搜索「Claude Code」的结果

io.github.nexu-io/open-design/design-extract

Extract design tokens (color / typography / spacing) from imported source code, screenshots, or Figma exports into the canonical token bag token-map consumes.

io.github.PostHog/posthog/review-hog-perspective-performance-reliability

The Performance & Reliability review perspective for ReviewHog. Verifies that changed code will perform and hold up in production — resource efficiency, error handling and recovery, scalability, and operational readiness. Reports performance and reliability issues only.

io.github.PostHog/posthog/review-hog-perspective-logic-correctness

The Logic & Correctness review perspective for ReviewHog. Verifies that changed code does what it is supposed to do — business logic, edge cases, data transformations, and query / data-access correctness. Reports correctness issues only; security and performance are separate perspectives.

io.github.PostHog/posthog/review-hog-perspective-contracts-security

The Contracts & Security review perspective for ReviewHog. Verifies that changed code is safe and maintains compatibility — API contracts and breaking changes, injection / authz / data exposure, input validation, and schema / interface alignment. Reports security and contract issues only.

io.github.microsoft/ai-agents-for-beginners/testing-course-samples

Use wen dem ask to check, test, do smoke-test, or run di course notebook and code samples against live Microsoft Foundry / Azure OpenAI setup. E cover how to set environment (.env, az login, packages), di scripts/validate-notebooks.ps1 runner, how to understand PASS/FAIL results, and which lessons ~need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

io.github.max-sixty/worktrunk/writing-user-outputs

CLI output formatting standards for worktrunk. Load before editing any code that calls warning_message, hint_message, error_message, info_message, eprintln, or println, or that produces strings the user will see (CLI help, progress UI, snapshot text). Documents ANSI color nesting rules, message patterns, and output system architecture.

io.github.simstudioai/sim/emcn-design-review

Review UI code for alignment with the emcn design system — components, tokens, patterns, and conventions

io.github.microsoft/ai-agents-for-beginners/testing-course-samples

Use when asked to validate, test, smoke-test, or run the course's notebook and code samples against a live Microsoft Foundry / Azure OpenAI configuration. Covers environment setup (.env, az login, packages), the scripts/validate-notebooks.ps1 runner, interpreting PASS/FAIL results, and which lessons need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

io.github.gibbok/typescript-book/typescript-book-review

Review and editing guide for TypeScript book and reference content in the style of The Concise TypeScript Book. Use when reviewing or editing educational prose, translated or non-English content, code examples, chapters, table-of-contents entries, or Markdown formatting in this repository to identify and fix typos, grammar mistakes, formatting problems, and minor clarity issues without altering the original meaning or the book’s concise, practical, example-driven style.

io.github.BuilderIO/agent-native/design-exploration

Use Design for UI/UX exploration, side-by-side design directions, interactive prototype previews, user selection, iteration, and design-to-code handoff through the hosted Design MCP app.

io.github.comet-ml/opik/writing-e2e-tests

Use when a developer wants to add, write, or create an end-to-end test for an Opik feature, page, or branch — e.g. "add an e2e test for the experiments comparison page", "write a test for the feature I just built", "e2e test for this branch", "cover the dataset items flow with a test". Runs the full loop in tests_end_to_end/e2e/ — analyze the feature and frontend code, explore the live UI with the Playwright MCP, write the Page Object Model + spec, and run it locally until green.

io.github.langfuse/langfuse/refactor-react-effects

Refactor avoidable React useEffect usage in Langfuse frontend code. Use when adding, reviewing, or removing effects; initializing forms or local UI state from query data; synchronizing client and server state; moving mutations or async workflows out of components; cleaning every effect from a frontend submodule; or reviewing whether an effect has a valid external-system owner.

io.github.rustfs/rustfs/security-advisory-lessons

Apply RustFS security lessons distilled from repository GitHub Security Advisories. Use when making or reviewing RustFS code changes, doing security checks, handling PR review for auth/authz, IAM, storage, RPC, logging, CORS, console/browser, encryption, policy, or endpoint changes, and when deciding which security regression tests are required.

io.github.vudovn/ag-kit/nextjs-react-expert

React and Next.js performance optimization from Vercel Engineering. Use when building React components, optimizing performance, eliminating waterfalls, reducing bundle size, reviewing code for performance issues, or implementing server/client-side optimizations.

