Task Analyzer

SkillProductivity

Selects the smallest set of task-execution skills and metacognitive safeguards for standalone task and diagnosis workflows.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Task Analyzer skill

What this skill tells your AI

The instructions your AI receives, as published by shinpr/claude-code-workflows in skills/task-analyzer/SKILL.md and read by ahel’s review.

Use skills-index.yaml as the available skill catalog. Documentation routing and workflow Structural Scale belong to documentation-criteria, not this skill.

Process

1. Identify Task Essence

State the observable purpose beyond the surface operation. Preserve an explicitly invoked recipe or governing artifact as the entry point.

2. Match Skills to Task Evidence

Extract task-evidence tags and match them to the catalog. Add a skill only when its rules change the requested action, verification, or handling of a concrete risk.

Task evidenceConsider
Observed defect or failureai-development-guide, testing-principles
Code implementation or refactoringcoding-principles, testing-principles
Requested design artifactdocumentation-criteria
Multiple credible implementation strategies requiring cost comparisonimplementation-approach
Observable cross-boundary behavior that cannot be proven more cheaplyintegration-e2e-testing
React or TypeScript frontend codetypescript-rules and applicable frontend testing rules

Select in this order:

  1. governing: defines the requested output or selected workflow.
  2. risk-control: changes proof or handling of an activated failure mode.
  3. supplementary: resolves a concrete remaining risk.

3. Generate Execution Guidance

Generate only warnings and questions that can change skill selection, verification, escalation, or the first action. Prefer the smallest evidence-gathering action that can establish the target or cause.

Task analysis does not own Structural Scale, file-count estimation, documentation requirements, approval gates, implementation phases, or subagent topology.

Output

taskAnalysis:
  essence: <fundamental purpose>
  extractedTags: [<task evidence tag>]
selectedRules:
  - skill: <skill name from skills-index.yaml>
    priority: <governing|risk-control|supplementary>
    reason: <how it changes execution or verification>
    sections: [<relevant section name>]
metaCognitiveGuidance:
  taskEssence: <fundamental purpose>
  pastFailures: [<applicable known failure pattern>]
  potentialPitfalls: [<task-specific risk>]
  firstStep:
    action: <smallest evidence-gathering or execution action>
    rationale: <why it comes first>
metaCognitiveQuestions: [<question that can change the approach>]
warningPatterns:
  - pattern: <applicable warning>
    mitigation: <proportionate response>

Return skill names and relevant section names. The consumer loads the named skills; filesystem paths, catalog metadata, and skill bodies remain at their source.

Completion Check

  • Task essence, tags, and first action are tied to the current request.
  • Every selected skill changes execution, verification, or a concrete risk response.
  • The selected set is the smallest sufficient set.
  • Questions and warnings are task-specific and proportionate.
  • Structural Scale and workflow routing remain with their owning process.

Signals

GitHub stars
681
Forks
102
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
task-analyzer-shinpr
Source
github.com/shinpr/claude-code-workflows