estimate
FeaturedEstimates 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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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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