FrancoStino/progressive-estimation
bundled-skills/progressive-estimation/SKILL.md
Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops
$ install --global
skillsauthnpx skillsauth add FrancoStino/opencode-skills-antigravity progressive-estimationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
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SKILL.md
- name:
- progressive-estimation
- description:
- Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops
- category:
- project-management
- risk:
- safe
- source:
- community
- date_added:
- 2026-03-10
- author:
- Enreign
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
- 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
-
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
- Installation Guide
- Research 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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