progressive-estimation

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

31,392 stars

Best use case

progressive-estimation is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

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

Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "progressive-estimation" skill to help with this workflow task. Context: Estimate AI-assisted and hybrid human+agent development work with research-backed PERT statistics and calibration feedback loops

Example output

A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

  • Use this skill when you want a reusable workflow rather than writing the same prompt again and again.

When not to use this skill

  • Do not use this when you only need a one-off answer and do not need a reusable workflow.
  • Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/progressive-estimation/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/progressive-estimation/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/progressive-estimation/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How progressive-estimation Compares

Feature / Agentprogressive-estimationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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

1. **Mode Detection** — Determines if the team works human-only, hybrid, or agent-first
2. **Task Classification** — Categorizes by size (XS–XL), complexity, and risk
3. **Formula Application** — Applies research-backed multipliers grounded in empirical studies
4. **PERT Calculation** — Produces expected values using three-point estimation
5. **Confidence Bands** — Generates P50, P75, P90 intervals
6. **Output Formatting** — Formats for Linear, JIRA, ClickUp, GitHub Issues, Monday, or GitLab
7. **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](https://github.com/Enreign/progressive-estimation)
- [Installation Guide](https://github.com/Enreign/progressive-estimation/blob/main/INSTALLATION.md)
- [Research References](https://github.com/Enreign/progressive-estimation/tree/main/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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