Imported from sickn33/agentic-awesome-skills (
skills/progressive-estimation/SKILL.md). Install upstream withnpx skills add sickn33/agentic-awesome-skills --skill progressive-estimation. Copyright stays with the author.
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
- Mode Detection — Determines if the team works human-only, hybrid, or agent-first
- Task Classification — Categorizes by size (XS–XL), complexity, and risk
- Formula Application — Applies locally measured multipliers when available, or clearly labeled scenarios
- PERT Calculation — Produces expected values using three-point estimation
- Confidence Bands — Generates percentiles only with a specified, calibrated forecast model
- Output Formatting — Formats for Linear, JIRA, ClickUp, GitHub Issues, Monday, or GitLab
- 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
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Problem: Overconfident estimates Solution: Use P75 or P90 for commitments, not P50
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Problem: Missing context Solution: The skill asks clarifying questions — provide team size and agent usage
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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
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.
