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Agent skill

Agentic Quality Engineering

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

A skill your agent uses when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.

MITAuto-check passedTesting & QA

Install Agentic Quality Engineering

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

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

GitHub CLI
$ gh skill install proffesor-for-testing/agentic-qe agentic-quality-engineering --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/agentic-quality-engineering .claude/skills/agentic-quality-engineering && 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
agentic-quality-engineering
GitHub stars
494
Token cost
~2.5k tokens
SKILL.md length
597 words
Files
2
Skills in repo
95
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.

  • Works in 5 steps: SPAWN appropriate agent(s) for the task… → CONFIGURE agent coordination… → EXECUTE with PACTS principles: Proactive… → …
  • Orchestrating QE agents
  • SKILL.md covers Quick Reference Card, Core Concepts, Agent Coordination and Implementation Phases, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agentic Quality Engineering is an agent skill from proffesor-for-testing/agentic-qe. Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `schemas/output.json`).

It sits in Testing & QA. 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

  • Orchestrating QE agents
  • Understanding PACTS principles
  • Configuring the AQE v3 fleet
  • Leveraging AI agents as force multipliers for quality work

Example prompts

  • “/agentic-quality-engineering”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. SPAWN appropriate agent(s) for the task using Task tool with agent type
  2. CONFIGURE agent coordination (hierarchical/mesh/sequential)
  3. EXECUTE with PACTS principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus, Structured…
  4. VALIDATE results through quality gates before deployment
  5. LEARN from outcomes - store patterns in aqe/learning/* namespace

What it can do on your machine

Read from SKILL.md and the folder at commit 829d030. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash, typescript and yaml).

    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

Agentic Quality Engineering loads about 2.5k tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 597 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~47
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from proffesor-for-testing/agentic-qe at commit 829d030, republished under its MIT licence (© proffesor-for-testing). 597 words, ~2,528 tokens.

Download SKILL.mdSave it as .claude/skills/agentic-quality-engineering/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
agentic-quality-engineering
description
Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.
category
qe-core
priority
critical
tokenEstimate
1400
agents
qe-test-generator, qe-test-executor, qe-coverage-analyzer, qe-quality-gate, qe-quality-analyzer, qe-performance-tester, qe-security-scanner…
implementation_status
optimized
optimization_version
1
last_optimized
2025-12-02
quick_reference_card
true
tags
pacts, agents, fleet, coordination, autonomous, structured, foundational
trust_tier
1

Agentic Quality Engineering

<default_to_action> When implementing agentic QE or coordinating agents:

  1. SPAWN appropriate agent(s) for the task using Task tool with agent type
  2. CONFIGURE agent coordination (hierarchical/mesh/sequential)
  3. EXECUTE with PACTS principles: Proactive analysis, Autonomous operation, Collaborative feedback, Targeted risk focus, Structured governance (observability and explainability of agent behavior)
  4. VALIDATE results through quality gates before deployment
  5. LEARN from outcomes - store patterns in aqe/learning/* namespace

Quick Agent Selection:

  • Test generation needed → qe-test-generator
  • Coverage gaps → qe-coverage-analyzer
  • Quality decision → qe-quality-gate
  • Security scan → qe-security-scanner
  • Performance test → qe-performance-tester
  • Full pipeline → qe-fleet-commander

Critical Success Factors:

  • Agents amplify human expertise, not replace it
  • Human-in-the-loop for critical decisions
  • Measure: bugs caught, time saved, coverage improved </default_to_action>

Quick Reference Card

When to Use
  • Designing autonomous testing systems
  • Scaling QE with intelligent agents
  • Implementing multi-agent coordination
  • Building CI/CD quality pipelines
PACTS Principles
PrincipleAgent BehaviorHuman Role
ProactiveAnalyze pre-merge, predict riskSet guardrails
AutonomousExecute tests, fix flaky testsReview critical
CollaborativeMulti-agent coordinationProvide context
TargetedRisk-based prioritizationDefine risk areas
StructuredGovernance, observability, explainable decisions (measure confidence, not trust)Audit behavior, set policy
19-Agent Fleet
CategoryAgentsPrimary Use
Core Testing (5)test-generator, test-executor, coverage-analyzer, quality-gate, quality-analyzerDaily testing
Performance/Security (2)performance-tester, security-scannerNon-functional
Strategic (3)requirements-validator, production-intelligence, fleet-commanderPlanning
Advanced (4)regression-risk-analyzer, test-data-architect, api-contract-validator, flaky-test-hunterSpecialized
Visual/Chaos (2)visual-tester, chaos-engineerEdge cases
Deployment (1)deployment-readinessRelease
Analysis (1)code-complexityMaintainability
Coordination Patterns
Hierarchical: fleet-commander → [generators] → [executors] → quality-gate
Mesh: test-gen ↔ coverage ↔ quality (peer decisions)
Sequential: risk-analyzer → test-gen → executor → coverage → gate
Success Criteria

