.claude/skills/plan-quality-verifier/SKILL.md
Verifies implementation plan quality across 8 dimensions (requirement-coverage, task-completeness, dependency-validity, scope-sanity, artifact-wiring, risk-assessment, testability, estimation-quality) and returns pass/fail with per-dimension scores.
npx skillsauth add oimiragieo/agent-studio plan-quality-verifierInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Score an implementation plan across 8 quality dimensions and gate execution on a minimum threshold.
Returns { pass, score, dimensions } where pass is true when overall score ≥ 60/100.
Invoke this skill before handing off a plan to executing agents:
Skill({ skill: 'plan-quality-verifier' });
Use when:
NO PLAN EXECUTES WITHOUT PASSING QUALITY VERIFICATION FIRST
A plan that scores < 60/100 must be revised before spawning executing agents.
Read the plan file:
cat .claude/context/plans/<plan-file>.md
Expected output: Raw markdown text of the plan.
const { verifyPlan } = require('.claude/lib/validation/plan-quality-verifier.cjs');
const planContent = require('fs').readFileSync('<plan-path>', 'utf8');
const result = verifyPlan(planContent);
console.log(JSON.stringify(result, null, 2));
Expected output:
{
"pass": true,
"score": 72,
"dimensions": [
{ "name": "requirement-coverage", "score": 8 },
{ "name": "task-completeness", "score": 7 },
{ "name": "dependency-validity", "score": 9 },
{ "name": "scope-sanity", "score": 6 },
{ "name": "artifact-wiring", "score": 7 },
{ "name": "risk-assessment", "score": 8 },
{ "name": "testability", "score": 7 },
{ "name": "estimation-quality", "score": 9 }
]
}
Verify: result.pass === true and result.score >= 60.
| Result | Action |
| ------------- | ----------------------------------------------------------------------------- |
| pass: true | Proceed — hand off plan to executing agents |
| pass: false | Block — return feedback to planner with failing dimensions |
| Score 60-69 | Marginal pass — flag low-scoring dimensions for improvement in next iteration |
| Score < 40 | Hard block — plan needs significant rework before re-evaluation |
When pass: false, format feedback:
Plan quality check FAILED (score: {{score}}/100)
Failing dimensions (score < 6):
{{#each dimensions where score < 6}}
- {{name}}: {{score}}/10 — needs improvement
{{/each}}
Action required: Revise the plan and re-run verification.
| Dimension | What it Measures | Good Signals | | -------------------- | ------------------------------------------------ | ------------------------------------------------- | | requirement-coverage | Are all stated requirements addressed by tasks? | Keywords: requirement, feature, user story | | task-completeness | Do tasks have clear ownership and deliverables? | Keywords: implement, create, update, agent | | dependency-validity | Are task dependencies explicit and cycle-free? | Keywords: depends, blocked by, after, before | | scope-sanity | Is the plan scope realistic and bounded? | Phase/wave structure, bounded scope language | | artifact-wiring | Are output artifacts explicitly listed per task? | Keywords: artifact, output, file, deliverable | | risk-assessment | Are risks identified with mitigations? | Keywords: risk, mitigation, fallback, contingency | | testability | Are acceptance criteria and test hooks defined? | Keywords: test, verify, acceptance, criteria | | estimation-quality | Are effort/time estimates provided? | Keywords: estimate, hours, days, effort, wave |
Input validated against schemas/input.schema.json before execution.
Output contract defined in schemas/output.schema.json.
Before starting:
Read .claude/context/memory/learnings.md for past plan quality issues and common failures.
After completing:
.claude/context/memory/issues.md.claude/context/memory/learnings.mdASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
plan-generator — Generates plans that should pass this verifierverification-before-completion — Gates task completion (complements plan gating)tdd — TDD methodology that complements testability dimension scoring.claude/lib/validation/plan-quality-verifier.cjstests/lib/validation/plan-quality-verifier.test.cjs.claude/skills/plan-quality-verifier/schemas/input.schema.jsontools
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
tools
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
data-ai
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
development
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.