Task Analyzer Skill for Claude

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 |