n\n**Third, OKRs work best when they are *few*.** A team with one Objective and three KRs has focus. A team with five Objectives and twenty KRs has a to-do list. Grove and Doerr both repeatedly emphasized: limit ruthlessly. Better to nail one ambitious Objective than to half-pursue five.\n\nThe framework has also produced documented failure modes:\n- **Bureaucracy.** Some companies have made OKR-setting itself a 6-week process; this defeats the framework.\n- **Performance-evaluation coupling.** Tying OKR scores to performance reviews and compensation routinely produces sandbagging; Google's official policy is that OKRs are *not* used for performance evaluation.\n- **Activity drift.** Without coaching, teams write activity-disguised KRs.\n\nFile v1.0.5:examples/bono-one-campaign-2002-2018.md\n\n# Method in Action: Bono's ONE Campaign (2002–2018)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nOKRs are usually told as a corporate story. The ONE Campaign shows the framework working in the opposite domain: a nonprofit advocacy organization with no revenue, no product, and a mission — end extreme poverty and preventable disease — that could absorb infinite effort without ever being \"done.\" In 2002, Bono co-founded DATA (Debt, AIDS, Trade, Africa) with fellow activists; it later folded into the ONE Campaign, an advocacy organization that lobbies governments rather than running programs. John Doerr, a board member, brought OKRs with him. Bono's account of what happened is documented in Doerr's *Measure What Matters* (2018).\n\nThe core problem was the activist's version of goal sprawl: passion generates causes faster than any organization can pursue them. Debt relief, AIDS treatment, trade rules, malaria, education — all worthy, all urgent. Without a forcing structure, an advocacy group campaigns on everything and moves nothing.\n\n**Writing the Objective (Step 1).** ONE's objectives were qualitative and genuinely aspirational — for example, securing debt cancellation for the world's poorest countries, or getting AIDS treatment to Africans who could not afford it. Each passed the test \"if achieved, would this objectively matter?\" — the answer was measured in lives.\n\n**Writing outcome KRs (Step 2).** The discipline that mattered was refusing activity metrics. For an advocacy group, the seductive KRs are activities: hold the concert, run the campaign, get the press coverage. ONE's key results were instead outcomes in the world it was trying to change: specific legislative and funding commitments from specific governments by specific dates. The 2005 Gleneagles G8 summit — where leaders committed to cancel the multilateral debt of the poorest countries and substantially increase aid to Africa — is the kind of external, verifiable result the campaigns were scored against. Concerts and coverage were tactics; the KR was whether the commitment materialized.\n\n**Calibrating and cascading (Steps 3–4).** Objectives like \"full debt cancellation\" were deliberate stretch goals — low-probability asks aimed at heads of state. The small organization cascaded them directly: board-level objectives set the one or two fights for the period, and every campaign team's work had to trace to them. What OKRs forced ONE to do, in Bono's telling, was say no — to drop good causes that did not serve the chosen objectives.\n\n**Scoring honestly (Steps 5–6).** The most consequential result came from the retrospective loop, not from a hit target. Reviewing what it was actually achieving, ONE confronted an uncomfortable finding: it was an organization campaigning *for* Africa that was not sufficiently *of* Africa — strategy set in Washington and London about a continent that wasn't in the room. Honest scoring turned that into a strategic pivot: ONE shifted from advocating for Africans to working with them, bringing African leaders into its board and staff and building its agenda with African partners. That is the framework functioning as designed — measurement changing what the organization chooses to do, not just recording it.\n\nThe broader results ONE and its predecessor counted among their era's wins — multilateral debt cancellation, the scale-up of AIDS funding through PEPFAR and the Global Fund, and the expansion of antiretroviral treatment from a wealthy-country privilege to millions of Africans — were collective achievements of many actors. But Doerr's and Bono's documented claim is narrower and defensible: OKRs gave a passion-driven organization focus, made its campaigns accountable to external outcomes rather than internal activity, and surfaced the strategic error that reshaped it.\n\nThe mapped steps:\n1. Write the Objective: qualitative, aspirational, life-scale — debt cancellation, AIDS treatment access\n2. Write 3-5 Key Results: external commitments secured (legislation, funding, debt cancelled), never activities (concerts, coverage)\n3. Calibrate ambition: deliberate stretch asks to heads of state, low individual probability\n4. Cascade and align: board objectives → campaign teams; OKRs used to say no to good-but-off-objective causes\n5. Run the cycle: campaigns scored against whether commitments (e.g., Gleneagles 2005) materialized\n6. Score honestly: retrospective exposed the \"for Africa, not with Africa\" gap → strategic pivot to African leadership and partnership\n\nPrimary source: Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. (Bono's ONE Campaign account.)\n\nFile v1.0.5:skill-card.md\n\n## Description:\n\nGuides agents through coaching teams to create measurable, outcome-focused OKRs and produce a quarterly OKR sheet.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[deciqai](https://clawhub.ai/user/deciqai)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nEmployees, team leads, founders, and operators use this skill to turn vague goals into a small set of qualitative objectives and quantitative key results, then review fit, ambition, dependencies, scoring, and cadence.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Generated OKRs may be measurable but still poorly matched to the organization's real priorities or constraints.\n\nMitigation: Review objectives and key results with accountable leaders before adoption, and adjust targets for business fit.\n\nRisk: Observed-example notes could modify a local playbook if an agent is allowed to write them back.\n\nMitigation: Permit write-back only when the user intentionally maintains this skill locally and has reviewed the notes.\n\nRisk: The OKR method can be counterproductive for teams with informal alignment that already works, inherently uncertain research output, or leadership that punishes stretch-goal misses.\n\nMitigation: Use the skill's fit checks before drafting OKRs and decline or reframe the workflow when those conditions apply.\n\n## Reference(s):\n\n- [Primary OKR Sources](references/sources.md)\n- [Andy Grove at Intel; John Doerr at Google](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)\n- [Bono's ONE Campaign](examples/bono-one-campaign-2002-2018.md)\n- [OKRs in an AI-Native Org](examples/ai-native-org-okrs-2024-2026.md)\n- [Google Re:Work OKR Guide](https://rework.withgoogle.com/guides/set-goals-with-okrs/)\n\n## Skill Output:\n\n**Output Type(s):** [Text, Markdown, Guidance]\n\n**Output Format:** [Markdown OKR sheet with coaching prompts, key results, dependencies, confidence notes, and verification checks]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May ask step-by-step follow-up questions before producing final OKRs.]\n\n## Skill Version(s):\n\n1.0.5 (source: release evidence)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nArchive v1.0.4: 7 files, 14339 bytes\n\nFiles: examples/ai-native-org-okrs-2024-2026.md (5327b), examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md (5091b), examples/bono-one-campaign-2002-2018.md (4761b), references/sources.md (1614b), skill-card.md (2475b), SKILL.md (7039b), _meta.json (135b)\n\nFile v1.0.4:SKILL.md\n\n---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible.\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with `north-star-metric`, `first-principles`, `metacognition`, `mece`.\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n- Setting OKRs on AI features and the draft KR is \"ship AI\" / \"increase AI usage\" (vanity/Goodhart metric — replace with outcome KRs)\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.\n\n**Step 3 — Calibrate ambition.** >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.\n\n**Step 4 — Cascade and align.** Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.\n\n**Step 5 — Run the quarterly cycle.** Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.\n\n**Step 6 — Score honestly.** Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.\n\n## Output: Quarterly OKR Sheet\n\n```\n# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual\n```\n\n*→ Method in Action: [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md) · [Bono's ONE Campaign](examples/bono-one-campaign-2002-2018.md)*\n\n*→ 2026 lens: [OKRs in an AI-native org (2024–2026)](examples/ai-native-org-okrs-2024-2026.md)*\n\n## Pack: OKR Patterns\n\n| Domain | Good KR (outcome) | Bad KR (activity) |\n|---|---|---|\n| Engineering | \"Reduce P0 incidents from 4/qtr to 1\" | \"Implement new deployment pipeline\" |\n| Sales | \"Close 15 new accounts at >$50k ACV\" | \"Hire 3 mid-market AEs\" |\n| Product | \"Increase day-7 retention from 22% to 40%\" | \"Launch new onboarding flow\" |\n| Marketing | \"Reach 50k organic blog visits/mo\" | \"Publish 10 blog posts\" |\n\n## Applying It Well\n\nLimit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] \"Ship feature X\" as a KR | Activity, not outcome. What change in the world does shipping X cause? That's the KR. |\n| [D] All KRs hit at 100% | Sandbagged. Recalibrate next quarter. |\n| [D] 30% achievement is normal | Goals unreasonable. Target is 60-80%. Recalibrate. |\n| [D] 20 KRs across 5 Objectives | Lack of focus. Cut to 1-2 Objectives, 3-5 KRs each. |\n| [D] OKRs tied to performance evaluation | Couples stretch with punishment; sandbagging becomes rational. Decouple. |\n| [D] Quarterly OKRs match what you'd have done anyway | Then they're a to-do list. The KR must change what the team chooses to do. |\n| [D] No scoring at quarter end | Skipping retrospective destroys the learning loop. Score every KR. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- KRs that are activities (\"ship,\" \"launch,\" \"hire,\" \"complete\") — 100% achievement every quarter\n- More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams\n- OKR scores tied to compensation — no quarterly retrospective\n\n## Verification\n\n- [ ] Each Objective: qualitative, ambitious, one sentence\n- [ ] Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)\n- [ ] Cross-team dependencies explicit; OKRs visible org-wide\n- [ ] Quarterly cadence established; scoring honest and decoupled from performance reviews\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 225 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/c/okr-goal-setting** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\nFile v1.0.4:_meta.json\n\n{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.4\",\n \"publishedAt\": 1783926099113\n}\n\nFile v1.0.4:references/sources.md\n\n# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n- Strathern, M. (1997). \"'Improving ratings': audit in the British University system.\" *European Review* 5(3):305–321. The canonical modern statement of Goodhart's law (\"when a measure becomes a target, it ceases to be a good measure\") — the mechanism behind vanity/gamed AI-usage KRs.\n- Goodhart, C. A. E. (1975/1984). \"Problems of Monetary Management: The U.K. Experience,\" in *Monetary Theory and Practice: The UK Experience.* Macmillan. The original observation later named Goodhart's law.\n\nFile v1.0.4:examples/ai-native-org-okrs-2024-2026.md\n\n# Method in Action: OKRs in an AI-Native Org (2024–2026)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nBy 2024–2026, \"add AI\" had become the default objective for nearly every product team. The trap is structural: an objective like *\"ship AI features\"* or *\"increase AI usage\"* invites exactly the vanity and Goodhart metrics OKRs exist to prevent. This is a composite, illustrative case — a small, fast product team (roughly 8 engineers plus a PM and a designer) putting an AI assistant into an existing SaaS product — chosen to show how the six Process steps discipline AI bets. The numbers below are illustrative targets, not measured results.\n\nThe team's first-draft OKR was the common one:\n\n- Objective: *\"Make our product AI-first.\"*\n- KR1: Ship the AI assistant to GA.\n- KR2: Get 50% of users to try the assistant.\n- KR3: Reach 1M AI messages/month.\n\nEvery one of these is a trap. \"Ship to GA\" is an activity. \"Try the assistant\" and \"messages/month\" are usage-of-a-feature metrics that go up whether or not the feature helps anyone — a chatbot that fails and makes users retry inflates message count. This is Goodhart's law (\"when a measure becomes a target, it ceases to be a good measure\") applied to AI: optimize AI usage and you can grow the number while destroying trust.\n\n**Step 1 — Write the Objective.** Rewrite around the value the AI is supposed to create, not the AI itself. Objective: *\"Users trust the assistant to finish real work for them.\"* Qualitative, aspirational, and it passes the test — if achieved, it objectively matters, because it is the difference between a demo and a product.\n\n**Step 2 — Write 3-5 outcome KRs.** The move is to measure completed value and cost, never activity or raw usage:\n\n- KR1 (task success): \"≥60% of assistant sessions end in a task the user accepts without editing or retrying\" — measured via accept/undo/retry telemetry, not a thumbs-up widget people ignore. This is the anti-vanity core: a session only counts if it produced accepted work.\n- KR2 (retention): \"Day-30 retention of assistant users rises from 22% to 38%\" — retention is Goodhart-resistant because a gamed, unhelpful feature does not bring people back.\n- KR3 (unit economics): \"Cost-per-resolved-task falls from ~$0.40 to ≤$0.18\" — an outcome KR that ties model/inference spend to delivered value, so \"more usage\" is only good if it is *cheaper* delivered value. (Approximate, illustrative figures.)\n- KR4 (trust/quality guardrail): \"Human-flagged incorrect answers stay ≤2% of sampled sessions\" — a counter-metric so the team cannot buy KR1–KR3 by shipping a confident-but-wrong assistant.\n\nNote what is absent: no \"messages sent,\" no \"features shipped,\" no \"% who tried it.\" Contrast with the skill's pattern table — \"Increase AI usage\" is the 2020s cousin of \"Launch new onboarding flow.\"\n\n**Step 3 — Calibrate ambition.** 22%→38% retention and 60% clean task-success on a new AI surface are genuine stretches (roughly 50–70% likelihood, not near-certain). The ≤2% error guardrail is deliberately *not* a stretch — guardrails should be safe by design, because you do not want the team betting on the ceiling of harm.\n\n**Step 4 — Cascade and align.** In a small fast team the cascade is short but the cross-team dependency is load-bearing: the assistant's cost-per-task (KR3) depends on the platform/infra team's inference and caching work, so that dependency must appear explicitly in *both* teams' OKRs. Without it, the AI team is scored on a number another team controls.\n\n**Step 5 — Run the quarterly cycle.** AI features degrade and drift faster than classic software (model updates, prompt regressions, changing user behavior), so the mid-quarter check matters more than usual — a KR1 that was 0.6 at mid-quarter can quietly fall as usage broadens beyond early adopters.\n\n**Step 6 — Score honestly.** The honest-scoring payoff is the same as in the classic cases: a stretched 0.6 on \"accepted task completions\" is worth far more than a sandbagged 1.0 on \"messages sent.\" If the team had shipped the first-draft OKR, it could have hit 1M messages/month, declared victory, and still have built something users quietly abandoned — the exact failure OKRs are designed to make visible.\n\nThe mapped steps:\n1. Write the Objective: value the AI delivers (\"trusted to finish real work\"), not the AI itself (\"AI-first\")\n2. Write outcome KRs: accepted task completions, retention, cost-per-resolved-task, plus an error-rate guardrail — never \"usage\" or \"shipped\"\n3. Calibrate: retention and task-success as real stretches; the safety guardrail deliberately not a stretch\n4. Cascade: inference-cost KR shared explicitly with the infra team it depends on\n5. Run the cycle: tighter mid-quarter checks because AI quality drifts\n6. Score honestly: a stretched 0.6 on accepted work beats a sandbagged 1.0 on message volume\n\n*Sources: Doerr, J. (2018). *Measure What Matters.* Portfolio (OKR outcome-vs-activity discipline). Goodhart, C. A. E. (1975), and Strathern, M. (1997), \"'Improving ratings': audit in the British University system,\" *European Review* 5(3):305–321 — the canonical \"when a measure becomes a target…\" formulation. AI-metric and drift framing is the author's illustrative synthesis, not a cited measured result.