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✳Claude
By Dave AIDave AI original
MIT · FreeView prompt
Estimate AI-assisted and hybrid human+agent development work with explicit PERT assumptions and calibration feedback loops
--- name: progressive-estimation description: "Estimate AI-assisted and hybrid human+agent development work with explicit PERT assumptions and calibration feedback loops" category: project-management risk: safe source: community date_added: "2026-03-10" author: Enreign tags: - estimation - project-management - pert - sprint-planning - ai-agents tools: - claude --- # Progressive Estimation Estimate AI-assisted and hybrid human+agent development work using three-point estimates, stated uncertainty assumptions and calibration feedback loops. ## Overview Progressive Estimation adapts to your team's working mode — human-only, hybrid, or agent-first — choosing explicit assumptions for each. Estimates remain uncertain and require local calibration. ## 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 locally measured multipliers when available, or clearly labeled scenarios 4. **PERT Calculation** — Produces expected values using three-point estimation 5. **Confidence Bands** — Generates percentiles only with a specified, calibrated forecast model 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." ## Estimation procedure Record scope, dependencies, acceptance criteria and whether durations mean effort or elapsed time. Elicit optimistic O, most likely M and pessimistic P in the same units, with O ≤ M ≤ P. A conventional PERT approximation is E = (O + 4M + P)/6 and SD ≈ (P − O)/6; these are modeling assumptions, not observed certainty. For O=2, M=4, P=12 hours, E=5 hours and SD≈1.67 hours. Record the assumptions behind each bound. Do not derive P50/P75/P90 directly from the mean or label a range a confidence interval without a stated distribution and method. For a project forecast include dependencies, correlated risks, review/queue time and limited human capacity. Use actual comparable completed tasks to calibrate AI multipliers; absent data, present explicit scenarios rather than claiming a research-backed speedup. Report actuals against the original scope and revise future forecasts. This guide does not bundle an estimation engine or calibration database. ## 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.--- name: progressive-estimation description: "Estimate AI-assisted and hybrid human+agent development work with explicit PERT assumptions and calibration feedback loops" category: project-management risk: safe source: community date_added: "2026-03-10" author: Enreign tags: - estimation - project-management - pert - sprint-planning - ai-agents tools: - claude --- # Progressive Estimation Estimate AI-assisted and hybrid human+agent development work using three-point estimates, stated uncertainty assumptions and calibration feedback loops. ## Overview Progressive Estimation adapts to your team's working mode — human-only, hybrid, or agent-first — choosing explicit assumptions for each. Estimates remain uncertain and require local calibration. ## 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 locally measured multipliers when available, or clearly labeled scenarios 4. **PERT Calculation** — Produces expected values using three-point estimation 5. **Confidence Bands** — Generates percentiles only with a specified, calibrated forecast model 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." ## Estimation procedure Record scope, dependencies, acceptance criteria and whether durations mean effort or elapsed time. Elicit optimistic O, most likely M and pessimistic P in the same units, with O ≤ M ≤ P. A conventional PERT approximation is E = (O + 4M + P)/6 and SD ≈ (P − O)/6; these are modeling assumptions, not observed certainty. For O=2, M=4, P=12 hours, E=5 hours and SD≈1.67 hours. Record the assumptions behind each bound. Do not derive P50/P75/P90 directly from the mean or label a range a confidence interval without a stated distribution and method. For a project forecast include dependencies, correlated risks, review/queue time and limited human capacity. Use actual comparable completed tasks to calibrate AI multipliers; absent data, present explicit scenarios rather than claiming a research-backed speedup. Report actuals against the original scope and revise future forecasts. This guide does not bundle an estimation engine or calibration database. ## 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.
Imported SKILL.md format for Claude Code, Codex and Cursor. Review the complete instructions and required tools before use. Package integrity checked; individual agent behavior has not been tested.