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Flags bottlenecks 4-8 weeks before they hit.\n\n## When to Use\n\n- Sprint planning feels like guesswork\n- You're not sure if you can take on a new client/project\n- Hiring decisions need data, not gut feel\n- Infrastructure keeps getting slammed at predictable times\n\n## How to Use\n\nTell the agent about your situation:\n\n```\n\"We have 8 engineers, 3 active projects, and a new client starting in March. Can we handle it?\"\n```\n\nThe agent will:\n1. **Audit current load** — Map people to commitments, calculate true utilization (not the number in your head)\n2. **Model scenarios** — What happens if the new project lands? What if two people quit? What if scope grows 30%?\n3. **Flag risks** — Identify single points of failure, overloaded roles, deadline clusters\n4. **Recommend actions** — Hire, redistribute, defer, or say no — with numbers behind each option\n\n## Capacity Framework\n\n### Utilization Bands\n| Band | Rate | Meaning |\n|------|------|---------|\n| 🟢 Green | <70% | Healthy buffer for unplanned work |\n| 🟡 Yellow | 70-85% | Sustainable but tight |\n| 🔴 Red | >85% | Burnout zone — something will slip |\n\n### Key Metrics\n- **Effective capacity** = headcount × available hours × efficiency factor (typically 0.7-0.8)\n- **Demand pipeline** = committed hours + probable hours (weighted by likelihood)\n- **Buffer ratio** = (capacity - demand) / capacity — target 15-25%\n- **Time to constraint** = weeks until demand exceeds capacity at current trajectory\n\n### Scenario Template\nFor each scenario, output:\n- Headcount needed vs. available\n- Skill gaps (specific roles/capabilities missing)\n- Timeline risk (which deadlines move)\n- Cost impact (overtime, contractors, lost revenue from saying no)\n- Recommended action with confidence level\n\n## Output Format\n\n```\nCAPACITY SNAPSHOT — [Date]\n━━━━━━━━━━━━━━━━━━━━━━━━━━\nTeam: [size] | Utilization: [%] | Buffer: [%]\nStatus: 🟢/🟡/🔴\n\nCURRENT COMMITMENTS\n- [Project A]: [X people, Y hours/week, end date]\n- [Project B]: ...\n\nPIPELINE (next 8 weeks)\n- [Incoming work]: probability %, estimated load\n- ...\n\nRISKS\n1. [Risk description + impact + timeframe]\n2. ...\n\nSCENARIOS\nA) [Scenario]: [outcome summary]\nB) [Scenario]: [outcome summary]\n\nRECOMMENDATION\n[Clear action with reasoning]\n```\n\n## Tips\n\n- Refresh capacity snapshots weekly during planning\n- Track actual vs. predicted utilization to calibrate your efficiency factor\n- Include non-project work (meetings, support, admin) — it's usually 20-30% of capacity\n- Don't plan above 80% utilization. The remaining 20% isn't slack, it's where real work happens.\n\n## Go Deeper\n\nYour capacity model is one piece of operational planning. For full business context packs covering finance, operations, and growth strategy across 10 industries:\n\n→ **[AfrexAI Context Packs](https://afrexai-cto.github.io/context-packs/)** — $47 each, built by operators who've done the work.\n\n→ **[AI Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/)** — Find out where your business is leaking money (free tool).\n\n→ **[Agent Setup Wizard](https://afrexai-cto.github.io/agent-setup/)** — Get your AI agent configured for your specific business in 5 minutes.\n","readmeExcerpt":"Capacity Planner Plan team and infrastructure capacity before it becomes a crisis. What It Does Takes your current workload data — team size, utilization rates, project pipeline, seasonal patterns — and builds a forward-looking capacity model. Flags bottlenecks 4-8 weeks before they hit. 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[Risk description + impact + timeframe]\n2. ...\n\nSCENARIOS\nA) [Scenario]: [outcome summary]\nB) [Scenario]: [outcome summary]\n\nRECOMMENDATION\n[Clear action with reasoning]"}],"parameters":{},"dependencies":[],"permissions":[],"extractedFiles":[],"languages":["typescript"],"docsSourceLabel":"CLAWHUB","editorialOverview":"Capacity Planner Capacity Planner Plan team and infrastructure capacity before it becomes a crisis. What It Does Takes your current workload data — team size, utilization rates, project pipeline, seasonal patterns — and builds a forward-looking capacity model. Flags bottlenecks 4-8 weeks before they hit. 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