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SebastianLuser/claude-code-arcane32026年10月27日 更新
免费在 GitHub 公开
estimate
Estimates task effort by analyzing complexity, dependencies, historical velocity, and risk factors. Produces a structured estimate with confidence levels.
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SebastianLuser/claude-code-arcane星标 3(GitHub 3 + cccskills 0)
实测评分 未实测
查看安装方法包含的文件（3）
- SKILL.md1.8 KB
- references/complexity-factors.md992 B
- references/estimate-template.md1.6 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
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
→ Read references/complexity-factors.md for detailed code complexity, scope, risk factors, and estimation guidelines
Phase 4: Generate the Estimate
→ Read references/estimate-template.md for the full estimate output template
Output the estimate with a brief summary: recommended budget, confidence level, and the single biggest risk factor.
This skill is read-only — no files are written. Verdict: COMPLETE — estimate generated.
Phase 5: Next Steps
- If confidence is Low: recommend a time-boxed spike (
/prototype) before committing. - If the task is > 10 days: recommend breaking it into smaller stories via
/create-stories. - To schedule the task: run
/sprint-plan updateto add it to the next sprint.
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