| 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 |
| tags | ["estimation","project-management","pert","sprint-planning","ai-agents"] |
| tools | ["claude"] |
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
