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Progressive estimation

Skill newmindsgroup/ai-agent-skills-library/dist/skills/progressive-estimation

Shared library of AI agent skills — works across Claude Code, Cursor, Codex, Windsurf, OpenCode, and Google Antigravity via a single universal installer.

Install

npx -y skills add newmindsgroup/ai-agent-skills-library --skill progressive-estimation

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Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

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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

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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