estimate-calibrator

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Produces calibrated three-point PERT estimates (best/likely/worst) with confidence intervals, unknowns, and assumptions. Triggers on: "estimate this", "how long will this take", "effort estimate", "confidence interval", "story points", "t-shirt sizing". NOT for task decomposition, use task-decomposer.

AI & Automation 313 stars 46 forks Updated 4 days ago MIT

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Quality Score: 93/100

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Description 5%
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Skill Content

# Estimate Calibrator Replaces single-point guesses with structured three-point estimates: decomposes work into atomic units, estimates best/likely/worst case for each, identifies unknowns and assumptions, calculates aggregate ranges using PERT, and assigns confidence levels with explicit rationale. ## Reference Files | File | Contents | Load When | | ---------------------------------- | ------------------------------------------------------------------------- | ---------------------- | | `references/estimation-methods.md` | PERT formula, three-point estimation, Monte Carlo basics | Always | | `references/unknown-categories.md` | Technical, scope, external, and organizational uncertainty types | Unknown identification | | `references/calibration-tips.md` | Cognitive biases in estimation, historical calibration, buffer strategies | Always | | `references/sizing-heuristics.md` | Common task size patterns, complexity indicators, reference class data | Quick sizing needed | ## Prerequisites - Work item description (feature, task, project) - Decomposed tasks (or use task-decomposer skill first) - Context: team familiarity, tech stack, existing codebase ## Workflow ### Phase 1: Decompose Work If the work item is not already decomposed into atomic units: 1. **Break into tasks** — Each task should be es...

Details

Author
Mathews-Tom
Repository
Mathews-Tom/armory
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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