proffesor-for-testing/agentic-qe

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Catalogue SkillsMP mis à jour: 26 octobre 2026 à 11:37

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<img src="assets/readme/project-header.svg" width="100%" alt="Agentic QE. Better tests, clearer risk, evidence you can inspect. Generate, execute, challenge and learn." /> # Agentic QE **Quality engineering tools for your coding agent.** Generate tests, investigate coverage gaps, analyze flaky behavior and coordinate quality checks across the software lifecycle. Keep the findings close to the code and use them to make a better quality decision. [![npm version](https://img.shields.io/npm/v/agentic-qe?color=168c91)](https://www.npmjs.com/package/agentic-qe) [![Monthly Downloads](https://img.shields.io/npm/dm/agentic-qe?color=168c91)](https://www.npmjs.com/package/agentic-qe) [![Total Downloads](https://img.shields.io/npm/d18m/agentic-qe?label=total%20downloads&color=168c91)](https://www.npmjs.com/package/agentic-qe) [![License: MIT](https://img.shields.io/badge/license-MIT-168c91)](LICENSE) [Start with one task](#start-with-one-task) · [Choose your client](#choose-your-client) · [Inspect the evidence](#inspect-the-evidence) · [Contribute](#contribute) · [Release notes](docs/releases/README.md) ## Put AQE to work | Your QE problem | What AQE helps you do | What to inspect | | --- | --- | --- | | A changed service needs meaningful tests | Generate unit, integration, property-based or BDD tests | Assertions, edge cases and executed test results | | Coverage is high but important behavior is missing | Analyze gaps and prioritize by risk | Uncovered branches and the rationale for priorities | | A flaky suite is eroding trust | Investigate failure patterns and suggest stabilization | Reproduction and results before and after a change | | A release needs a quality assessment | Coordinate coverage, security, contracts and resilience checks | Findings, tool output and unresolved risks | | Your team repeats the same investigation | Store and retrieve QE patterns | Pattern relevance and recorded outcomes | Supports framework-specific output for tools including Jest, Vitest, Playwright, Cypress, pytest and JUnit. Review generated tests and run them in your environment; an agent's recommendation is an input to your team's decision. ## Start with one task Requires **Node.js ≥22.13.0** and **npm ≥10.0.0**. Use the coding agent/client you already have configured. ```bash npm install -g agentic-qe cd your-project aqe init --auto aqe health ``` **npm 12+:** dependency install scripts are blocked unless allowed, and AQE's SQLite store (`better-sqlite3`) needs its install script to fetch its native binary. Name the packages when you install: ```bash npm install -g agentic-qe --allow-scripts=agentic-qe,better-sqlite3,hnswlib-node ``` Include `agentic-qe` in the command — npm's warning suggests `npm install -g --allow-scripts=...` without a package name, which installs the current directory instead. If `aqe init` reports that the `better-sqlite3` native module could not be loaded on an existing install, rebuild it with `npm rebuild -g better-sqlite3 --allow-scripts=better-sqlite3` (for a project-local install: `npm install-scripts approve better-sqlite3 && npm rebuild better-sqlite3`). `hnswlib-node` is optional; AQE runs without it. `aqe init --auto` analyzes your project and configures the default Claude Code surface, including MCP. Review the generated project files. For another client, select its setup flag below and use its MCP connection flow. In your coding agent, start with a narrow request: ```text Generate tests for src/services/UserService.ts. Explain the risks and edge cases each test covers. Run the tests if execution is available, and report failures and anything you could not verify. ``` Then ask for a second view: ```text Find coverage gaps in src/services/ and prioritize them by risk. Challenge weak assertions in the generated tests and explain what still needs human review. ``` The paths are examples; use a real file in your project. Success means tests you can inspect and execute, plus a clear account of what remains untested. A coverage target is a request, not a promised result. ## Choose your client | Client | Project setup | | --- | --- | | Claude Code | `aqe init --auto` | | OpenAI Codex CLI | `aqe init --auto --with-codex` | | GitHub Copilot | `aqe init --auto --with-copilot` | | Cursor | `aqe init --auto --with-cursor` | | Cline | `aqe init --auto --with-cline` | | OpenCode | `aqe init --auto --with-opencode` | | AWS Kiro | `aqe init --auto --with-kiro` | | Kilo Code | `aqe init --auto --with-kilocode` | | Roo Code | `aqe init --auto --with-roocode` | | Windsurf | `aqe init --auto --with-windsurf` | | Continue.dev | `aqe init --auto --with-continuedev` | These are implemented setup paths, not a claim of identical behavior across clients. The default Claude Code files are also created unless you add `--no-claude` when targeting another client. ```bash # Configure every supported client aqe init --auto --with-all-platforms # Inspect or add client configuration later aqe platform list aqe platform setup cursor aqe platform verify cursor # Standalone MCP server entry point aqe-mcp ``` See the [Platform Setup Guide](docs/platform-setup-guide.md) for client-specific configuration. ### Claude Code plugin: a scoped alternative Use the `agentic-qe-fleet` plugin when you want a smaller Claude Code surface with slash commands. The full `aqe init` path adds broader project configuration and persistent learning setup. In Claude Code: ```text /plugin install agentic-qe-fleet --marketplace proffesor-for-testing/agentic-qe ``` Or load it from a checkout: ```bash git clone https://github.com/proffesor-for-testing/agentic-qe.git claude --plugin-dir ./agentic-qe/plugins/agentic-qe-fleet ``` Start with `/aqe-fleet-status`, `/aqe-generate src/services/Auth.ts` or `/aqe-analyze src/`. The plugin registers its own MCP server, pinned to the matching package version (`npx -y agentic-qe@<plugin version> mcp`). See the [plugin README](plugins/agentic-qe-fleet/README.md) for the bundled agents, commands and skills. <details> <summary>Windows and native dependency setup</summary> `agentic-qe` runs on Windows, but several of its performance-oriented native dependencies (`hnswlib-node` for HNSW search, `@ruvector/gnn` for graph neural networks, `@ruvector/rvf-node` for the RVF pattern store) ship as optional native modules. `hnswlib-node` in particular has no prebuilt binaries and compiles from source via `node-gyp`. `npm install` does not fail when these modules can't build — they're declared optional and AQE falls back to JavaScript paths at runtime. **Important caveat about the fallback.** The pure-JavaScript HNSW fallback (`ProgressiveHnswBackend`) is correct but degrades to O(N) brute-force search when neither `hnswlib-node` nor `@ruvector/gnn` is available. That's fine for small projects but **unsuitable for large indexes** (tens of thousands of vectors and up). If you plan to run AQE against a sizeable codebase on Windows, install the native build toolchain so `hnswlib-node` compiles: - **Python 3** (added to `PATH`), and - **Visual Studio 2022 Build Tools** with the *Desktop development with C++* workload — works with any node-gyp version, **or** - **Visual Studio 2026** with the same workload **plus** an upgraded npm (`npm install -g npm@latest` — VS 2026 detection requires npm ≥ 11.6.3, shipped via node-gyp ≥ 12.1.0). Node 22 LTS still ships npm 10.x by default, which cannot detect VS 2026; either upgrade npm globally or use VS 2022 Build Tools instead. If the native dep fails to build you'll see a `node-gyp` warning during install (e.g. `gyp ERR! find VS`); the install itself completes. To verify which backend is active: ```bash aqe health # Look for "HNSW backend: native" (hnswlib-node) or "HNSW backend: js" # (ProgressiveHnswBackend — see caveat above). ``` On Linux and macOS, verify the active backend with `aqe health` as well; native module availability depends on your environment. </details> ## How a quality task flows <img src="assets/readme/quality-loop.svg" width="100%" alt="Conceptual quality workflow: frame risk, generate and execute tests, challenge findings through coverage, mutation, security and review, then make a human quality decision and record the outcome." /> The `qe-queen-coordinator` decomposes a quality assessment into domain tasks and combines findings. Domains cover test generation and execution, coverage, quality assessment, defect intelligence, requirements, code intelligence, security, contracts, visual accessibility, resilience, learning and enterprise integration. Patterns can be stored and retrieved across sessions. AQE provides outcome feedback and consolidation mechanisms; retrieval alone does not prove that future tests are better. Model routing offers cost-aware task selection, while actual costs and quality depend on your provider and workload. Anti-sycophancy checks examine weak or tautological tests. Adversarial review adds a way to challenge a verdict. See [quality gate features](docs/loki-mode-features.md) and [QE Court design](docs/implementation/adrs/ADR-124-qe-court-adversarial-verdict-service.md). ## Inspect the evidence | Evidence | What it supports | Scope and limits | | --- | --- | --- | | [Shipped agent definitions](assets/agents/v3/) | Inspect each agent's intended role and instructions | Source definitions establish inventory, not demonstrated