*\n\nFile v1.0.4:examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md\n\n# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. When properly designed and deployed, they're a vaccine against fuzzy thinking — and fuzzy execution. KEY RESULTS benchmark and monitor HOW we get to the objective. Effective KRs are specific and time-bound, aggressive yet realistic. Most of all, they are measurable and verifiable.\"\n>\n> — Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. p. 7.\n\nBy the time Google's IPO documents were filed in 2004, the company had been running on OKRs for five years. Page and Brin credited OKRs in their founders' letter, and the framework became the default operating system across much of Silicon Valley over the next 15 years.\n\nThree operational lessons from Intel/Google and from the many failed OKR implementations:\n\n**First, the discipline that defeats most teams is \"outcome, not activity.\"** Activity-disguised-as-outcome KRs (\"ship feature,\" \"hire team,\" \"complete project\") are the dominant failure mode. They feel measurable but they decouple completion from business impact. A team can hit 100% of activity KRs while moving the business 0%.\n\n**Second, the 70% target is a *cultural* commitment, not a procedural one.** Organizations that punish people for missing 100% will be told 100% goals — which will be sandbagged 100% goals. The framework requires explicit leadership commitment that 60-80% on stretch is the right outcome, and that 100% on conservative is a problem to be flagged.\n\n**Third, OKRs work best when they are *few*.** A team with one Objective and three KRs has focus. A team with five Objectives and twenty KRs has a to-do list. Grove and Doerr both repeatedly emphasized: limit ruthlessly. Better to nail one ambitious Objective than to half-pursue five.\n\nThe framework has also produced documented failure modes:\n- **Bureaucracy.** Some companies have made OKR-setting itself a 6-week process; this defeats the framework.\n- **Performance-evaluation coupling.** Tying OKR scores to performance reviews and compensation routinely produces sandbagging; Google's official policy is that OKRs are *not* used for performance evaluation.\n- **Activity drift.** Without coaching, teams write activity-disguised KRs.\n\nFile v1.0.4:examples/bono-one-campaign-2002-2018.md\n\n# Method in Action: Bono's ONE Campaign (2002–2018)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nOKRs are usually told as a corporate story. The ONE Campaign shows the framework working in the opposite domain: a nonprofit advocacy organization with no revenue, no product, and a mission — end extreme poverty and preventable disease — that could absorb infinite effort without ever being \"done.\" In 2002, Bono co-founded DATA (Debt, AIDS, Trade, Africa) with fellow activists; it later folded into the ONE Campaign, an advocacy organization that lobbies governments rather than running programs. John Doerr, a board member, brought OKRs with him. Bono's account of what happened is documented in Doerr's *Measure What Matters* (2018).\n\nThe core problem was the activist's version of goal sprawl: passion generates causes faster than any organization can pursue them. Debt relief, AIDS treatment, trade rules, malaria, education — all worthy, all urgent. Without a forcing structure, an advocacy group campaigns on everything and moves nothing.\n\n**Writing the Objective (Step 1).** ONE's objectives were qualitative and genuinely aspirational — for example, securing debt cancellation for the world's poorest countries, or getting AIDS treatment to Africans who could not afford it. Each passed the test \"if achieved, would this objectively matter?\" — the answer was measured in lives.\n\n**Writing outcome KRs (Step 2).** The discipline that mattered was refusing activity metrics. For an advocacy group, the seductive KRs are activities: hold the concert, run the campaign, get the press coverage. ONE's key results were instead outcomes in the world it was trying to change: specific legislative and funding commitments from specific governments by specific dates. The 2005 Gleneagles G8 summit — where leaders committed to cancel the multilateral debt of the poorest countries and substantially increase aid to Africa — is the kind of external, verifiable result the campaigns were scored against. Concerts and coverage were tactics; the KR was whether the commitment materialized.\n\n**Calibrating and cascading (Steps 3–4).** Objectives like \"full debt cancellation\" were deliberate stretch goals — low-probability asks aimed at heads of state. The small organization cascaded them directly: board-level objectives set the one or two fights for the period, and every campaign team's work had to trace to them. What OKRs forced ONE to do, in Bono's telling, was say no — to drop good causes that did not serve the chosen objectives.\n\n**Scoring honestly (Steps 5–6).** The most consequential result came from the retrospective loop, not from a hit target. Reviewing what it was actually achieving, ONE confronted an uncomfortable finding: it was an organization campaigning *for* Africa that was not sufficiently *of* Africa — strategy set in Washington and London about a continent that wasn't in the room. Honest scoring turned that into a strategic pivot: ONE shifted from advocating for Africans to working with them, bringing African leaders into its board and staff and building its agenda with African partners. That is the framework functioning as designed — measurement changing what the organization chooses to do, not just recording it.\n\nThe broader results ONE and its predecessor counted among their era's wins — multilateral debt cancellation, the scale-up of AIDS funding through PEPFAR and the Global Fund, and the expansion of antiretroviral treatment from a wealthy-country privilege to millions of Africans — were collective achievements of many actors. But Doerr's and Bono's documented claim is narrower and defensible: OKRs gave a passion-driven organization focus, made its campaigns accountable to external outcomes rather than internal activity, and surfaced the strategic error that reshaped it.\n\nThe mapped steps:\n1. Write the Objective: qualitative, aspirational, life-scale — debt cancellation, AIDS treatment access\n2. Write 3-5 Key Results: external commitments secured (legislation, funding, debt cancelled), never activities (concerts, coverage)\n3. Calibrate ambition: deliberate stretch asks to heads of state, low individual probability\n4. Cascade and align: board objectives → campaign teams; OKRs used to say no to good-but-off-objective causes\n5. Run the cycle: campaigns scored against whether commitments (e.g., Gleneagles 2005) materialized\n6. Score honestly: retrospective exposed the \"for Africa, not with Africa\" gap → strategic pivot to African leadership and partnership\n\nPrimary source: Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. (Bono's ONE Campaign account.)\n\nFile v1.0.4:skill-card.md\n\n## Description:
\nHelps agents coach users through writing outcome-focused objectives and measurable key results, including fit checks, calibration, alignment, scoring, and OKR examples.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[deciqai](https://clawhub.ai/user/deciqai)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nEmployees, managers, founders, and operating teams use this skill to turn vague or activity-based goals into focused quarterly OKRs with measurable outcomes, explicit dependencies, and honest scoring.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: OKR drafts can expose business strategy, internal metrics, and performance context if shared outside the intended audience.
\nMitigation: Review and redact company-specific goals, metrics, and dependencies before sharing generated OKRs or examples externally.
\nRisk: External links in the artifact may be useful references but are not required for safe runtime use.
\nMitigation: Treat included links as optional reading and do not require network access to use the skill.
\n\n\n## Reference(s):
\n- [ClawHub skill page](https://clawhub.ai/deciqai/skills/okr-goal-setting)
\n- [Sources - okr-goal-setting](references/sources.md)
\n- [How Google sets goals: OKRs](https://rework.withgoogle.com/guides/set-goals-with-okrs/)
\n- [Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)
\n- [Method in Action: Bono's ONE Campaign (2002-2018)](examples/bono-one-campaign-2002-2018.md)
\n- [Method in Action: OKRs in an AI-Native Org (2024-2026)](examples/ai-native-org-okrs-2024-2026.md)
\n\n\n## Skill Output:
\n**Output Type(s):** [Guidance, Markdown, Analysis]
\n**Output Format:** [Markdown OKR worksheet, coaching questions, and concise goal-setting guidance]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May pause for user input during step-by-step coaching before producing final OKRs.]
