progressive-estimation
Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops
Works with
--- name: progressive-estimation description: Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops license: MIT --- # Progressive Estimation Estimate AI-assisted and hybrid human+agent development work using research-backed formulas with PERT statistics, confidence bands, and calibration feedback loops. ## Overview Progressive Estimation adapts to your team's working mode — human-only, hybrid, or agent-first — applying the right velocity model and multipliers for each. It produces statistical estimates rather than gut feelings. ## When to Use This Skill - Estimating development tasks where AI agents handle part of the work - Sprint planning with hybrid human+agent teams - Batch sizing a backlog (handles 5 or 500 issues) - Staffing and capacity planning with agent multipliers - Release date forecasting with confidence intervals ## How It Works 1. **Mode Detection** — Determines if the team works human-only, hybrid, or agent-first 2. **Task Classification** — Categorizes by size (XS–XL), complexity, and risk 3. **Formula Application** — Applies research-backed multipliers grounded in empirical studies 4. **PERT Calculation** — Produces expected values using three-point estimation 5. **Confidence Bands** — Generates P50, P75, P90 intervals 6. **Output Formatting** — Formats for Linear, JIRA, ClickUp, GitHub Issues, Monday, or GitLab 7. **Calibration** — Feeds back actuals to improve future estimates ## Examples **Single task:** > "Estimate building a REST API with authentication using Claude Code" **Batch mode:** > "Estimate these 12 JIRA tickets for our next sprint" **With context:** > "We have 3 developers using AI agents for ~60% of implementation. Estimate this feature." ## Best Practices - Start with a single task to calibrate before moving to batch mode - Feed back actual completion times to improve the calibration system - Use "instant mode" for quick T-shirt sizing without full PERT analysis - Be explicit about team composition and agent usage percentage ## Common Pitfalls - **Problem:** Overconfident estimates **Solution:** Use P75 or P90 for commitments, not P50 - **Problem:** Missing context **Solution:** The skill asks clarifying questions — provide team size and agent usage - **Problem:** Stale calibration **Solution:** Re-calibrate when team composition or tooling changes significantly ## Related Skills - `@sprint-planning` - Sprint planning and backlog management - `@project-management` - General project management workflows - `@capacity-planning` - Team velocity and capacity planning ## Additional Resources - [Source Repository](https://github.com/Enreign/progressive-estimation) - [Installation Guide](https://github.com/Enreign/progressive-estimation/blob/main/INSTALLATION.md) - [Research References](https://github.com/Enreign/progressive-estimation/tree/main/references) ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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