effectiveness | | [Packaged skills](assets/skills/) and [validation guide](docs/guides/skill-validation.md) | Inspect workflows, validators and evaluations | Trust tiers describe validation artifacts; they do not guarantee production outcomes | | [Interaction benchmark run records](benchmarks/interaction/results/README.md) | Compare QE-guided and bare interactions against ground truth | June 11, 2026: two scenarios; no demonstrated arm benefit, statistically underpowered | | [MCP tests](tests/unit/mcp/) and [integration tests](tests/integration/mcp/) | Inspect protocol and handler checks | Repository tests are not a record of a fresh passing run | | [Changelog](CHANGELOG.md) and [release notes](docs/releases/README.md) | Trace changes and fixes | Read the entry matching your installed version | **Inventory verified October 9, 2026 at [`829d030`](https://github.com/proffesor-for-testing/agentic-qe/tree/829d03060d56ee82e6fa294b2be8f6c5fc9f2766):** 53 top-level `qe-*.md` agent definitions + 7 QE subagents; 86 packaged `SKILL.md` entry points; 13 domain directories. The scoped plugin separately contains 11 agents, 9 skills and 9 command files. Client setup flags above cover 11 clients. These counts exclude platform infrastructure agents and supporting Markdown files. ## Commands for everyday work ```bash aqe agent list # Agent inventory aqe fleet status # Fleet status aqe health # Runtime health aqe learning stats # Learning statistics aqe learning dream # Pattern consolidation aqe brain export # Export learned patterns aqe code index src/ # Build code intelligence index aqe code index src/ --incremental aqe code index . --git-since HEAD~5 aqe code search "authentication" aqe code impact src/ aqe code deps src/ aqe code complexity src/ aqe code c4 . # C4 diagrams with confidence ``` ## LLM providers and local models AQE Fleet's LLM-enhanced analysis (ADR-043, ADR-051) routes through a HybridRouter that picks providers based on routing rules and your env config. Set one or more API keys and Fleet auto-detects what's available: | Provider | Env var(s) | Type | Notes | |----------|-----------|------|-------| | **Claude** | `ANTHROPIC_API_KEY` | Cloud | Default in routing rules | | **OpenAI** | `OPENAI_API_KEY` | Cloud | Wide model coverage | | **Gemini** | `GOOGLE_AI_API_KEY` / `GEMINI_API_KEY` / `GOOGLE_API_KEY` | Cloud | Cheap; free tier | | **OpenRouter** | `OPENROUTER_API_KEY` | Cloud | Multiple models behind one key | | **Ollama** | (local) | Local | Local inference | | **Azure OpenAI** | `AZURE_OPENAI_API_KEY` | Cloud | Enterprise | | **Bedrock** | `AWS_ACCESS_KEY_ID` | Cloud | AWS-native | ```bash export GEMINI_API_KEY="..." # or any supported provider aqe init --auto ``` Persistent config (`aqe llm config --set` writes to `.agentic-qe/llm-config.json`): ```bash aqe llm config --set mode=cost-optimized aqe llm config --set defaultProvider=gemini aqe llm config # show current effective config ``` API keys are env-only — `aqe llm config --set apiKey=...` is refused to keep secrets out of project-committed config. ### Opting out To run Fleet without any LLM calls (deterministic-only mode): ```bash export AQE_LLM_ROUTER_DISABLED=1 # env-only kill-switch ``` Or programmatically: `new QEKernelImpl({ llmRouter: { enabled: false } })`. ### Free local models (opt-in) — cheap-first test generation Run test generation on a **free local model first** (local Ollama, cloud Ollama, OpenRouter free models, or any OpenAI-compatible endpoint), with an automatic **repair loop**, falling back to your normal LLM path only when needed. Off by default — enable with `AQE_FREE_TIER=1`: ```bash ollama pull qwen3-coder:30b export AQE_FREE_TIER=1 # opt in (default model: qwen3-coder:30b) ``` `qwen3-coder:30b` needs ~18 GB of RAM. `qwen3:8b` fits smaller machines but measured below the QE test-generation quality floor (ADR-111). Local inference avoids per-call cloud charges; hardware costs and fallback cloud calls still apply. See the [Free-Tier Local Models guide](docs/guides/free-tier-local-models.md) for provider options and configuration. ## Documentation | Guide | Use it for | | --- | --- | | [Platform setup](docs/platform-setup-guide.md) | Client configuration | | [Free-tier local models](docs/guides/free-tier-local-models.md) | Opt-in local/free generation and fallback | | [Skill validation](docs/guides/skill-validation.md) | Trust tiers and evaluations | | [Learning system](docs/guides/reasoningbank-learning-system.md) | Pattern storage and feedback | | [Code intelligence](docs/guides/fleet-code-intelligence-integration.md) | Indexing, graphs and search | | [Architecture glossary](docs/v3-technical-architecture-glossary.md) | Technical terminology | ## Contribute Bring a small problem we can reproduce. Useful