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Found 1,577 Skills
Provides guidance for property-based testing across multiple languages and smart contracts. Use when writing tests, reviewing code with serialization/validation/parsing patterns, designing features, or when property-based testing would provide stronger coverage than example-based tests.
Prepares codebases for security review using Trail of Bits' checklist. Helps set review goals, runs static analysis tools, increases test coverage, removes dead code, ensures accessibility, and generates documentation (flowcharts, user stories, inline comments).
Atheris is a coverage-guided Python fuzzer based on libFuzzer. Use for fuzzing pure Python code and Python C extensions.
Coverage-guided fuzzer built into LLVM for C/C++ projects. Use for fuzzing C/C++ code that can be compiled with Clang.
Ruzzy is a coverage-guided Ruby fuzzer by Trail of Bits. Use for fuzzing pure Ruby code and Ruby C extensions.
CloudBase platform knowledge and best practices. Use this skill for general CloudBase platform understanding, including storage, hosting, authentication, cloud functions, database permissions, and data models.
Manages a two-layer memory system (hot cache + cold storage) for SEO/GEO project context, tracking keywords, competitors, metrics, and campaign status with intelligent promotion/demotion.
Logic coherence pass for per-H3 section files: enforce a clear paragraph-1 thesis and surface paragraph-island risks (connector stats are diagnostic, not a quota) before merging. **Trigger**: logic polisher, section logic, thesis statement, connectors, 段落逻辑, 连接词, 论证主线, 润色逻辑. **Use when**: `sections/S*.md` exist but read like paragraph islands; you want a targeted, debuggable self-loop before `section-merger`. **Skip if**: sections are missing/thin (fix `subsection-writer` first) or evidence packs/briefs are scaffolded (fix C3/C4 first). **Network**: none. **Guardrail**: do not add new citations; do not invent facts; do not change citation keys; do not move citations across subsections.
Meta's 86M prompt injection and jailbreak detector. Filters malicious prompts and third-party data for LLM apps. 99%+ TPR, <1% FPR. Fast (<2ms GPU). Multilingual (8 languages). Deploy with HuggingFace or batch processing for RAG security.
Power systems engineering covering grid modeling, power flow analysis, energy storage dispatch, demand response, and electricity market economics. Spans transmission/distribution planning to real-time operations. Use when "power flow|load flow|grid model, energy storage|battery dispatch|ESS, demand response|load management|peak shaving, electricity market|LMP|locational marginal price, grid stability|frequency|voltage, capacity planning|resource adequacy, unit commitment|economic dispatch, transmission|distribution|power system, " mentioned.
Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.
Choose and combine Eve storage primitives to give agents persistent memory — short-term workspace, medium-term attachments and threads, long-term org docs and filesystem. Use when designing how agents remember, retrieve, and share knowledge.