task-analyzer
GitHub执行元认知任务分析与技能选择，通过解析任务本质、评估规模及类型，结合标签匹配与隐含依赖，精准推荐所需技能组合，以优化工作流效率。
Trigger Scenarios
Install
npx skills add shinpr/claude-code-workflows --skill task-analyzer -g -y
SKILL.md
Frontmatter
{
"name": "task-analyzer",
"description": "Performs metacognitive task analysis and skill selection. Use when determining task complexity, selecting appropriate skills, or estimating work scale."
}
Task Analyzer
Provides metacognitive task analysis and skill selection guidance.
Skills Index
See skills-index.yaml for available skills metadata.
Task Analysis Process
1. Understand Task Essence
Identify the fundamental purpose beyond surface-level work:
| Surface Work | Fundamental Purpose |
|---|---|
| "Fix this bug" | Problem solving, root cause analysis |
| "Implement this feature" | Feature addition, value delivery |
| "Refactor this code" | Quality improvement, maintainability |
| "Update this file" | Change management, consistency |
Action: Map the user request to one row in the Surface Work → Fundamental Purpose table above. If no row matches, state the fundamental purpose explicitly before proceeding.
2. Estimate Task Scale
| Scale | File Count | Indicators |
|---|---|---|
| Small | 1-2 | Single function/component change |
| Medium | 3-5 | Multiple related components |
| Large | 6+ | Cross-cutting concerns, architecture impact |
Scale affects skill priority:
- Scale >= Large → include documentation-criteria with priority high, preserving the repository's 6+ file planning contract
- Scale >= Large and the task changes executable system behavior or requires dependency/phase strategy → also include implementation-approach with priority high
- Scale >= Large but the task is research, documentation-only, or mechanical → include implementation-approach only when an implementation strategy decision is actually required
- Scale = Small → select task-type essential skills only and target a maximum of 3; exceed 3 when another skill directly governs a named risk, language, or output contract
3. Identify Task Type
| Type | Characteristics | Key Skills |
|---|---|---|
| Implementation | New code, features | coding-principles, testing-principles |
| Fix | Bug resolution | ai-development-guide, testing-principles |
| Refactoring | Structure improvement | coding-principles, ai-development-guide |
| Design | Architecture decisions | documentation-criteria, implementation-approach |
| Quality | Testing, review | testing-principles, integration-e2e-testing |
4. Tag-Based Skill Matching
Extract relevant tags from task description and match against skills-index.yaml:
Task: "Implement user authentication with tests"
Extracted tags: [implementation, testing, security]
Matched skills:
- coding-principles (implementation, security)
- testing-principles (testing)
- ai-development-guide (implementation)
5. Implicit Relationships
Consider hidden dependencies:
| Task Involves | Also Include |
|---|---|
| Error handling | debugging, testing |
| New features | design, implementation, documentation |
| Performance | profiling, optimization, testing |
| Frontend | typescript-rules, test-implement |
| API/Integration | integration-e2e-testing |
Output Format
Return structured analysis with skill metadata from skills-index.yaml:
taskAnalysis:
essence: <string> # Fundamental purpose identified
type: <implementation|fix|refactoring|design|quality>
scale: <small|medium|large>
estimatedFiles: <number>
tags: [<string>, ...] # Extracted from task description
selectedSkills:
- skill: <skill-name> # From skills-index.yaml
priority: <high|medium|low>
reason: <string> # Why this skill was selected
# Pass through metadata from skills-index.yaml
tags: [...]
typical-use: <string>
size: <small|medium|large>
sections: [...] # All sections from yaml, unfiltered
Note: Section selection (choosing which sections are relevant) is done after reading the actual SKILL.md files.
Consumer mapping: rule-advisor consumes this intermediate shape and owns the final schema transformation: type → taskType (quality → quality-improvement; other values unchanged), tags → extractedTags, and selectedSkills → selectedRules after it reads and extracts the selected skill sections. Preserve metaCognitiveQuestions as a separate final field generated from the question design below.
Skill Selection Priority
- Essential - Directly related to task type
- Quality - Testing and quality assurance
- Process - Workflow and documentation
- Supplementary - Reference and best practices
Metacognitive Question Design
Generate up to 5 questions whose answers can change skill selection, verification, or escalation. Return no questions when none would change those decisions.
| Task Type | Question Focus |
|---|---|
| Implementation | Design validity, edge cases, performance |
| Fix | Root cause (5 Whys), impact scope, regression testing |
| Refactoring | Current problems, target state, phased plan |
| Design | Requirement clarity, future extensibility, trade-offs |
Warning Patterns
Detect and flag these patterns:
| Pattern | Warning | Mitigation |
|---|---|---|
| Large change detected | Pair with implementation-approach | Split into phases per strategy |
| Implementation task detected | Pair with testing-principles | Apply TDD from start |
| Error fix requested | Pair with ai-development-guide | Apply 5 Whys before fixing |
| Multi-file task without plan | Pair with documentation-criteria | Create work plan first |
Version History
-
56ab6c1
Current 2026-07-19 22:47
重构提示词执行指导，细化大型任务的技能优先级策略，增加对研究或文档类任务的实现路径判断逻辑。
- 66e3b29 2026-07-05 12:02