starting points include a failing test, a client setup issue, a better example, or a validator that challenges an unsupported claim. Read [CONTRIBUTING.md](CONTRIBUTING.md) and [AGENTS.md](AGENTS.md) before changing code. For bug reports, include your AQE version, Node version, operating system, client, expected behavior and a minimal reproduction. Use [Issues](https://github.com/proffesor-for-testing/agentic-qe/issues) for reproducible bugs, [Issues](https://github.com/proffesor-for-testing/agentic-qe/issues) for questions and use cases, and the [security policy](SECURITY.md) for security reports. ```bash git clone https://github.com/proffesor-for-testing/agentic-qe.git cd agentic-qe npm install npm run build npm test -- --run ``` For a focused change, start with the relevant checks documented in [AGENTS.md](AGENTS.md), such as `npm run typecheck`, `npm run lint` and the relevant test suite. Keep production learning databases intact and test data changes against copies. ## License and support **MIT** — see [LICENSE](LICENSE). Free to use, fork, build and contribute under its terms. [Support the project](FUNDING.md) · [Project documentation](docs/) · [View all contributors](CONTRIBUTORS.md) ## Contributors  | <img src="https://github.com/proffesor-for-testing.png" width="60" style="border-radius:50%"/> **[@proffesor-for-testing](https://github.com/proffesor-for-testing)** Project Lead | <img src="https://github.com/fndlalit.png" width="60" style="border-radius:50%"/> **[@fndlalit](https://github.com/fndlalit)** QX Partner, Testability | <img src="https://github.com/shaal.png" width="60" style="border-radius:50%"/> **[@shaal](https://github.com/shaal)** Core Development | <img src="https://github.com/mondweep.png" width="60" style="border-radius:50%"/> **[@mondweep](https://github.com/mondweep)** Architecture | |:---:|:---:|:---:|:---:| | <img src="https://github.com/rudycelekli.png" width="60" style="border-radius:50%"/> **[@rudycelekli](https://github.com/rudycelekli)** Reliability & Security Fixes | <img src="https://github.com/stuinfla.png" width="60" style="border-radius:50%"/> **[@stuinfla](https://github.com/stuinfla)** Test Execution Evidence | <img src="https://github.com/nagoodman.png" width="60" style="border-radius:50%"/> **[@nagoodman](https://github.com/nagoodman)** OpenCode Integration | <img src="https://github.com/Jordi-Izquierdo-DDS.png" width="60" style="border-radius:50%"/> **[@Jordi-Izquierdo-DDS](https://github.com/Jordi-Izquierdo-DDS)** RVF/HNSW Fix | | <img src="https://github.com/JLMA-Agentic-Ai.png" width="60" style="border-radius:50%"/> **[@JLMA-Agentic-Ai](https://github.com/JLMA-Agentic-Ai)** Package Exports | <img src="https://github.com/amonkarsidhant.png" width="60" style="border-radius:50%"/> **[@amonkarsidhant](https://github.com/amonkarsidhant)** MCP-first Setup | <img src="https://github.com/gurdasnijor.png" width="60" style="border-radius:50%"/> **[@gurdasnijor](https://github.com/gurdasnijor)** Smithery Integration | |  ## Acknowledgments Agentic QE was developed with help from **[Ruflo](https://github.com/ruvnet/ruflo), formerly Claude Flow**, by **[rUv](https://github.com/ruvnet)**, for coordination during development. **[RuVector](https://github.com/ruvnet/ruvector) provides the vector and RVF database foundation** through the `@ruvector/*` packages; AQE also uses `better-sqlite3` for SQLite persistence. Ruflo is development tooling, not a required AQE runtime dependency. <a href="https://github.com/ruvnet/ruflo"><img src="assets/readme/credit-ruflo.svg" width="600" alt="Ruflo, formerly Claude Flow. Coordination support during Agentic QE development. Open the upstream repository." /></a> <a href="https://github.com/ruvnet/ruvector"><img src="assets/readme/credit-ruvector.svg" width="600" alt="RuVector. Vector search and RVF pattern storage, alongside SQLite persistence. Open the upstream repository." /></a> Thanks also to [Agentic Flow](https://github.com/ruvnet/agentic-flow) for agent patterns and learning systems, and to the maintainers and contributors across the testing tools this project builds on. The cards are original AQE artwork and link to the upstream projects.

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Analystes en assurance qualité des logiciels et testeurs Développeurs de logiciels Analystes en sécurité de l'information Autres occupations informatiques

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qe-court

Analystes en assurance qualité des logiciels et testeurs

Adversarial review court — a delivery (diff, PR, test suite, or artifact) is prosecuted by independent AI reviewers from different vendors, each with its own probe set, then a SHIP verdict must SURVIVE escalating deeper reviewers before it stands. Use when…