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:

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 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

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:

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

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 up to 5 questions whose answers can change skill selection, verification, or escalation. Return no questions when none would change those decisions.

Warning Patterns

Detect and flag these patterns: