测试设计、漏洞检测、质量保证（1.3万 个）
Use when comparing Scenario models on the same brief through MCP: a bake-off with shared prompts and inputs, cost, generation time and quality measured side by side, choosing a default model, testing a new release against the incumbent, or finding which model handles a style, edit, or reference image best. Keywords: model comparison, benchmark, bake-off, A/B test, contact sheet, cost per asset, latency.
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Use when a Scenario output must be checked and improved rather than accepted first roll: iterating a generation until it matches the brief, fixing a batch that came back off-brief, retrying failed shots methodically, wiring an automated generate, review, revise loop, or deciding whether to re-prompt, swap references, inpaint, post-process, or change model. Keywords: refine, iterate, critique, review loop, QA, self-correction, verify, acceptance criteria, retry, drift.
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Use when a generated Scenario image needs a quality check or a brand-brief compliance verdict before it ships: pass/warn/fail scoring, on-brand review against a configured brief, QA gating a batch, pricing or refreshing a stored verdict, or iterating a generation until it clears the gate. Keywords: quality gate, quality check, QA, verdict, brief compliance, on-brand, brand brief, pass fail, score, review, asset_quality_gate_run.
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Use when finished Scenario assets have to give something back: a caption for a dataset or alt text, a reusable style description, a verdict against a brief, a canny, depth, pose, or segmentation control map for the next model, or the asset itself found again by text, tags, or visual similarity and filed into a collection. Keywords: caption, describe, analyze, QA, control map, find similar, reverse image search, semantic search, tag, collection.
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Audit model delegation and subagent effectiveness for a session — which models handled which subagent types, per-type success rates and average durations, and wasted delegations (heavy models on trivial work or types that consistently fail) — using the Agent Monitor workflow intelligence API. Use when reviewing how a session delegated work across models and subagents.
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Generate a CCAM reliability report from session outcomes, hook events, alerts, tool failures, and data freshness. Use for health reviews, incident follow-up, hook-delivery audits, or reliability trend summaries.
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Audit hook delivery health from Agent Monitor data — balance PreToolUse vs PostToolUse (a gap means tools that started but never reported back), detect missing Stop/SubagentStop terminators (sessions/subagents that never closed), and check for stale ingestion (no recent events). Use when hooks look unreliable or events seem to be dropping.
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Audit the configured MCP servers (user + project scope) via the Agent Monitor Config Explorer API: transport (stdio vs http), command/args and env variable names, headers, and the source file each definition came from. Reads /api/cc-config/mcp. Use when reviewing MCP integrations for hygiene, duplication, or unexpected transports.
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MANDATORY for every coding agent (Claude Code, Codex, or any other) on every change-set — every applicable source file the agent creates or updates MUST start with the project's copyright/authorship header (file overview + exact author line). Use automatically whenever writing a new file or editing an existing one; do not wait to be asked. Covers JS/TS/TSX/CJS/MJS, Python, shell, and CSS. Includes the audit script to verify repo-wide compliance.
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Run a full audit of the user's Claude Code configuration via the Agent Monitor Config Explorer API: counts per surface (user vs project), duplicate or overlapping skills and subagents, hooks that run shell commands, and which surfaces are read-only vs mutable. Reads /api/cc-config/overview, /skills, /agents, /commands, /hooks, and /settings. Use when reviewing your Claude Code setup for sprawl, duplication, or risk.
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Inspect and safely edit the Claude Code and Codex configuration surfaces exposed by CCAM. Use when auditing skills, agents, commands, plugins, marketplaces, MCP servers, hooks, settings, memory, keybindings, profiles, rules, or instruction files, and when a backup-backed allowlisted edit is required.
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Inventory hooks across the user, project, and project-local settings plus the ~/.claude/hooks scripts directory — read through the Agent Monitor Config Explorer API — and flag hooks that POST to the network or run arbitrary commands. Reads /api/cc-config/hooks and /api/cc-config/hook-scripts. Use when auditing hook safety.
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Crabbox/Testbox remote proof: portable provider routing, untrusted isolation, Linux/macOS/Windows/WSL2, live E2E, diagnostics, cleanup.
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Use when adding a new docs section or product area, editing llms.ts / the llms.txt or llms/[...slug] / llms-full.txt routes / get-llm-text / skill.md / .well-known endpoints, or working on the "agent score", "llms.txt", or anything "agent-ready" in the docs and site apps. Explains the invariants the Mintlify agent-readiness audit measures and how to hold them.
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Generate pytest test cases for Python functions and classes
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Generate comprehensive test suites for existing code
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Code and product quality gates for shipping with confidence. Runs code review, QA testing, performance checks, and security audits. Use when the user says 'review my code', 'is this ready to ship', 'check quality', 'run QA', 'test this', 'security check', 'pre-launch review', or wants a quality assessment before deploying.
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Review a business plan from the perspective of the named target customer. Asks: would they actually pay this price for this solution today? Outputs a verdict with one of four modes — PAY NOW / PAY WITH FRICTION / WOULDN'T PAY / WRONG ICP. Use when the user has a defined ICP and pricing and wants to stress-test whether the value prop survives contact with reality. Triggered by: 'customer review', 'would they pay', 'pricing reality check', '客户视角', '会付费吗'.
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Discover profitable business ideas from scratch. Analyzes market gaps, trending niches, user skills, and competitive landscapes with a competitive intelligence protocol including 4-filter benchmark stress test and Blue Ocean differentiation grid. Use when the user has no idea what to build, wants to explore opportunities, needs market research, competitive benchmarking, or says 'find me a business idea', 'what should I build', 'market research', 'find opportunities', or 'competitive analysis'.
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Build the actual product — from landing page to deployed MVP with payment integration, QA testing, and post-deploy canary monitoring. Handles code generation, deployment, database setup, authentication, Stripe/payment integration, systematic QA protocol, and production health scoring. Use when the user needs to build something, deploy a product, set up payments, create a landing page, or says 'build this', 'deploy', 'create MVP', 'set up payments', or 'ship it'.
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Run an offline ASO audit on canonical App Store metadata under `./metadata` and surface keyword gaps using Astro MCP. Use after pulling metadata with `asc metadata pull`.
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Orchestrate TestFlight distribution, groups, testers, and What to Test notes using asc. Use when rolling out betas.
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Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
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Define, review, or update SECURITY_MODEL.md files that tell security review tools a repository's threat model, trust boundaries, invariants, reportable finding classes, and exclusions. Use when asked to "write a security model", "threat model this repo for scanners", "define security review scope", "what counts as a vulnerability here", "update SECURITY_MODEL.md", "why did the scanner flag or miss this", or to check whether existing security guidance still matches the code. Not for running a security review or editing the SECURITY.md disclosure policy.
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