\n\n## Skill Version(s):
\n1.0.4 (source: release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v1.0.3: 6 files, 11089 bytes\n\nFiles: examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md (5091b), examples/bono-one-campaign-2002-2018.md (4761b), references/sources.md (1116b), skill-card.md (2344b), SKILL.md (6802b), _meta.json (135b)\n\nFile v1.0.3:SKILL.md\n\n---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible.\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with `north-star-metric`, `first-principles`, `metacognition`, `mece`.\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.\n\n**Step 3 — Calibrate ambition.** >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.\n\n**Step 4 — Cascade and align.** Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.\n\n**Step 5 — Run the quarterly cycle.** Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.\n\n**Step 6 — Score honestly.** Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.\n\n## Output: Quarterly OKR Sheet\n\n```\n# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual\n```\n\n*→ Method in Action: [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md) · [Bono's ONE Campaign](examples/bono-one-campaign-2002-2018.md)*\n\n## Pack: OKR Patterns\n\n| Domain | Good KR (outcome) | Bad KR (activity) |\n|---|---|---|\n| Engineering | \"Reduce P0 incidents from 4/qtr to 1\" | \"Implement new deployment pipeline\" |\n| Sales | \"Close 15 new accounts at >$50k ACV\" | \"Hire 3 mid-market AEs\" |\n| Product | \"Increase day-7 retention from 22% to 40%\" | \"Launch new onboarding flow\" |\n| Marketing | \"Reach 50k organic blog visits/mo\" | \"Publish 10 blog posts\" |\n\n## Applying It Well\n\nLimit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] \"Ship feature X\" as a KR | Activity, not outcome. What change in the world does shipping X cause? That's the KR. |\n| [D] All KRs hit at 100% | Sandbagged. Recalibrate next quarter. |\n| [D] 30% achievement is normal | Goals unreasonable. Target is 60-80%. Recalibrate. |\n| [D] 20 KRs across 5 Objectives | Lack of focus. Cut to 1-2 Objectives, 3-5 KRs each. |\n| [D] OKRs tied to performance evaluation | Couples stretch with punishment; sandbagging becomes rational. Decouple. |\n| [D] Quarterly OKRs match what you'd have done anyway | Then they're a to-do list. The KR must change what the team chooses to do. |\n| [D] No scoring at quarter end | Skipping retrospective destroys the learning loop. Score every KR. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- KRs that are activities (\"ship,\" \"launch,\" \"hire,\" \"complete\") — 100% achievement every quarter\n- More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams\n- OKR scores tied to compensation — no quarterly retrospective\n\n## Verification\n\n- [ ] Each Objective: qualitative, ambitious, one sentence\n- [ ] Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)\n- [ ] Cross-team dependencies explicit; OKRs visible org-wide\n- [ ] Quarterly cadence established; scoring honest and decoupled from performance reviews\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 164 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/c/okr-goal-setting** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\nFile v1.0.3:_meta.json\n\n{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.3\",\n \"publishedAt\": 1783509185136\n}\n\nFile v1.0.3:references/sources.md\n\n# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n\nFile v1.0.3:examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md\n\n# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. When properly designed and deployed, they're a vaccine against fuzzy thinking — and fuzzy execution. KEY RESULTS benchmark and monitor HOW we get to the objective. Effective KRs are specific and time-bound, aggressive yet realistic. Most of all, they are measurable and verifiable.\"\n>\n> — Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. p. 7.\n\nBy the time Google's IPO documents were filed in 2004, the company had been running on OKRs for five years. Page and Brin credited OKRs in their founders' letter, and the framework became the default operating system across much of Silicon Valley over the next 15 years.\n\nThree operational lessons from Intel/Google and from the many failed OKR implementations:\n\n**First, the discipline that defeats most teams is \"outcome, not activity.\"** Activity-disguised-as-outcome KRs (\"ship feature,\" \"hire team,\" \"complete project\") are the dominant failure mode. They feel measurable but they decouple completion from business impact. A team can hit 100% of activity KRs while moving the business 0%.\n\n**Second, the 70% target is a *cultural* commitment, not a procedural one.** Organizations that punish people for missing 100% will be told 100% goals — which will be sandbagged 100% goals. The framework requires explicit leadership commitment that 60-80% on stretch is the right outcome, and that 100% on conservative is a problem to be flagged.\n\n**Third, OKRs work best when they are *few*.** A team with one Objective and three KRs has focus. A team with five Objectives and twenty KRs has a to-do list. Grove and Doerr both repeatedly emphasized: limit ruthlessly. Better to nail one ambitious Objective than to half-pursue five.\n\nThe framework has also produced documented failure modes:\n- **Bureaucracy.** Some companies have made OKR-setting itself a 6-week process; this defeats the framework.\n- **Performance-evaluation coupling.** Tying OKR scores to performance reviews and compensation routinely produces sandbagging; Google's official policy is that OKRs are *not* used for performance evaluation.\n- **Activity drift.** Without coaching, teams write activity-disguised KRs.\n\nFile v1.0.3:examples/bono-one-campaign-2002-2018.md\n\n# Method in Action: Bono's ONE Campaign (2002–2018)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nOKRs are usually told as a corporate story. The ONE Campaign shows the framework working in the opposite domain: a nonprofit advocacy organization with no revenue, no product, and a mission — end extreme poverty and preventable disease — that could absorb infinite effort without ever being \"done.\" In 2002, Bono co-founded DATA (Debt, AIDS, Trade, Africa) with fellow activists; it later folded into the ONE Campaign, an advocacy organization that lobbies governments rather than running programs. John Doerr, a board member, brought OKRs with him. Bono's account of what happened is documented in Doerr's *Measure What Matters* (2018).\n\nThe core problem was the activist's version of goal sprawl: passion generates causes faster than any organization can pursue them. Debt relief, AIDS treatment, trade rules, malaria, education — all worthy, all urgent. Without a forcing structure, an advocacy group campaigns on everything and moves nothing.\n\n**Writing the Objective (Step 1).** ONE's objectives were qualitative and genuinely aspirational — for example, securing debt cancellation for the world's poorest countries, or getting AIDS treatment to Africans who could not afford it. Each passed the test \"if achieved, would this objectively matter?\" — the answer was measured in lives.\n\n**Writing outcome KRs (Step 2).** The discipline that mattered was refusing activity metrics. For an advocacy group, the seductive KRs are activities: hold the concert, run the campaign, get the press coverage. ONE's key results were instead outcomes in the world it was trying to change: specific legislative and funding commitments from specific governments by specific dates. The 2005 Gleneagles G8 summit — where leaders committed to cancel the multilateral debt of the poorest countries and substantially increase aid to Africa — is the kind of external, verifiable result the campaigns were scored against. Concerts and coverage were tactics; the KR was whether the commitment materialized.\n\n**Calibrating and cascading (Steps 3–4).** Objectives like \"full debt cancellation\" were deliberate stretch goals — low-probability asks aimed at heads of state. The small organization cascaded them directly: board-level objectives set the one or two fights for the period, and every campaign team's work had to trace to them. What OKRs forced ONE to do, in Bono's telling, was say no — to drop good causes that did not serve the chosen objectives.\n\n**Scoring honestly (Steps 5–6).** The most consequential result came from the retrospective loop, not from a hit target. Reviewing what it was actually achieving, ONE confronted an uncomfortable finding: it was an organization campaigning *for* Africa that was not sufficiently *of* Africa — strategy set in Washington and London about a continent that wasn't in the room. Honest scoring turned that into a strategic pivot: ONE shifted from advocating for Africans to working with them, bringing African leaders into its board and staff and building its agenda with African partners. That is the framework functioning as designed — measurement changing what the organization chooses to do, not just recording it.\n\nThe broader results ONE and its predecessor counted among their era's wins — multilateral debt cancellation, the scale-up of AIDS funding through PEPFAR and the Global Fund, and the expansion of antiretroviral treatment from a wealthy-country privilege to millions of Africans — were collective achievements of many actors. But Doerr's and Bono's documented claim is narrower and defensible: OKRs gave a passion-driven organization focus, made its campaigns accountable to external outcomes rather than internal activity, and surfaced the strategic error that reshaped it.\n\nThe mapped steps:\n1. Write the Objective: qualitative, aspirational, life-scale — debt cancellation, AIDS treatment access\n2. Write 3-5 Key Results: external commitments secured (legislation, funding, debt cancelled), never activities (concerts, coverage)\n3. Calibrate ambition: deliberate stretch asks to heads of state, low individual probability\n4. Cascade and align: board objectives → campaign teams; OKRs used to say no to good-but-off-objective causes\n5. Run the cycle: campaigns scored against whether commitments (e.g., Gleneagles 2005) materialized\n6. Score honestly: retrospective exposed the \"for Africa, not with Africa\" gap → strategic pivot to African leadership and partnership\n\nPrimary source: Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. (Bono's ONE Campaign account.)\n\nFile v1.0.3:skill-card.md\n\n## Description:
\nGuides agents through setting and reviewing OKRs with qualitative objectives, measurable key results, stretch calibration, alignment, and quarterly scoring.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[deciqai](https://clawhub.ai/user/deciqai)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nEmployees, managers, founders, and operating teams use this skill to convert vague goals into focused quarterly OKRs with measurable outcomes, explicit dependencies, honest scoring, and fit checks for when OKRs are inappropriate.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Generated OKRs may be treated as authoritative business planning or employee evaluation guidance.
\nMitigation: Review generated OKRs with responsible leaders before using them for planning, compensation, or performance decisions.
\nRisk: OKRs can be counterproductive for very small teams, deeply uncertain research work, or cultures that punish stretch-goal misses.
\nMitigation: Apply the skill's fit checks first, and do not use OKR scoring as a performance evaluation mechanism.
\n\n\n## Reference(s):
\n- [Primary Sources - OKR Goal Setting](references/sources.md)
\n- [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)
\n- [Bono's ONE Campaign, 2002-2018](examples/bono-one-campaign-2002-2018.md)
\n- [How Google sets goals: OKRs](https://rework.withgoogle.com/guides/set-goals-with-okrs/)
\n- [ClawHub Skill Page](https://clawhub.ai/deciqai/skills/okr-goal-setting)
\n\n\n## Skill Output:
\n**Output Type(s):** [Markdown, Guidance, Analysis]
\n**Output Format:** [Markdown coaching responses and quarterly OKR sheets]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May proceed step by step and pause for user input before drafting final OKRs.]