✅ 10x deployment frequency with same/better quality ✅ Coverage gaps detected in real-time ✅ Bugs caught pre-production ❌ Agents acting without human oversight on critical decisions ❌ Deploying all 19 agents at once (start with 1-2)

Core Concepts

QE Evolution
StageApproachLimitation
TraditionalManual everythingHuman bottleneck
AutomationScripts + fixed scenariosNeeds orchestration
AgenticAI agents + human judgmentRequires trust-building

Core Premise: Agents amplify human expertise for 10x scale.

Key Capabilities

1. Intelligent Test Generation

typescript
// Agent analyzes code change, generates targeted tests
const tests = await qeTestGenerator.generate(prDiff);
// → Happy path, edge cases, error handling tests

2. Pattern Detection - Scan logs, find anomalies, correlate errors

3. Adaptive Strategy - Adjust test focus based on risk signals

4. Root Cause Analysis - Link failures to code changes, suggest fixes

Agent Coordination

Memory Namespaces
aqe/test-plan/*     - Test planning decisions
aqe/coverage/*      - Coverage analysis results
aqe/quality/*       - Quality metrics and gates
aqe/learning/*      - Patterns and Q-values
aqe/coordination/*  - Cross-agent state
Memory Operations (MCP Tools)

CRITICAL: Always use aqe memory store with persist: true for learnings.

1. Store data to persistent memory:

bash
// Store test plan decisions (persisted to .agentic-qe/memory.db)
aqe memory store \
  --key "aqe/test-plan/pr-123" \
  --namespace "aqe/test-plan" \
  --value '{...}' \
  --json

2. Retrieve prior learnings before task:

bash
// Query patterns before starting test generation
const priorData = await aqe memory get --key "aqe/learning/patterns/test-generation/*" --namespace "aqe/learning" --json

// Use patterns to guide current task
if (priorData.success) {
  console.log(`Loaded ${priorData.patterns.length} prior patterns`);
}

3. Store coverage analysis results:

bash
aqe memory store \
  --key "aqe/coverage/auth-module" \
  --namespace "aqe/coverage" \
  --value '{...}' \
  --json
Show full SKILL.md (228 more words)Show less
Three-Phase Memory Protocol

For coordinated multi-agent tasks, use the STATUS → PROGRESS → COMPLETE pattern:

bash
// PHASE 1: STATUS - Task starting
aqe memory store \
  --key "aqe/coordination/task-123/status" \
  --namespace "aqe/coordination" \
  --value '{...}' \
  --json

// PHASE 2: PROGRESS - Intermediate updates
aqe memory store \
  --key "aqe/coordination/task-123/progress" \
  --namespace "aqe/coordination" \
  --value '{...}' \
  --json

// PHASE 3: COMPLETE - Task finished
aqe memory store \
  --key "aqe/coordination/task-123/complete" \
  --namespace "aqe/coordination" \
  --value '{...}' \
  --json
Blackboard Events
EventTriggerSubscribers
test:generatedNew tests createdexecutor, coverage
coverage:gapGap detectedtest-generator
quality:decisionGate evaluatedfleet-commander
security:findingVulnerability foundquality-gate
Example: PR Quality Pipeline
typescript
// 1. Risk analysis
const risks = await Task("Analyze PR", prDiff, "qe-regression-risk-analyzer");

// 2. Generate tests for risks
const tests = await Task("Generate tests", risks, "qe-test-generator");

// 3. Execute + analyze
const results = await Task("Run tests", tests, "qe-test-executor");
const coverage = await Task("Check coverage", results, "qe-coverage-analyzer");

// 4. Quality decision
const decision = await Task("Evaluate", {results, coverage}, "qe-quality-gate");
// → GO/NO-GO with rationale

Implementation Phases

PhaseDurationGoalAgent(s)
ExperimentWeeks 1-4Validate one use case1 agent
IntegrateMonths 2-3CI/CD pipeline3-4 agents
ScaleMonths 4-6Multiple use cases8+ agents
EvolveOngoingContinuous learningFull fleet
Phase 1 Example
bash
# Week 1: Deploy single agent
aqe agent spawn qe-test-generator

# Weeks 2-3: Generate tests for 10 PRs
# Track: bugs found, test quality, review time

# Week 4: Measure impact
aqe agent metrics qe-test-generator
# → Tests: 150, Bugs: 12, Time saved: 8h

Limitations & Strengths

Agents Excel At
  • Volume: Scan thousands of logs in seconds
  • Patterns: Find correlations humans miss
  • Tireless: 24/7 testing and monitoring
  • Speed: Instant code change analysis
Agents Need Humans For
  • Business context and priorities
  • Ethical judgment and trade-offs
  • Creative exploration ("what if" scenarios)
  • Domain expertise (healthcare, finance, legal)

