Agent skill

Qe Quality Assessment

by proffesor-for-testing in proffesor-for-testing/agentic-qe

Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics.

MITAuto-check passedDevelopment

Install Qe Quality Assessment

skills CLI
$ npx skills add proffesor-for-testing/agentic-qe --skill qe-quality-assessment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install proffesor-for-testing/agentic-qe qe-quality-assessment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/assets/skills/qe-quality-assessment .claude/skills/qe-quality-assessment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qe-quality-assessment
GitHub stars
495
Token cost
~1.9k tokens
SKILL.md length
290 words
Files
5 (incl. scripts)
Skills in repo
93
Repo updated
First seen
Licence
MIT

At a glance

Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics.

  • Works in 3 steps: Code Quality Metrics → Quality Gates → Deployment Readiness
  • Assessing deployment readiness
  • SKILL.md covers Purpose, Activation, Quick Start and Agent Workflow, plus 8 more sections
  • Calls node

What it does

Qe Quality Assessment is an agent skill from proffesor-for-testing/agentic-qe. Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts (for example `evals/qe-quality-assessment.yaml`, `run-history.json` and `schemas/output.json`).

It sits in Development, covering Quality gates, Code quality and Refactoring. The repository describes itself as: Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support… The licence is MIT.

When your agent uses it

  • Assessing deployment readiness
  • Configuring quality gates
  • Scoring a codebase for release
  • Generating quality reports with pass/fail verdicts

Example prompts

  • “Use the qe-quality-assessment skill to evaluate code quality through complexity analysis, lint results, code smell detection, and test health metrics”
  • “/qe-quality-assessment”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Code Quality Metrics
  2. Quality Gates
  3. Deployment Readiness

What it can do on your machine

Read from SKILL.md and the folder at commit 1363bc7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    Shell commands in SKILL.md call:

    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qe Quality Assessment loads about 1.9k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 290 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from proffesor-for-testing/agentic-qe at commit 1363bc7, republished under its MIT licence (© proffesor-for-testing). 290 words, ~1,911 tokens.

Download SKILL.mdSave it as .claude/skills/qe-quality-assessment/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
qe-quality-assessment
description
Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Use when assessing deployment readiness, configuring quality gates, scoring a codebase for release, or generating quality reports with pass/fail verdicts.
trust_tier
3
validation.schema_path
schemas/output.json
validation.validator_path
scripts/validate-config.json
validation.eval_path
evals/qe-quality-assessment.yaml

QE Quality Assessment

Purpose

Guide the use of v3's quality assessment capabilities including automated quality gates, metrics aggregation, trend analysis, and deployment readiness evaluation.

Activation

  • When evaluating code quality
  • When setting up quality gates
  • When assessing deployment readiness
  • When tracking quality metrics
  • When generating quality reports

Quick Start

bash
# Run quality assessment
aqe quality assess --scope src/ --gates all

# Check deployment readiness
aqe quality deploy-ready --environment production

# Generate quality report
aqe quality report --format dashboard --period 30d

# Compare quality between releases
aqe quality compare --from v1.0 --to v2.0

Agent Workflow

typescript
// Comprehensive quality assessment
Task("Assess code quality", `
  Evaluate quality for src/:
  - Code complexity (cyclomatic, cognitive)
  - Test coverage and mutation score
  - Security vulnerabilities
  - Code smells and technical debt
  - Documentation coverage
  Generate quality score and recommendations.
`, "qe-quality-analyzer")

// Deployment readiness check
Task("Check deployment readiness", `
  Evaluate if release v2.1.0 is ready for production:
  - All tests passing
  - Coverage thresholds met
  - No critical vulnerabilities
  - Performance benchmarks passed
  - Documentation updated
  Provide go/no-go recommendation.
`, "qe-deployment-advisor")