\n\n## Skill Version(s):
\n1.0.3 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v1.0.2: 6 files, 11068 bytes\n\nFiles: examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md (5091b), examples/bono-one-campaign-2002-2018.md (4761b), references/sources.md (1116b), skill-card.md (2199b), SKILL.md (6908b), _meta.json (135b)\n\nFile v1.0.2:SKILL.md\n\n---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible.\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with `north-star-metric`, `first-principles`, `metacognition`, `mece`.\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.\n\n**Step 3 — Calibrate ambition.** >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.\n\n**Step 4 — Cascade and align.** Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.\n\n**Step 5 — Run the quarterly cycle.** Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.\n\n**Step 6 — Score honestly.** Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.\n\n## Output: Quarterly OKR Sheet\n\n```\n# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual\n```\n\n*→ Method in Action: [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md) · [Bono's ONE Campaign](examples/bono-one-campaign-2002-2018.md)*\n\n## Pack: OKR Patterns\n\n| Domain | Good KR (outcome) | Bad KR (activity) |\n|---|---|---|\n| Engineering | \"Reduce P0 incidents from 4/qtr to 1\" | \"Implement new deployment pipeline\" |\n| Sales | \"Close 15 new accounts at >$50k ACV\" | \"Hire 3 mid-market AEs\" |\n| Product | \"Increase day-7 retention from 22% to 40%\" | \"Launch new onboarding flow\" |\n| Marketing | \"Reach 50k organic blog visits/mo\" | \"Publish 10 blog posts\" |\n\n## Applying It Well\n\nLimit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] \"Ship feature X\" as a KR | Activity, not outcome. What change in the world does shipping X cause? That's the KR. |\n| [D] All KRs hit at 100% | Sandbagged. Recalibrate next quarter. |\n| [D] 30% achievement is normal | Goals unreasonable. Target is 60-80%. Recalibrate. |\n| [D] 20 KRs across 5 Objectives | Lack of focus. Cut to 1-2 Objectives, 3-5 KRs each. |\n| [D] OKRs tied to performance evaluation | Couples stretch with punishment; sandbagging becomes rational. Decouple. |\n| [D] Quarterly OKRs match what you'd have done anyway | Then they're a to-do list. The KR must change what the team chooses to do. |\n| [D] No scoring at quarter end | Skipping retrospective destroys the learning loop. Score every KR. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- KRs that are activities (\"ship,\" \"launch,\" \"hire,\" \"complete\") — 100% achievement every quarter\n- More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams\n- OKR scores tied to compensation — no quarterly retrospective\n\n## Verification\n\n- [ ] Each Objective: qualitative, ambitious, one sentence\n- [ ] Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)\n- [ ] Cross-team dependencies explicit; OKRs visible org-wide\n- [ ] Quarterly cadence established; scoring honest and decoupled from performance reviews\n\n---\n\n*Part of **deciqAI Knowledge Skills** — 163 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/skills/okr-goal-setting?utm_source=clawhub&utm_medium=marketplace&utm_campaign=knowledge-skills&utm_content=okr-goal-setting** · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.*\n\nFile v1.0.2:_meta.json\n\n{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.2\",\n \"publishedAt\": 1783472262070\n}\n\nFile v1.0.2:references/sources.md\n\n# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n\nFile v1.0.2:examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md\n\n# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. When properly designed and deployed, they're a vaccine against fuzzy thinking — and fuzzy execution. KEY RESULTS benchmark and monitor HOW we get to the objective. Effective KRs are specific and time-bound, aggressive yet realistic. Most of all, they are measurable and verifiable.\"\n>\n> — Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. p. 7.\n\nBy the time Google's IPO documents were filed in 2004, the company had been running on OKRs for five years. Page and Brin credited OKRs in their founders' letter, and the framework became the default operating system across much of Silicon Valley over the next 15 years.\n\nThree operational lessons from Intel/Google and from the many failed OKR implementations:\n\n**First, the discipline that defeats most teams is \"outcome, not activity.\"** Activity-disguised-as-outcome KRs (\"ship feature,\" \"hire team,\" \"complete project\") are the dominant failure mode. They feel measurable but they decouple completion from business impact. A team can hit 100% of activity KRs while moving the business 0%.\n\n**Second, the 70% target is a *cultural* commitment, not a procedural one.** Organizations that punish people for missing 100% will be told 100% goals — which will be sandbagged 100% goals. The framework requires explicit leadership commitment that 60-80% on stretch is the right outcome, and that 100% on conservative is a problem to be flagged.\n\n**Third, OKRs work best when they are *few*.** A team with one Objective and three KRs has focus. A team with five Objectives and twenty KRs has a to-do list. Grove and Doerr both repeatedly emphasized: limit ruthlessly. Better to nail one ambitious Objective than to half-pursue five.\n\nThe framework has also produced documented failure modes:\n- **Bureaucracy.** Some companies have made OKR-setting itself a 6-week process; this defeats the framework.\n- **Performance-evaluation coupling.** Tying OKR scores to performance reviews and compensation routinely produces sandbagging; Google's official policy is that OKRs are *not* used for performance evaluation.\n- **Activity drift.** Without coaching, teams write activity-disguised KRs.\n\nFile v1.0.2:examples/bono-one-campaign-2002-2018.md\n\n# Method in Action: Bono's ONE Campaign (2002–2018)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nOKRs are usually told as a corporate story. The ONE Campaign shows the framework working in the opposite domain: a nonprofit advocacy organization with no revenue, no product, and a mission — end extreme poverty and preventable disease — that could absorb infinite effort without ever being \"done.\" In 2002, Bono co-founded DATA (Debt, AIDS, Trade, Africa) with fellow activists; it later folded into the ONE Campaign, an advocacy organization that lobbies governments rather than running programs. John Doerr, a board member, brought OKRs with him. Bono's account of what happened is documented in Doerr's *Measure What Matters* (2018).\n\nThe core problem was the activist's version of goal sprawl: passion generates causes faster than any organization can pursue them. Debt relief, AIDS treatment, trade rules, malaria, education — all worthy, all urgent. Without a forcing structure, an advocacy group campaigns on everything and moves nothing.\n\n**Writing the Objective (Step 1).** ONE's objectives were qualitative and genuinely aspirational — for example, securing debt cancellation for the world's poorest countries, or getting AIDS treatment to Africans who could not afford it. Each passed the test \"if achieved, would this objectively matter?\" — the answer was measured in lives.\n\n**Writing outcome KRs (Step 2).** The discipline that mattered was refusing activity metrics. For an advocacy group, the seductive KRs are activities: hold the concert, run the campaign, get the press coverage. ONE's key results were instead outcomes in the world it was trying to change: specific legislative and funding commitments from specific governments by specific dates. The 2005 Gleneagles G8 summit — where leaders committed to cancel the multilateral debt of the poorest countries and substantially increase aid to Africa — is the kind of external, verifiable result the campaigns were scored against. Concerts and coverage were tactics; the KR was whether the commitment materialized.\n\n**Calibrating and cascading (Steps 3–4).** Objectives like \"full debt cancellation\" were deliberate stretch goals — low-probability asks aimed at heads of state. The small organization cascaded them directly: board-level objectives set the one or two fights for the period, and every campaign team's work had to trace to them. What OKRs forced ONE to do, in Bono's telling, was say no — to drop good causes that did not serve the chosen objectives.\n\n**Scoring honestly (Steps 5–6).** The most consequential result came from the retrospective loop, not from a hit target. Reviewing what it was actually achieving, ONE confronted an uncomfortable finding: it was an organization campaigning *for* Africa that was not sufficiently *of* Africa — strategy set in Washington and London about a continent that wasn't in the room. Honest scoring turned that into a strategic pivot: ONE shifted from advocating for Africans to working with them, bringing African leaders into its board and staff and building its agenda with African partners. That is the framework functioning as designed — measurement changing what the organization chooses to do, not just recording it.\n\nThe broader results ONE and its predecessor counted among their era's wins — multilateral debt cancellation, the scale-up of AIDS funding through PEPFAR and the Global Fund, and the expansion of antiretroviral treatment from a wealthy-country privilege to millions of Africans — were collective achievements of many actors. But Doerr's and Bono's documented claim is narrower and defensible: OKRs gave a passion-driven organization focus, made its campaigns accountable to external outcomes rather than internal activity, and surfaced the strategic error that reshaped it.\n\nThe mapped steps:\n1. Write the Objective: qualitative, aspirational, life-scale — debt cancellation, AIDS treatment access\n2. Write 3-5 Key Results: external commitments secured (legislation, funding, debt cancelled), never activities (concerts, coverage)\n3. Calibrate ambition: deliberate stretch asks to heads of state, low individual probability\n4. Cascade and align: board objectives → campaign teams; OKRs used to say no to good-but-off-objective causes\n5. Run the cycle: campaigns scored against whether commitments (e.g., Gleneagles 2005) materialized\n6. Score honestly: retrospective exposed the \"for Africa, not with Africa\" gap → strategic pivot to African leadership and partnership\n\nPrimary source: Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. (Bono's ONE Campaign account.)\n\nFile v1.0.2:skill-card.md\n\n## Description:
\nHelps agents coach teams through writing measurable objectives, outcome-focused key results, ambition calibration, alignment, scoring, and quarterly OKR review.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[deciqai](https://clawhub.ai/user/deciqai)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nEmployees, managers, founders, and operating teams use this skill to turn vague or activity-based goals into focused quarterly OKRs with measurable outcomes, explicit dependencies, and honest scoring.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: OKR planning can include sensitive company goals, metrics, targets, dependencies, and performance context.
