task-analyzer
The task-analyzer skill provides metacognitive guidance for breaking down work requests and selecting appropriate Claude Code skills. Use it when facing ambiguous assignments, determining whether a task is small or large-scale, identifying whether work requires implementation versus refactoring versus design, and matching task characteristics to available skills through tag extraction and implicit dependency analysis.
git clone --depth 1 https://github.com/shinpr/claude-code-workflows /tmp/task-analyzer && cp -r /tmp/task-analyzer/dev-workflows-frontend/skills/task-analyzer ~/.claude/skills/task-analyzerSKILL.md
# Task Analyzer
Use [skills-index.yaml](references/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 evidence | Consider |
|---|---|
| Observed defect or failure | `ai-development-guide`, `testing-principles` |
| Code implementation or refactoring | `coding-principles`, `testing-principles` |
| Requested design artifact | `documentation-criteria` |
| Multiple credible implementation strategies requiring cost comparison | `implementation-approach` |
| Observable cross-boundary behavior that cannot be proven more cheaply | `integration-e2e-testing` |
| React or TypeScript frontend code | `typescript-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
```yaml
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.Generates integration/E2E test skeletons from Design Doc ACs using ROI-based selection and journey-based E2E reservation. Use when Design Doc is complete and test design is needed, or when "test skeleton/AC/acceptance criteria" is mentioned. Behavior-first approach for minimal tests with maximum coverage.
Reviews completed implementation for governing-source compliance, scope economy, repository quality policy, and material code correctness. Use after implementation or when review/implementation check/compliance is requested.
Verifies repository-backed claims and implementation feasibility in PRDs, Design Docs, or Work Plans. Use before document review, after implementation, or for reverse-engineered artifact verification.
Collects compact repository evidence for scope confirmation, technical option selection, complete design, and verification. Use before Design Doc creation when repository facts can change scope, reuse, contracts, cost, or proof.
Detects conflicts across multiple Design Docs and provides structured reports. Use when multiple Design Docs exist, or when "consistency/conflict/sync/between documents" is mentioned. Focuses on detection and reporting only, no modifications.
Reviews one document or one ADR batch against governing requirements, repository evidence, and the needs of its next consumer. Use before user approval or when document consistency and completeness need verification.
Reviews changed integration and E2E tests against skeletons, proof obligations, or explicit prompt claims. Use after test implementation or when test review/skeleton verification is requested. Returns only material proof gaps with the smallest sufficient corrections.
Comprehensively collects problem-related information and creates evidence matrix. Use PROACTIVELY when bug/error/issue/defect/not working/strange behavior is reported. Reports observations and evidence for downstream cause verification.