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progressive-estimation

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

.agents/skills/progressive-estimation TypeScript
TY
MA
2+ layers Tracked stack
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Phases
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References
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Scripts
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Architectural Overview

Skill Reading

"This module is grounded in ai engineering patterns and exposes 1 core capabilities across 1 execution phases."

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

Primary Stack

TypeScript

Tooling Surface

Guide only

Workspace Path

.agents/skills/progressive-estimation

Operational Ecosystem

The complete hardware and software toolchain required.

This skill is mostly documentation-driven and does not expose extra scripts, references, examples, or templates.

Module Topology

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Antigravity Core

Antigravity Core

Principal Engineering Agent

A high-performance agentic architecture developed by Deepmind for autonomous coding tasks.
120 Installs
4.2 Reliability
1 Workspace Files
4.2
Workspace Reliability Avg
5
68%
4
22%
3
10%
2
0%
1
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No explicit validation signals were parsed for this skill yet, but the module remains available for inspection and chat launch.

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