\nMitigation: Avoid sharing confidential planning details unless the deployment environment is approved for that information.
\nRisk: Publisher and external reference links may lead outside NVIDIA-controlled properties.
\nMitigation: Review third-party publisher and reference links separately before relying on them.
\n\n\n## Reference(s):
\n- [Sources - okr-goal-setting](references/sources.md)
\n- [Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)
\n- [Method in Action: Bono's ONE Campaign (2002-2018)](examples/bono-one-campaign-2002-2018.md)
\n- [How Google sets goals: OKRs](https://rework.withgoogle.com/guides/set-goals-with-okrs/)
\n\n\n## Skill Output:
\n**Output Type(s):** [guidance, markdown, text]
\n**Output Format:** [Markdown OKR sheet and step-by-step coaching guidance]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [Plain-text coaching output; no executable code or external tool calls.]
\n\n## Skill Version(s):
\n1.0.2 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v1.0.1: 5 files, 8438 bytes\n\nFiles: examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md (5091b), references/sources.md (1116b), skill-card.md (2156b), SKILL.md (6696b), _meta.json (135b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible.\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with [`north-star-metric`](../north-star-metric/SKILL.md), [`first-principles`](../first-principles/SKILL.md), [`metacognition`](../metacognition/SKILL.md), [`mece`](../mece/SKILL.md).\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.\n\n**Step 3 — Calibrate ambition.** >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.\n\n**Step 4 — Cascade and align.** Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.\n\n**Step 5 — Run the quarterly cycle.** Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.\n\n**Step 6 — Score honestly.** Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.\n\n## Output: Quarterly OKR Sheet\n\n```\n# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual\n```\n\n*→ Method in Action: [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)*\n\n## Pack: OKR Patterns\n\n| Domain | Good KR (outcome) | Bad KR (activity) |\n|---|---|---|\n| Engineering | \"Reduce P0 incidents from 4/qtr to 1\" | \"Implement new deployment pipeline\" |\n| Sales | \"Close 15 new accounts at >$50k ACV\" | \"Hire 3 mid-market AEs\" |\n| Product | \"Increase day-7 retention from 22% to 40%\" | \"Launch new onboarding flow\" |\n| Marketing | \"Reach 50k organic blog visits/mo\" | \"Publish 10 blog posts\" |\n\n## Applying It Well\n\nLimit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] \"Ship feature X\" as a KR | Activity, not outcome. What change in the world does shipping X cause? That's the KR. |\n| [D] All KRs hit at 100% | Sandbagged. Recalibrate next quarter. |\n| [D] 30% achievement is normal | Goals unreasonable. Target is 60-80%. Recalibrate. |\n| [D] 20 KRs across 5 Objectives | Lack of focus. Cut to 1-2 Objectives, 3-5 KRs each. |\n| [D] OKRs tied to performance evaluation | Couples stretch with punishment; sandbagging becomes rational. Decouple. |\n| [D] Quarterly OKRs match what you'd have done anyway | Then they're a to-do list. The KR must change what the team chooses to do. |\n| [D] No scoring at quarter end | Skipping retrospective destroys the learning loop. Score every KR. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- KRs that are activities (\"ship,\" \"launch,\" \"hire,\" \"complete\") — 100% achievement every quarter\n- More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams\n- OKR scores tied to compensation — no quarterly retrospective\n\n## Verification\n\n- [ ] Each Objective: qualitative, ambitious, one sentence\n- [ ] Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)\n- [ ] Cross-team dependencies explicit; OKRs visible org-wide\n- [ ] Quarterly cadence established; scoring honest and decoupled from performance reviews\n\n---\n\n*Part of **deciqAI Knowledge Skills** — open-source thinking skills that make rigor executable for AI agents. Built by deciqAI · https://deciqai.com · Contributions welcome — see the template at the repo root.*\n\nFile v1.0.1:_meta.json\n\n{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.1\",\n \"publishedAt\": 1783463479867\n}\n\nFile v1.0.1:references/sources.md\n\n# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n\nFile v1.0.1:examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md\n\n# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. When properly designed and deployed, they're a vaccine against fuzzy thinking — and fuzzy execution. KEY RESULTS benchmark and monitor HOW we get to the objective. Effective KRs are specific and time-bound, aggressive yet realistic. Most of all, they are measurable and verifiable.\"\n>\n> — Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. p. 7.\n\nBy the time Google's IPO documents were filed in 2004, the company had been running on OKRs for five years. Page and Brin credited OKRs in their founders' letter, and the framework became the default operating system across much of Silicon Valley over the next 15 years.\n\nThree operational lessons from Intel/Google and from the many failed OKR implementations:\n\n**First, the discipline that defeats most teams is \"outcome, not activity.\"** Activity-disguised-as-outcome KRs (\"ship feature,\" \"hire team,\" \"complete project\") are the dominant failure mode. They feel measurable but they decouple completion from business impact. A team can hit 100% of activity KRs while moving the business 0%.\n\n**Second, the 70% target is a *cultural* commitment, not a procedural one.** Organizations that punish people for missing 100% will be told 100% goals — which will be sandbagged 100% goals. The framework requires explicit leadership commitment that 60-80% on stretch is the right outcome, and that 100% on conservative is a problem to be flagged.\n\n**Third, OKRs work best when they are *few*.** A team with one Objective and three KRs has focus. A team with five Objectives and twenty KRs has a to-do list. Grove and Doerr both repeatedly emphasized: limit ruthlessly. Better to nail one ambitious Objective than to half-pursue five.\n\nThe framework has also produced documented failure modes:\n- **Bureaucracy.** Some companies have made OKR-setting itself a 6-week process; this defeats the framework.\n- **Performance-evaluation coupling.** Tying OKR scores to performance reviews and compensation routinely produces sandbagging; Google's official policy is that OKRs are *not* used for performance evaluation.\n- **Activity drift.** Without coaching, teams write activity-disguised KRs.\n\nFile v1.0.1:skill-card.md\n\n## Description:
\nGuides teams through writing measurable, outcome-focused Objectives and Key Results, including fit checks, calibration, alignment, quarterly scoring, and common OKR failure patterns.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[deciqai](https://clawhub.ai/user/deciqai)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nEmployees, managers, founders, and strategy operators use this skill to turn vague team goals into quarterly OKRs with qualitative objectives, quantitative key results, explicit dependencies, and honest scoring.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Generated OKRs may still contain activity-based or unrealistic key results if users provide weak inputs.
\nMitigation: Review each key result for quantitative outcome focus, time bounds, and realistic stretch before adoption.
\nRisk: Using OKR scores as compensation or performance review inputs can encourage sandbagging and undermine stretch goals.
\nMitigation: Keep OKR scoring transparent and decoupled from compensation and performance evaluation.
\n\n\n## Reference(s):
\n- [Sources - okr-goal-setting](references/sources.md)
\n- [Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)
\n- [How Google sets goals: OKRs](https://rework.withgoogle.com/guides/set-goals-with-okrs/)
\n\n\n## Skill Output:
\n**Output Type(s):** [Markdown, Guidance, Analysis]
\n**Output Format:** [Markdown coaching responses and quarterly OKR sheets]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May stop at explicit wait points during step-by-step coaching.]
