progressive-estimation
Estimates development work by applying PERT (Program Evaluation and Review Technique) statistics to produce optimistic, pessimistic, and most-likely timelines, then calibrates future estimates based on historical accuracy feedback.
How to Install
git clone --depth 1 https://github.com/sickn33/antigravity-awesome-skills.git && cp antigravity-awesome-skills/skills/progressive-estimation ~/.claude/skills/progressive-estimation -rProgressive 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
- 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 research-backed multipliers grounded in empirical studies
- PERT Calculation — Produces expected values using three-point estimation
- Confidence Bands — Generates P50, P75, P90 intervals
- 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."
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.
Details
| Category | AI/ML → ml |
| Source | sickn33/antigravity-awesome-skills |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 41.5K |
| Est. per Skill | 47 (shared across 868 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | Safe |
Related Skills
Works Well With
Skills from the same repository — often designed to work together