Progressive Estimation for AI Teams is a operations claude skill built by sickn33. Best for: Engineering managers and tech leads estimate sprint capacity and release dates for teams using AI agents, receiving P50/P75/P90 confidence intervals instead of guesses..

What it does
Estimate hybrid human+agent development work using PERT statistics, confidence bands, and calibration feedback loops.
Category
operations
Created by
sickn33
Last updated
Claude Skilloperations GitHub-backed CuratedintermediateClaude Code

Progressive Estimation for AI Teams

Estimate hybrid human+agent development work using PERT statistics, confidence bands, and calibration feedback loops.

Skill instructions


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

  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

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.

Use this skill

Most skills are portable instruction packages. Claude Code supports SKILL.md directly. Other agents can use adapted files like AGENTS.md, .cursorrules, and GEMINI.md.

Claude Code

Save SKILL.md into your Claude Skills folder, then restart Claude Code.

mkdir -p ~/.claude/skills/progressive-estimation-for-ai-teams && curl -L "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/HEAD/skills/progressive-estimation/SKILL.md" -o ~/.claude/skills/progressive-estimation-for-ai-teams/SKILL.md

Installs to ~/.claude/skills/progressive-estimation-for-ai-teams/SKILL.md.

Use cases

Engineering managers and tech leads estimate sprint capacity and release dates for teams using AI agents, receiving P50/P75/P90 confidence intervals instead of guesses.

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Stats

Installs0
GitHub Stars35.4k
Forks5820
LicenseMIT License
UpdatedMar 25, 2026