estimate

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Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels.

AI & Automation 21,463 stars 3118 forks Updated 3 weeks ago MIT

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Quality Score: 97/100

Stars 20%
100
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

## Phase 1: Understand the Task Read the task description from the argument. If the description is too vague to estimate meaningfully, ask for clarification before proceeding. Read CLAUDE.md for project context: tech stack, coding standards, architectural patterns, and any estimation guidelines. Read relevant design documents from `design/gdd/` if the task relates to a documented feature or system. --- ## Phase 2: Scan Affected Code Identify files and modules that would need to change: - Assess complexity (size, dependency count, cyclomatic complexity) - Identify integration points with other systems - Check for existing test coverage in the affected areas - Read past sprint data from `production/sprints/` for similar completed tasks and historical velocity --- ## Phase 3: Analyze Complexity Factors **Code Complexity:** - Lines of code in affected files - Number of dependencies and coupling level - Whether this touches core/engine code vs leaf/feature code - Whether existing patterns can be followed or new patterns are needed **Scope:** - Number of systems touched - New code vs modification of existing code - Amount of new test coverage required - Data migration or configuration changes needed **Risk:** - New technology or unfamiliar libraries - Unclear or ambiguous requirements - Dependencies on unfinished work - Cross-system integration complexity - Performance sensitivity --- ## Phase 4: Generate the Estimate ```markdown ## Task Estimate: [Task Name] Generat...

Details

Author
Donchitos
Repository
Donchitos/Claude-Code-Game-Studios
Created
4 months ago
Last Updated
3 weeks ago
Language
Shell
License
MIT

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