io.github.danielmiessler/LifeOS/Apify

Scrapes social platforms, business data, and e-commerce via Apify actors — Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps, Amazon, and web crawls — filtering in code. USE WHEN scrape Instagram, scrape LinkedIn, scrape TikTok, scrape YouTube, scrape Facebook, Google Maps leads, Amazon reviews, business intelligence, multi-platform social listening, competitive analysis, lead generation, social monitoring, Apify actors, web crawl, extract contacts. NOT FOR X/Twitter operations (use _X), 4-tier progressive scraping with proxy escalation (use BrightData), or real-Chrome bot bypass and computer use (use Interceptor).

io.github.macro-inc/macro/sqlx-query-validator

Inspect Rust changes for SQLx queries. Use after modifying Rust code that adds or changes SQLx queries to ensure compile-time SQLx macros are used, run `just prepare_db` for offline query cache, and review queries for performance and security issues.

io.github.penpot/penpot/security-and-hardening

Hardens code against vulnerabilities. Use when handling user input, authentication, data storage, or external integrations. Use when building any feature that accepts untrusted data, manages user sessions, or interacts with third-party services.

io.github.joe-bell/cva/security-review

Security code review for vulnerabilities. Use when asked to "security review", "find vulnerabilities", "check for security issues", "audit security", "OWASP review", or review code for injection, XSS, authentication, authorization, cryptography issues. Provides systematic review with confidence-based reporting.

io.github.apache/shardingsphere/review-pr

Used to review whether an Apache ShardingSphere PR truly fixes the root cause, assess side effects and regression risks, and judge code correctness or mergeability from public evidence. If not mergeable, produce committer-tone change requests or needs-discussion feedback when the PR direction, root-cause model, or problem framing should be reopened. Also use for GitHub-visible PR discussion, review comments, challenged findings, and copy-ready committer replies where review correctness, mergeability, change-request validity, or root-cause evidence is being judged. Supports full-coverage ledger review for high-risk or explicitly anti-drip requests, and self-iterates before final output.

io.github.DietrichGebert/ponytail/ponytail

Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).

io.github.compozy/compozy/writing-tech-post

Authors engineering blog posts end-to-end: launch deep-dives, incident postmortems, architecture migrations, performance case studies, tutorials, AI/agent system writeups, security disclosures, and research-to-product translations. Picks the correct archetype, plans the abstraction ladder, enforces an evidence cadence (diagrams, benchmarks, profiles, traces, code, ablations), tunes voice against publisher house styles (Datadog, Vercel, GitHub, AWS, Meta, Cloudflare, Jane Street), and runs a pre-publish gate for narrative momentum and disclosure ethics. Use when drafting a new engineering post, restructuring a draft that feels flat, deciding which evidence form belongs where, validating that depth and product context are balanced, or preparing a postmortem, migration, or performance narrative for external publication. Do not use for API reference documentation, README authoring, marketing copy, release notes, generic SEO content, ghost-written executive thought leadership, or non-engineering long-form essays.

io.github.compozy/compozy/writing-skills

Author, refactor, and debug agent skills. Use when creating a new skill from scratch, pruning or restructuring a bloated SKILL.md, tightening a skill description's triggers, or diagnosing bundled references the agent ignores. Don't use for agent instruction files like CLAUDE.md or AGENTS.md (use agent-md-refactor), general documentation, or READMEs.

io.github.compozy/compozy/refactoring-analysis

Audits a codebase for refactoring opportunities using Martin Fowler's code smell and technique catalog — long functions, duplication, high coupling, complex conditionals, primitive obsession — and writes a prioritized report to docs/_refacs/. Use when auditing code quality, planning a refactoring sprint, or reviewing architectural health; not for style/formatting, performance, or security audits.

io.github.compozy/compozy/testing-boss

Testing doctrine for tests that reveal bugs instead of passing for the wrong reason — spanning software and LLM/AI systems. Use when authoring or reviewing tests, adding a mock, deciding where a test belongs, letting a coding agent generate tests, triaging flaky CI, designing an eval suite for an LLM/agent feature, or rebuilding a brittle suite. Not for general code review, library debugging unrelated to tests, CI pipeline design beyond tests, or production observability.