Best Practices

DoDon't
Start with one agent, one use caseDeploy all 18 at once
Build feedback loops earlyDeploy and forget
Human reviews agent outputAuto-merge without review
Measure bugs caught, time savedTrack vanity metrics (test count)
Build trust graduallyGive full autonomy immediately
Trust Progression
Month 1: Agent suggests → Human decides
Month 2: Agent acts → Human reviews after
Month 3: Agent autonomous on low-risk
Month 4: Agent handles critical with oversight

Agent Coordination Hints

yaml
coordination:
  topology: hierarchical
  commander: qe-fleet-commander
  memory_namespace: aqe/coordination
  blackboard_topic: qe-fleet

preload_skills:
  - agentic-quality-engineering  # Always (this skill)
  - risk-based-testing           # For prioritization
  - quality-metrics              # For measurement

agent_assignments:
  qe-test-generator: [api-testing-patterns, tdd-london-chicago]
  qe-coverage-analyzer: [quality-metrics, risk-based-testing]
  qe-security-scanner: [security-testing, risk-based-testing]
  qe-performance-tester: [performance-testing]
  • holistic-testing-pact - PACTS principles deep dive
  • risk-based-testing - Prioritize agent focus
  • quality-metrics - Measure agent effectiveness
  • api-testing-patterns, security-testing, performance-testing - Specialized testing

Resources

  • Agent definitions: .claude/agents/
  • CLI: aqe agent --help
  • Fleet status: aqe fleet status

Success Metric: Deploy 10x more frequently with same or better quality through intelligent agent collaboration.

© 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 1 other file in assets/skills/agentic-quality-engineering of proffesor-for-testing/agentic-qe.

  • SKILL.md
  • schemas/output.json

Open the folder on GitHubat commit 829d030

Compare with similar skills

Agentic Quality Engineering 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.

Agentic Quality Engineering compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agentic Quality Engineering this skillproffesor-for-testing/agentic-qe494—~2.5kAutomated safety check: PassMIT
OpenHarness End-to-End EvalsHKUDS/OpenHarness16k1 repos~2.1kAutomated safety check: NotesMIT
Clawteam DevHKUDS/ClawTeam5.5k1 repos~1.1kAutomated safety check: PassMIT
Acceptance Evidence for Deliverieslobehub/lobehub83k—~9.7kAutomated safety check: PassApache-2.0
Qwen Code E2E TestingQwenLM/qwen-code28k—~2.1kAutomated safety check: PassApache-2.0
Skill Testdatabricks-solutions/ai-dev-kit1.9k—~1.9kAutomated safety check: PassCustom licence

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Categories

Questions about Agentic Quality Engineering

What does Agentic Quality Engineering do?

A skill your agent uses when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work. Agentic Quality Engineering is an agent skill from proffesor-for-testing/agentic-qe. Use when orchestrating QE agents, understanding PACTS principles, configuring the AQE v3 fleet, or leveraging AI agents as force multipliers for quality work.

When should I use Agentic Quality Engineering?

Agentic Quality Engineering fits situations like: orchestrating QE agents; understanding PACTS principles; configuring the AQE v3 fleet; leveraging AI agents as force multipliers for quality work.

How do I install Agentic Quality Engineering in Claude Code?

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

How do I install Agentic Quality Engineering in Codex?

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

Can I use Agentic Quality Engineering 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 agentic-quality-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agentic-quality-engineering, .gemini/skills/agentic-quality-engineering, .github/skills/agentic-quality-engineering and .opencode/skills/agentic-quality-engineering in your project.

What does Agentic Quality Engineering need to run?

SKILL.md names no scripts, command-line tools or credentials: Agentic Quality Engineering is instructions for the agent only.

Does Agentic Quality Engineering 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 Agentic Quality Engineering 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. Review the folder before installing.

What licence does Agentic Quality Engineering use?

Agentic Quality Engineering 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 Agentic Quality Engineering use?

About 2.5k tokens (SKILL.md is roughly 10k 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 Agentic Quality Engineering?

Skills that share tags, products or a category with Agentic Quality Engineering: OpenHarness End-to-End Evals (HKUDS/OpenHarness, 16k stars), Clawteam Dev (HKUDS/ClawTeam, 5.5k stars), Acceptance Evidence for Deliveries (lobehub/lobehub, 83k stars) and Qwen Code E2E Testing (QwenLM/qwen-code, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agentic Quality Engineering?

proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 494 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on October 4, 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.