\n\n## Skill Version(s):
\n1.0.1 (source: server release evidence)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
\n\nArchive v1.0.0: 5 files, 8643 bytes\n\nFiles: examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md (5091b), references/sources.md (1116b), skill-card.md (2640b), SKILL.md (6696b), _meta.json (135b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible.\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with [`north-star-metric`](../north-star-metric/SKILL.md), [`first-principles`](../first-principles/SKILL.md), [`metacognition`](../metacognition/SKILL.md), [`mece`](../mece/SKILL.md).\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.\n\n**Step 3 — Calibrate ambition.** >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.\n\n**Step 4 — Cascade and align.** Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.\n\n**Step 5 — Run the quarterly cycle.** Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.\n\n**Step 6 — Score honestly.** Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.\n\n## Output: Quarterly OKR Sheet\n\n```\n# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual\n```\n\n*→ Method in Action: [Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)*\n\n## Pack: OKR Patterns\n\n| Domain | Good KR (outcome) | Bad KR (activity) |\n|---|---|---|\n| Engineering | \"Reduce P0 incidents from 4/qtr to 1\" | \"Implement new deployment pipeline\" |\n| Sales | \"Close 15 new accounts at >$50k ACV\" | \"Hire 3 mid-market AEs\" |\n| Product | \"Increase day-7 retention from 22% to 40%\" | \"Launch new onboarding flow\" |\n| Marketing | \"Reach 50k organic blog visits/mo\" | \"Publish 10 blog posts\" |\n\n## Applying It Well\n\nLimit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.\n\n*→ Primary sources: [references/sources.md](references/sources.md)*\n\n## Common Rationalizations\n\n**[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.**\n\n| Fake move | Reality |\n|---|---|\n| [D] \"Ship feature X\" as a KR | Activity, not outcome. What change in the world does shipping X cause? That's the KR. |\n| [D] All KRs hit at 100% | Sandbagged. Recalibrate next quarter. |\n| [D] 30% achievement is normal | Goals unreasonable. Target is 60-80%. Recalibrate. |\n| [D] 20 KRs across 5 Objectives | Lack of focus. Cut to 1-2 Objectives, 3-5 KRs each. |\n| [D] OKRs tied to performance evaluation | Couples stretch with punishment; sandbagging becomes rational. Decouple. |\n| [D] Quarterly OKRs match what you'd have done anyway | Then they're a to-do list. The KR must change what the team chooses to do. |\n| [D] No scoring at quarter end | Skipping retrospective destroys the learning loop. Score every KR. |\n| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |\n\n## Red Flags\n\n- KRs that are activities (\"ship,\" \"launch,\" \"hire,\" \"complete\") — 100% achievement every quarter\n- More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams\n- OKR scores tied to compensation — no quarterly retrospective\n\n## Verification\n\n- [ ] Each Objective: qualitative, ambitious, one sentence\n- [ ] Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)\n- [ ] Cross-team dependencies explicit; OKRs visible org-wide\n- [ ] Quarterly cadence established; scoring honest and decoupled from performance reviews\n\n---\n\n*Part of **deciqAI Knowledge Skills** — open-source thinking skills that make rigor executable for AI agents. Built by deciqAI · https://deciqai.com · Contributions welcome — see the template at the repo root.*\n\nFile v1.0.0:_meta.json\n\n{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.0\",\n \"publishedAt\": 1782893839355\n}\n\nFile v1.0.0:references/sources.md\n\n# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n\nFile v1.0.0:examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md\n\n# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. When properly designed and deployed, they're a vaccine against fuzzy thinking — and fuzzy execution. KEY RESULTS benchmark and monitor HOW we get to the objective. Effective KRs are specific and time-bound, aggressive yet realistic. Most of all, they are measurable and verifiable.\"\n>\n> — Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. p. 7.\n\nBy the time Google's IPO documents were filed in 2004, the company had been running on OKRs for five years. Page and Brin credited OKRs in their founders' letter, and the framework became the default operating system across much of Silicon Valley over the next 15 years.\n\nThree operational lessons from Intel/Google and from the many failed OKR implementations:\n\n**First, the discipline that defeats most teams is \"outcome, not activity.\"** Activity-disguised-as-outcome KRs (\"ship feature,\" \"hire team,\" \"complete project\") are the dominant failure mode. They feel measurable but they decouple completion from business impact. A team can hit 100% of activity KRs while moving the business 0%.\n\n**Second, the 70% target is a *cultural* commitment, not a procedural one.** Organizations that punish people for missing 100% will be told 100% goals — which will be sandbagged 100% goals. The framework requires explicit leadership commitment that 60-80% on stretch is the right outcome, and that 100% on conservative is a problem to be flagged.\n\n**Third, OKRs work best when they are *few*.** A team with one Objective and three KRs has focus. A team with five Objectives and twenty KRs has a to-do list. Grove and Doerr both repeatedly emphasized: limit ruthlessly. Better to nail one ambitious Objective than to half-pursue five.\n\nThe framework has also produced documented failure modes:\n- **Bureaucracy.** Some companies have made OKR-setting itself a 6-week process; this defeats the framework.\n- **Performance-evaluation coupling.** Tying OKR scores to performance reviews and compensation routinely produces sandbagging; Google's official policy is that OKRs are *not* used for performance evaluation.\n- **Activity drift.** Without coaching, teams write activity-disguised KRs.\n\nFile v1.0.0:skill-card.md\n\n## Description:
\nGuides agents through setting measurable OKRs, calibrating stretch goals, aligning teams, and producing a quarterly OKR sheet.
\n\nThis skill is ready for commercial/non-commercial use.
\n\n## Publisher:
\n[deciqai](https://clawhub.ai/user/deciqai)
\n\n### License/Terms of Use:
\nMIT-0
\n\n\n## Use Case:
\nEmployees, managers, founders, and operating teams use this skill to turn vague or activity-based goals into focused quarterly objectives with measurable key results, explicit dependencies, and honest scoring. It is useful when teams need stronger alignment, stretch calibration, or a repeatable OKR cadence.
\n\n### Deployment Geography for Use:
\nGlobal
\n\n## Known Risks and Mitigations:
\nRisk: Generated OKRs may be treated as binding management policy or may miss important team context.
\nMitigation: Review generated objectives and key results with accountable leaders before adoption, and adapt them to current strategy, constraints, and team maturity.
\nRisk: Tying stretch OKR scores directly to compensation can encourage sandbagging and weaken honest scoring.
\nMitigation: Keep OKR scoring separate from performance evaluation and review scores as learning signals rather than compensation inputs.
\nRisk: Business goals shared in prompts may contain sensitive strategy, revenue, hiring, or product information.
\nMitigation: Avoid sharing confidential details unless the runtime and data-handling environment are approved for that information.
\n\n\n## Reference(s):
\n- [Sources - okr-goal-setting](references/sources.md)
\n- [Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999](examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md)
\n- [How Google sets goals: OKRs](https://rework.withgoogle.com/guides/set-goals-with-okrs/)
\n- [ClawHub skill page](https://clawhub.ai/deciqai/skills/okr-goal-setting)
\n\n\n## Skill Output:
\n**Output Type(s):** [Guidance, Markdown]
\n**Output Format:** [Markdown with structured OKR sheet templates, coaching questions, checklists, and scoring guidance]
\n**Output Parameters:** [1D]
\n**Other Properties Related to Output:** [May pause for user input during coach-mode steps before producing final OKRs.]
\n\n## Skill Version(s):
\n1.0.0 (source: server release metadata)
\n\n## Ethical Considerations:
\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.
