Agent Skills: Task Analyzer

Performs metacognitive task analysis and skill selection. Use when determining task complexity, selecting appropriate skills, or estimating work scale. Returns skills with confidence scores and metadata.

UncategorizedID: shinpr/claude-code-workflows/task-analyzer

Install this agent skill to your local

pnpm dlx add-skill https://github.com/shinpr/claude-code-workflows/tree/HEAD/skills/task-analyzer

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skills/task-analyzer/SKILL.md

Skill Metadata

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 and implementation-approach in selectedSkills with priority high
  • Scale = Small → limit selectedSkills to task-type essential skills only (max 3)

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.

Skill Selection Priority

  1. Essential - Directly related to task type
  2. Quality - Testing and quality assurance
  3. Process - Workflow and documentation
  4. Supplementary - Reference and best practices

Metacognitive Question Design

Generate 3-5 questions according to task nature:

| 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 |