测试设计、漏洞检测、质量保证（1.3万 个）
The AI Scientist-v2 is Sakana AI's open-source research agent that turns a topic description into research ideas, runs machine-learning experiments with an agentic tree search, and writes a LaTeX paper with citations. Use when someone wants to automate ML research, generate and test hypotheses, run "AI Scientist" or "AI-Scientist-v2", or study automated scientific discovery.
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Run autonomous AI-driven penetration tests on web applications using tools like Shannon, PentAGI, and similar frameworks. Use when tasks involve setting up automated penetration testing pipelines, combining AI agents with security tools (nmap, subfinder, nuclei, sqlmap), building autonomous exploit chains, generating pentest reports with proof-of-concept exploits, or integrating AI pentesting into CI/CD pipelines. Covers the full pentest lifecycle from reconnaissance to reporting using AI orchestration.
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Run AI agent and LLM evaluations in CI/CD pipelines — automated quality gates that fail the build when AI output quality drops. Use when someone asks to "test my AI agent", "add evals to CI", "catch prompt regressions", "compare models", "evaluate LLM output quality", "set up AI quality gates", or "benchmark my agent before deploying". Covers eval frameworks (Cobalt, Promptfoo, Braintrust), LLM-as-judge scoring, threshold-based assertions, and GitHub Actions integration.
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Run AI agent code safely in isolated sandboxes with resource limits, audit trails, and kill switches. Use when someone asks to "sandbox my agent", "run agent code safely", "add guardrails to AI agent", "isolate agent execution", "audit agent actions", "prevent agent from deleting files", "restrict agent permissions", or "add safety controls to AI coding agent". Covers Docker isolation, filesystem restrictions, network policies, resource locking, and comprehensive audit logging.
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Actix Web is a high-performance async web framework for Rust. It provides type-safe extractors, routing, middleware and a built-in test harness for building fast, memory-safe HTTP APIs. Use when a user asks to build a Rust REST API, add an Actix handler or middleware, handle errors with ResponseError, share a database pool, or test an Actix service.
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Audit web pages and components for WCAG 2.2 accessibility compliance. Use when a user asks to check accessibility, find a11y issues, audit for WCAG compliance, fix screen reader problems, check color contrast, ensure keyboard navigation works, or prepare for accessibility regulations like the European Accessibility Act or ADA.
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Detect AI-generated text using rule-based analysis, LLM-as-judge scoring, and optional external APIs. Use when: auditing content for AI authorship, academic integrity checks, editorial review, SEO content audits.
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Plans a controlled experiment (A/B test) so its result can be trusted: writes the hypothesis, picks one primary metric and the guardrails, computes sample size and run time, specifies how visitors are assigned and when exposure is logged, and reads out the result with a confidence interval. Use when someone says "set up an A/B test", "split test this page", "how many visitors do I need", "how long should the experiment run", "is this result significant", "can I stop the test early", or wants to test a headline, price, layout or onboarding change against the current version.
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Aider is an open-source terminal AI pair programmer that edits files in your git repository with an LLM and commits each change. Use when a user asks to run Aider, let an AI refactor or fix code from the command line, script Aider with --message, auto-run lint and tests after edits, or point Aider at Claude, GPT, Gemini, DeepSeek or a local model.
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Resolve the currently-unresolved review threads on the current branch's PR in one sweep — assess each, fix it (test-first, verified) or reply-only, then reply and resolve — and return a summary. Use standalone to address PR review feedback, or as the thread-handling step invoked by /watch-pr. Repo is adamayoung/TMDb.
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Implement features and fix bugs test-first, the Canon TDD way (test list → one test → make it pass → refactor → repeat). Use when implementing any new feature, endpoint, model, or method, fixing a bug, or executing a plan — write the test list and a failing test BEFORE production code.
