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OKRs (Objectives and Key Results)

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... 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-

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Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

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Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

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Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:okr-goal-setting

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SKILL.md

---
name: okr-goal-setting
description: "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.
  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"
---

# OKRs (Objectives and Key Results)

## Overview

OKRs 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).

Composes with `north-star-metric`, `first-principles`, `metacognition`, `mece`.

## When to Use

- Team goals are vague, unmeasurable, or just "complete project X" lists
- Teams hitting all goals but the business isn't moving (sandbagging signal)
- Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure
- Setting OKRs on AI features and the draft KR is "ship AI" / "increase AI usage" (vanity/Goodhart metric — replace with outcome KRs)

**Not when:** under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete case → run The Process directly.
- **Coach mode:** user unfamiliar or no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. 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.
2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.
3. Elicit the actual goal the team is trying to set.
> **[WAIT — do not advance until user responds]**
4. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?
> **[WAIT — do not advance until user responds]**
5. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.
> **[WAIT — do not advance until user responds]**

## The Process

**Step 1 — Write the Objective.** Qualitative, aspirational, time-bounded, one sentence. Test: "If achieved, would this objectively matter to the business?"

**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" =

_meta.json

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  "slug": "okr-goal-setting",
  "version": "1.0.5",
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references/sources.md

# Sources — okr-goal-setting

> *Primary sources for the [okr-goal-setting](../SKILL.md) skill.*

- 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.
- 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.
- Drucker, P. F. (1954). *The Practice of Management.* Harper & Row. The intellectual ancestor: Management by Objectives (MBO).
- 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.
- 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.
- "How Google sets goals: OKRs." Re:Work by Google. https://rework.withgoogle.com/guides/set-goals-with-okrs/ — Google's own guide.
- 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.
- 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.

examples/ai-native-org-okrs-2024-2026.md

# Method in Action: OKRs in an AI-Native Org (2024–2026)

> *Example for the [okr-goal-setting](../SKILL.md) skill.*

By 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.

The team's first-draft OKR was the common one:

- Objective: *"Make our product AI-first."*
- KR1: Ship the AI assistant to GA.
- KR2: Get 50% of users to try the assistant.
- KR3: Reach 1M AI messages/month.

Every 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.

**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.

**Step 2 — Write 3-5 outcome KRs.** The move is to measure completed value and cost, never activity or raw usage:

- 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.
- 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.
- 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.)
- 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.

Note 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."

**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).

examples/andy-grove-at-intel-1971-john-doerr-at-google-1999.md

# Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999

> *Example for the [okr-goal-setting](../SKILL.md) skill.*

Andy 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.

Grove's specific modifications:

1. **Quarterly, not annual cadence.** The semiconductor industry moved too fast for annual goal-setting. Quarterly was the unit at which Intel needed to align.

2. **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.

3. **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%.

4. **Transparency.** Every Intel employee could see every other employee's OKRs, including Grove's. This enforced alignment and exposed conflicts early.

Grove documented the system in *High Output Management* (1983), where he wrote:

> "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."
>
> "How do you know if you have set the goals correctly? If they have you in a sweat, they're probably about right."
>
> — Grove, A. S. (1983). *High Output Management.* Random House. ISBN 978-0679762881 (Vintage edition, 1995). pp. 110-112.

The 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.

In 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.**

Doerr's later articulation of the framework's structure (Doerr, 2018):

> "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 thin

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