Quality Dimensions

1. Code Quality Metrics
typescript
await qualityAnalyzer.assessCode({
  scope: 'src/**/*.ts',
  metrics: {
    complexity: {
      cyclomatic: { max: 15, warn: 10 },
      cognitive: { max: 20, warn: 15 }
    },
    maintainability: {
      index: { min: 65 },
      duplication: { max: 3 }  // percent
    },
    documentation: {
      publicAPIs: { min: 80 },
      complexity: { min: 70 }
    }
  }
});
2. Quality Gates
typescript
await qualityGate.evaluate({
  gates: {
    coverage: { min: 80, blocking: true },
    // Fault detection — coverage is necessary but NOT sufficient (ADR-113).
    // A suite can hit 90% coverage and catch no bugs; mutation score does not lie.
    mutationScore: { min: 0.6, blocking: false },   // warn-by-default; opt-in blocking
    regenerability: { min: 0.5, blocking: false },  // Deletion Test: durable oracle backing per module
    complexity: { max: 15, blocking: false },
    vulnerabilities: { critical: 0, high: 0, blocking: true },
    duplications: { max: 3, blocking: false },
    techDebt: { maxRatio: 5, blocking: false }
  },
  action: {
    onPass: 'proceed',
    onFail: 'block-merge',
    onWarn: 'notify'
  }
});
2a. Regenerability gate (ADR-113)

Coverage measures lines executed; mutation score measures whether the tests would notice a bug, and regenerability answers the Deletion Test — "if this module were deleted and regenerated, would a wrong rebuild be caught?" Regenerability = mutation score discounted by the durability tier backing the module (durable > live > ephemeral

none); ephemeral-only tests score low because they don't survive a reimplementation.

typescript
import { evaluateRegenerabilityGate } from '../../../src/feedback/regenerability-gate.js';

const verdict = evaluateRegenerabilityGate(moduleProfiles, {
  mutationScoreMin: 0.6,
  regenerabilityMin: 0.5,
  mode: 'warn',   // 'block' to fail CI; warn-by-default so adoption never breaks pipelines
});
// verdict.passed (thresholds met) · verdict.blocking (should fail CI) · verdict.failures[]

Surface it next to coverage in reports: Coverage 88% ✅ / Mutation 41% ⚠️ / Regenerability: ephemeral-only ⚠️.

3. Deployment Readiness
typescript
await deploymentAdvisor.assess({
  release: 'v2.1.0',
  criteria: {
    testing: {
      unitTests: 'all-pass',
      integrationTests: 'all-pass',
      e2eTests: 'critical-pass',
      performanceTests: 'baseline-met'
    },
    quality: {
      coverage: 80,
      noNewVulnerabilities: true,
      noRegressions: true
    },
    documentation: {
      changelog: true,
      apiDocs: true,
      releaseNotes: true
    }
  }
});

Quality Score Calculation

yaml
quality_score:
  components:
    test_coverage:
      weight: 0.25
      metrics: [statement, branch, function]

    code_quality:
      weight: 0.20
      metrics: [complexity, maintainability, duplication]

    security:
      weight: 0.25
      metrics: [vulnerabilities, dependencies]

    reliability:
      weight: 0.20
      metrics: [bug_density, flaky_tests, error_rate]

    documentation:
      weight: 0.10
      metrics: [api_coverage, readme, changelog]

  scoring:
    A: 90-100
    B: 80-89
    C: 70-79
    D: 60-69
    F: 0-59

Quality Dashboard

typescript
interface QualityDashboard {
  overallScore: number;  // 0-100
  grade: 'A' | 'B' | 'C' | 'D' | 'F';
  dimensions: {
    name: string;
    score: number;
    trend: 'improving' | 'stable' | 'declining';
    issues: Issue[];
  }[];
  gates: {
    name: string;
    status: 'pass' | 'fail' | 'warn';
    value: number;
    threshold: number;
  }[];
  trends: {
    period: string;
    scores: number[];
    alerts: Alert[];
  };
  recommendations: Recommendation[];
}

CI/CD Integration

yaml
# Quality gate in pipeline
quality_check:
  stage: verify
  script:
    - aqe quality assess --gates all --output report.json
  rules:
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
  artifacts:
    reports:
      quality: report.json
  allow_failure:
    exit_codes:
      - 1  # Warnings only

Run History

After each quality assessment, append results to run-history.json in this skill directory:

bash
node -e "
const fs = require('fs');
const h = JSON.parse(fs.readFileSync('.claude/skills/qe-quality-assessment/run-history.json'));
h.runs.push({date: new Date().toISOString().split('T')[0], gate_result: 'PASS_OR_FAIL', failed_checks: []});
fs.writeFileSync('.claude/skills/qe-quality-assessment/run-history.json', JSON.stringify(h, null, 2));
"

Read run-history.json before each run — alert if quality gate failed 3 of last 5 runs.