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Audit the knowledge/ base and the .claude/ skills, agents and workflows for staleness, self-contradiction, and drift from their own stated rules — four independent adversarial auditors (two lenses over two trees) verify every load-bearing claim against the current tree, cross-examine each other within their tree, and reach a consensus on if/what needs changing. Use periodically, after a run of deliveries that changed build config, target layout or the skills themselves, or whenever you suspect the docs have aged out of true.
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Implement the current plan test-first, the Canon TDD way, driving to a single finishing condition — an empty test list. Derives a Canon TDD test list from the plan, shows it before any code, writes one failing test at a time, and keeps the list visible as it evolves. Use when the user asks to implement, build, or execute the current/approved plan.
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Compile the TMDb package AND all test targets (without running the tests) to catch test-code compile errors. Use after changing tests or shared code to check everything still builds — delegates to the tooling-runner agent (Haiku) and returns a concise pass/fail + errors-as-file:line summary. Differs from /build, which compiles only the library; to run the tests, use /test.
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Adversarially review the current implementation plan with three independent critic subagents, reconcile their findings into a consensus, and apply the agreed feedback to the plan. Use after drafting a plan (in plan mode or a plan/design doc) and before starting implementation, or whenever the user asks to pressure-test, critique, or harden a plan.
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Review the working-tree changes (vs main) for correctness, concurrency, architecture, testing, and API/doc issues — following .github/CODE_REVIEW.md — and return a severity-graded report. Scales the machinery to the diff size: a single code-reviewer agent for a small change, or a fan-out-and-verify Workflow for a large/multi-unit one. Produces findings; it does not apply fixes (the caller does).
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Run all TMDb unit tests (Swift Testing, the TMDb test plan). Use after code changes and before a PR to verify unit tests pass — delegates the run to the tooling-runner agent (Haiku) and returns total/passed/failed counts plus each failure as Suite/test with file:line. For the live-API suite, use /integration-test.
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Take a plan all the way to a ready-to-merge pull request — review the plan (scaled to risk), implement it test-first, code-review and fix, run the CI gate, open the PR, and watch it green. Use after you have an approved plan (from plan mode, or the Plan agent); pass `issue <n>` to deliver a specific GitHub issue you name, or `next` to take the top startable issue off the project board's Ready column — either way it re-verifies, claims and plans the issue first. Invoking it with a plan already in hand is itself plan approval — it then runs autonomously to a single hard stop: ready-to-merge.
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Diagnose a failing CI workflow run (lint, markdown lint, build, or unit tests) — identify which job failed, the cause, and a concrete fix
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Diagnose a failing TMDb integration-test run — summarise the failure, rank the likely cause, and suggest a concrete fix
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Run the TMDb integration tests against the live TMDb API (the Integration test plan; requires TMDB_API_KEY/USERNAME/PASSWORD). Use before a PR, or after model/fixture changes, to validate against real API responses — delegates to the tooling-runner agent (Haiku) and returns counts + failures, distinguishing genuine failures from transient live-API/env issues. For mocked unit tests, use /test.
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Run a full implementation verification pass after code or data changes. Use when the user asks to verify, QA, smoke test, run checks, validate a feature, inspect a local app in the browser, capture screenshots, or turn discovered QA issues into regression tests/checklists with user approval.
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Root cause based one-shot bug fix. debugger diagnosis → requirements.md generation → /execute. /bugfix "error description" Full investigation pipeline: debugger + gap-analyzer + standard verify. QA suggestion after successful fix.
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Greenfield project architecture + harness scaffolding for AI Agent productivity. Interview-driven decisions → requirements.md → execute. Produces: Code Structure (vertical slice exemplar), Test Infrastructure, Guard Rails, conditional extensions, AND Harness (CLAUDE.md with domain/team context, rules, skills, hooks). L2: architecture decisions, L3: harness setup, L4: requirements + harness decisions (tasks generated later by /execute into plan.json). Use when: "/scaffold", "scaffold", "new project", "set up project", "프로젝트 세팅", "초기 구조"
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