Stable API and interface design patterns. Use when designing REST endpoints, module boundaries, component prop interfaces, or any public contract between systems. Covers contract-first development, error semantics (RFC 9457), REST conventions, pagination, idempotency, rate limiting, and backward compatibility. For TypeScript type patterns (branded types, discriminated unions, schemas), see typescript-strict. For validation at trust boundaries, see typescript-strict.
- 📄 .gitignore
- 📄 CHANGELOG.md
- 📄 CLAUDE.md
Audit and rewrite content to remove AI writing patterns ("AI-isms"). Use this skill when asked to "remove AI-isms," "clean up AI writing," "edit writing for AI patterns," "audit writing for AI tells," or "make this sound less like AI." Supports a detection-only mode that flags patterns without rewriting.
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- 📄 SKILL.md
Review and analyze a skill against best practices for length, intent scope, and trigger patterns
REST and gRPC API design patterns for Go services. Covers HTTP handlers, middleware, routing, request/response patterns, versioning, pagination, graceful shutdown, and OpenAPI documentation. Use when designing APIs, writing HTTP handlers, implementing middleware, structuring REST endpoints, or setting up gRPC services.
Use after implementing features, before claiming a phase is complete, when reviewing AI-generated code, or when code feels overly complex. Also use when you notice repeated patterns across files, a function exceeds 40 lines, nesting exceeds 3 levels, or an abstraction has only one implementation. Covers duplication, dead code, over-engineering, and AI-specific bloat patterns like verbose error handling and redundant type checks.
Find ways to cut your Vercel bill. Run after making changes to catch cost-heavy patterns early in Next.js projects.
Transform Chinese text into Chinglish (中式英語) — English that is heavily influenced by Chinese grammar, word order, and thought patterns, producing the characteristic "中式英文" style found on signs, menus, instructions, and everyday speech. Can also take English text and re-render it through a Chinese-thinking lens to produce Chinglish. Applies a 25-item checklist covering article errors, copula dropping, topic-comment structure, verb confusion, literal calques, tense flattening, and more. Useful for humor, creative writing, language education, or demonstrating L1 transfer patterns. Triggers on "/chinglish", "寫成中式英文", "翻成中式英語", "Chinglish化", "translate to chinglish", "make it chinglish", "chinglish this", "中式英文".
- 📁 examples/
- 📄 MICROSIMULATION_REFORM_GUIDE.md
- 📄 SKILL.md
Common analysis patterns for PolicyEngine research repositories (CRFB, newsletters, dashboards, impact studies). For population-level estimates (cost, poverty, distributional impacts), use the policyengine-microsimulation skill instead. --- # PolicyEngine analysis Patterns for creating policy impact analyses, dashboards, and research using PolicyEngine. **For population-level estimates** (budgetary cost, poverty impact, distributional analysis), use the **policyengine-microsimulation** skill instead. This skill covers analysis repo patterns, visualization, and household-level calculations. See `MICROSIMULATION_REFORM_GUIDE.md` for UK-specific microsimulation patterns. ## For Users ### What are Analysis Repositories?
Designs agent-native applications where agents are first-class citizens with full tool parity, atomic primitives, and explicit completion signals. Covers tool design, context injection, agent-to-UI communication, and mobile checkpoint/resume patterns. Use when architecting an agentic system, designing tool surfaces, building agent-aware UI, implementing context.md patterns, or asking "how do I make my app agent-native.
Create a new rsactor actor with proper structure and patterns.
v1.0.27 -- Detect and fix Go error handling antipatterns across a codebase. Use when auditing error handling, fixing double-handled errors, removing log-and-return patterns, cleaning up log-and-wrap helpers, or when the user asks to analyze error handling hygiene, find error handling violations, or ensure errors are handled exactly once. Covers detection patterns, classification of true vs false positives, fix strategies for interior vs boundary code, and verification steps.
Analyze session replay patterns across experiment variants to understand user behavior differences. Use when the user wants to see how users interact with different experiment variants, identify usability issues, compare behavior patterns between control and test groups, or get qualitative insights to complement quantitative experiment results.