Skill Composition

  • Before assessment → Run /qe-coverage-analysis and /mutation-testing first
  • If issues found → Use /test-failure-investigator to diagnose failures
  • For PR review → Combine with /code-review-quality for comprehensive review

Gotchas

  • NEVER trust agent-reported pass/fail status — 12 test failures were caught that agents claimed were passing (Nagual pattern, reward 0.92)
  • Completion theater: agent hardcoded version '3.0.0' instead of reading from package.json — verify actual values in output
  • Fix issues in priority waves (P0 → P1 → P2) with verification between each wave — don't fix everything in parallel
  • quality-assessment domain has 53.7% success rate — expect failures and have fallback
  • If HybridMemoryBackend initialization fails, run aqe health to diagnose, or aqe init to re-initialize

Coordination

Primary Agents: qe-quality-analyzer, qe-deployment-advisor, qe-metrics-collector Coordinator: qe-quality-coordinator Related Skills: qe-coverage-analysis, security-testing

© proffesor-for-testing, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts) in assets/skills/qe-quality-assessment of proffesor-for-testing/agentic-qe.

  • SKILL.md
  • evals/qe-quality-assessment.yaml
  • run-history.json
  • schemas/output.json
  • scripts/validate-config.json

Open the folder on GitHubat commit 1363bc7

Compare with similar skills

Qe Quality Assessment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qe Quality Assessment compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qe Quality Assessment this skillproffesor-for-testing/agentic-qe495—~1.9kAutomated safety check: PassMIT
Sonarclaudeagentculture/culture114—~764Automated safety check: PassApache-2.0
Code Quality Gate Checkertelagod/code-abyss244—~609Automated safety check: NotesMIT
Constraint-Driven Developmentaddyosmani/agent-skills104k2 repos~5.2kAutomated safety check: PassMIT
AI Development Guideshinpr/claude-code-workflows694—~3.9kAutomated safety check: PassMIT
Sonarcloud Reviewlucasvieirasilva/nx-plugins153—~2.7kAutomated safety check: NotesMIT

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Questions about Qe Quality Assessment

What does Qe Quality Assessment do?

Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics. Qe Quality Assessment is an agent skill from proffesor-for-testing/agentic-qe. Evaluates code quality through complexity analysis, lint results, code smell detection, and test health metrics.

When should I use Qe Quality Assessment?

Qe Quality Assessment fits situations like: assessing deployment readiness; configuring quality gates; scoring a codebase for release; generating quality reports with pass/fail verdicts.

How do I install Qe Quality Assessment in Claude Code?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-quality-assessment -a claude-code`. Or copy the skill folder (assets/skills/qe-quality-assessment in proffesor-for-testing/agentic-qe) into .claude/skills/qe-quality-assessment in your project. Claude Code loads it when a task matches its description.

How do I install Qe Quality Assessment in Codex?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-quality-assessment -a codex`. Or copy the skill folder (assets/skills/qe-quality-assessment in proffesor-for-testing/agentic-qe) into .agents/skills/qe-quality-assessment in your project. Codex loads it when a task matches its description.

Can I use Qe Quality Assessment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add proffesor-for-testing/agentic-qe --skill qe-quality-assessment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qe-quality-assessment, .gemini/skills/qe-quality-assessment, .github/skills/qe-quality-assessment and .opencode/skills/qe-quality-assessment in your project.

What does Qe Quality Assessment need to run?

Going by SKILL.md and its folder, Qe Quality Assessment needs the command-line tools its instructions call (node).

Does Qe Quality Assessment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qe Quality Assessment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Qe Quality Assessment use?

Qe Quality Assessment is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qe Quality Assessment use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qe Quality Assessment?

Skills that share tags, products or a category with Qe Quality Assessment: Sonarclaude (agentculture/culture, 114 stars), Code Quality Gate Checker (telagod/code-abyss, 244 stars), Constraint-Driven Development (addyosmani/agent-skills, 104k stars) and AI Development Guide (shinpr/claude-code-workflows, 694 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qe Quality Assessment?

proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 495 GitHub stars. The repository holds 93 skills in this directory. The repository was last updated on October 9, 2026.

Source: proffesor-for-testing/agentic-qe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.