","readmeExcerpt":"Skill: OKRs (Objectives and Key Results) Owner: deciqai Summary: Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't mov... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:09:35.782Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/okr-goal-setting.json) v1.0.4 | 2026-07-","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual"},{"language":"text","snippet":"# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual"},{"language":"text","snippet":"# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual"},{"language":"text","snippet":"# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual"},{"language":"text","snippet":"# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual"},{"language":"text","snippet":"# OKR: Q[X] [Year] — [Team]\n## Objective 1: \n- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])\n- KR2: ... KR3: ...\n## Dependencies: depends on / others depend on us for\n## Confidence: start / mid-quarter / end actual"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: okr-goal-setting\ndescription: \"Activate when: user says 'set OKRs', 'write objectives and key results', 'our goals aren't measurable', 'teams are hitting targets but the business isn't moving', 'we need stretch goals', 'align teams around quarterly goals', or mentions Doerr / Measure What Matters.\n Do NOT activate when: the team is under ~10 people with informal alignment that works fine; output is genuinely uncertain (deep R&D, research labs) making 'achieve X by Q3' structurally impossible. More: deciqai.com/c/okr-goal-setting\"\n---\n\n# OKRs (Objectives and Key Results)\n\n## Overview\n\nOKRs separate ambition from measurement: an **Objective** is qualitative and aspirational; **Key Results** (3-5) are quantitative outcomes proving the objective was reached. KRs must be *outcomes*, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).\n\nComposes with `north-star-metric`, `first-principles`, `metacognition`, `mece`.\n\n## When to Use\n\n- Team goals are vague, unmeasurable, or just \"complete project X\" lists\n- Teams hitting all goals but the business isn't moving (sandbagging signal)\n- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure\n- Setting OKRs on AI features and the draft KR is \"ship AI\" / \"increase AI usage\" (vanity/Goodhart metric — replace with outcome KRs)\n\n**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.\n\n## Coaching Novices (Adaptive Front Door)\n\n- **Engine mode:** user has a concrete case → run The Process directly.\n- **Coach mode:** user unfamiliar or no concrete case → guide step by step.\n\nIn Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.\n\n1. OKRs separate the inspirational *what we're trying to be* (Objective) from the measurable *how we'll know* (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.\n2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.\n3. Elicit the actual goal the team is trying to set.\n> **[WAIT — do not advance until user responds]**\n4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?\n> **[WAIT — do not advance until user responds]**\n5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.\n> **[WAIT — do not advance until user responds]**\n\n## The Process\n\n**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: \"If achieved, would this objectively matter to the business?\"\n\n**Step 2 — Write 3-5 Key Results.** Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. \"Ship v2.0\" = activity (wrong). \"Reach 100k WAU on v2.0 within 30 days\" = "},{"path":"_meta.json","content":"{\n \"ownerId\": \"kn754b8sk22s8c6gjxt02bftbn88q7ye\",\n \"slug\": \"okr-goal-setting\",\n \"version\": \"1.0.5\",\n \"publishedAt\": 1784225375782\n}"},{"path":"references/sources.md","content":"# Sources — okr-goal-setting\n\n> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*\n\n- Grove, A. S. (1983/1995). *High Output Management.* Random House (Vintage). ISBN 978-0679762881. Chapter 6 introduces the iMBO system that became OKRs. The canonical primary source.\n- Doerr, J. (2018). *Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs.* Portfolio. ISBN 978-0525536222. The popular treatment with Google case detail.\n- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).\n- Wodtke, C. (2016). *Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results.* Cucina Media. ISBN 978-0996006040. The operational guide for OKR implementation.\n- Niven, P. R. & Lamorte, B. (2016). *Objectives and Key Results: Driving Focus, Alignment, and Engagement with OKRs.* Wiley. ISBN 978-1119252399. A second comprehensive practitioner guide.\n- \"How Google sets goals: OKRs.\" Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.\n- Strathern, M. (1997). \"'Improving ratings': audit in the British University system.\" *European Review* 5(3):305–321. The canonical modern statement of Goodhart's law (\"when a measure becomes a target, it ceases to be a good measure\") — the mechanism behind vanity/gamed AI-usage KRs.\n- Goodhart, C. A. E. (1975/1984). \"Problems of Monetary Management: The U.K. Experience,\" in *Monetary Theory and Practice: The UK Experience.* Macmillan. The original observation later named Goodhart's law."},{"path":"examples/ai-native-org-okrs-2024-2026.md","content":"# Method in Action: OKRs in an AI-Native Org (2024–2026)\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nBy 2024–2026, \"add AI\" had become the default objective for nearly every product team. The trap is structural: an objective like *\"ship AI features\"* or *\"increase AI usage\"* invites exactly the vanity and Goodhart metrics OKRs exist to prevent. This is a composite, illustrative case — a small, fast product team (roughly 8 engineers plus a PM and a designer) putting an AI assistant into an existing SaaS product — chosen to show how the six Process steps discipline AI bets. The numbers below are illustrative targets, not measured results.\n\nThe team's first-draft OKR was the common one:\n\n- Objective: *\"Make our product AI-first.\"*\n- KR1: Ship the AI assistant to GA.\n- KR2: Get 50% of users to try the assistant.\n- KR3: Reach 1M AI messages/month.\n\nEvery one of these is a trap. \"Ship to GA\" is an activity. \"Try the assistant\" and \"messages/month\" are usage-of-a-feature metrics that go up whether or not the feature helps anyone — a chatbot that fails and makes users retry inflates message count. This is Goodhart's law (\"when a measure becomes a target, it ceases to be a good measure\") applied to AI: optimize AI usage and you can grow the number while destroying trust.\n\n**Step 1 — Write the Objective.** Rewrite around the value the AI is supposed to create, not the AI itself. Objective: *\"Users trust the assistant to finish real work for them.\"* Qualitative, aspirational, and it passes the test — if achieved, it objectively matters, because it is the difference between a demo and a product.\n\n**Step 2 — Write 3-5 outcome KRs.** The move is to measure completed value and cost, never activity or raw usage:\n\n- KR1 (task success): \"≥60% of assistant sessions end in a task the user accepts without editing or retrying\" — measured via accept/undo/retry telemetry, not a thumbs-up widget people ignore. This is the anti-vanity core: a session only counts if it produced accepted work.\n- KR2 (retention): \"Day-30 retention of assistant users rises from 22% to 38%\" — retention is Goodhart-resistant because a gamed, unhelpful feature does not bring people back.\n- KR3 (unit economics): \"Cost-per-resolved-task falls from ~$0.40 to ≤$0.18\" — an outcome KR that ties model/inference spend to delivered value, so \"more usage\" is only good if it is *cheaper* delivered value. (Approximate, illustrative figures.)\n- KR4 (trust/quality guardrail): \"Human-flagged incorrect answers stay ≤2% of sampled sessions\" — a counter-metric so the team cannot buy KR1–KR3 by shipping a confident-but-wrong assistant.\n\nNote what is absent: no \"messages sent,\" no \"features shipped,\" no \"% who tried it.\" Contrast with the skill's pattern table — \"Increase AI usage\" is the 2020s cousin of \"Launch new onboarding flow.\"\n\n**Step 3 — Calibrate ambition.** 22%→38% retention and 60% clean task-success on a new AI surface are genuine stretches (roughly 50–70% likelihood, not near-certain)."},{"path":"examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md","content":"# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999\n\n> *Example for the [okr-goal-setting](../SKILL.md) skill.*\n\nAndy Grove joined Intel in 1968 as its first employee after Bob Noyce and Gordon Moore (the founders). By 1971, as Intel was scaling rapidly into memory chips, Grove needed an operating system for the company. He took Peter Drucker's *Management by Objectives* (MBO) framework and modified it.\n\nGrove's specific modifications:\n\n1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.\n\n2. **Explicit Objective / Key Result split.** Drucker's MBO had goals; Grove insisted that what people *want* (the Objective) and *how they know* (the Key Result) be separated structurally, because conflating them produced unmeasurable goals.\n\n3. **Stretch as a structural feature.** Grove understood that 100% achievement was usually a signal of low ambition; he calibrated Intel's OKRs such that high performers hit 60-70%.\n\n4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.\n\nGrove documented the system in *High Output Management* (1983), where he wrote:\n\n> \"The two key phrases are objectives and key results, and they match the two purposes of the system. The objective is the direction: 'We want to dominate the mid-range microcomputer market.' The key results are some specific milestones that, taken together, indicate that we are progressing toward the objective. For each objective, several key results are typical.\"\n>\n> \"How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right.\"\n>\n> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.\n\nThe system became Intel's operating cadence through the 1970s-1990s. **John Doerr** joined Intel in 1974, learned OKRs as a young employee under Grove, and brought them to Kleiner Perkins when he became a partner in 1980. For the next 18 years, Doerr taught OKRs to portfolio companies — many of whom adopted them in varying forms.\n\nIn autumn 1999, Doerr was invited to present OKRs to Google's founders, Larry Page and Sergey Brin, and the 30-person startup. The story Doerr tells in *Measure What Matters*: he showed up at the Mountain View office, walked through the framework, and asked the founders if they'd adopt it. Both said yes. **Google has used OKRs continuously since 1999 — through their growth from 30 employees to >180,000, through IPO, through massive market dominance.**\n\nDoerr's later articulation of the framework's structure (Doerr, 2018):\n\n> \"An OBJECTIVE is simply WHAT is to be achieved, no more, no less. By definition, objectives are significant, concrete, action-oriented, and (ideally) inspirational. 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