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Found 6,689 Skills
Implement Cisco's Foundry specification for agentic AI security evaluation systems with multi-agent architecture
OpenAI Responses API for stateful agentic applications with reasoning preservation. Use for MCP integration, built-in tools, background processing, or migrating from Chat Completions.
Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathematics, scientific reasoning, drug discovery, protein design, weather modeling, theorem proving, single-cell, PDE solving, or anything similar. Hugging Science (huggingscience.co) is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces; the `hugging-science` org on Hugging Face hosts community datasets, models, and demo Spaces. This skill helps you discover the right resource AND actually use it — loading datasets via `datasets`, running models via `transformers` or the HF Inference API, calling Spaces like BoltzGen via `gradio_client`, and citing blog posts for methodology. Trigger this skill whenever a user mentions a scientific ML task, asks for "a dataset/model for X" where X is a scientific topic, wants to fine-tune on scientific data, asks about protein / molecule / genome / climate / materials / astronomy / pathology / weather ML, or needs AI tools for research — even if they never say "Hugging Science" explicitly. The catalog is purpose-built for LLM agents (it ships an `llms-full.txt`); prefer it over generic web search for these tasks.
Srcwalk is the agent's code navigator: one tree-sitter CLI for repo maps, token-aware large-file reads, symbol search, callers/callees, deps, impact checks, and precise drill-ins. Use it before raw reads or grep for code-structure work. Run `srcwalk guide` first. Must use! It is the installed binary's source of truth.
Invoke a Rubber Duck Reviewer subagent to independently critique plans and implementations before proceeding. Use when the agent is about to implement a non-trivial plan (multi-file changes, architectural decisions, security-sensitive logic, database schema changes), after completing a self-contained unit of work (module, endpoint, feature), when stuck or facing repeated failures (same test fails 2+ times, unexpected results), or when the agent wants independent validation of assumptions and design decisions. Triggers on any non-trivial implementation task where independent critique would catch blind spots before they become costly mistakes.
Use this skill whenever the user wants to work with the Loops CLI from the terminal. This includes installing or updating the CLI, authenticating, storing and selecting API keys, validating credentials, and running commands for contacts, contact properties, lists, events, transactional email, campaigns, email messages, themes, and components. Trigger on phrases like "Loops CLI", "loops auth login", "loops campaigns create", "loops email-messages update", "loops themes list", "loops components get", "loops contacts create", "loops events send", "loops transactional send", "loops api-key", "loops agent-context", "brew install loops-so/tap/loops", or any time the user wants to use Loops from the shell instead of application code.
Localization (i18n) across all CometChat UI Kit families — React, React Native, Angular, Android (V5/V6), iOS, Flutter (V5/V6). Covers CometChatLocalize.init signature differences (positional vs object), bundled languages, custom-language registration, RTL support, fallback to English, and cross-family drift risks. Cross-family — applies wherever the agent is configuring CometChat localization.
Drive the Duvo public API from the terminal via the `duvo` CLI (`@duvoai/cli`). Use when the user wants to script Duvo — managing agents, runs, cases, queues, files, skills, connections, Clarity processes, or hitting an arbitrary endpoint via `duvo api` — instead of clicking through the Duvo web UI or hand-crafting `curl` calls.
Review generated or changed production code before it ships, using Clean Code, SOLID, DRY, KISS, YAGNI, and LLM-specific failure-mode checks in any programming language. Best used reactively after an agent writes, edits, refactors, or fixes code, before presenting, committing, or merging the result. Use when the user asks "review this PR", "is this safe to merge?", "make this cleaner", "audit this code", "refactor this", "fix this bug", or after a coding agent produced implementation code. Can also guide writing when explicitly invoked before a risky edit. DO NOT USE for factual/conceptual questions, CI/tooling config, git workflow, running/debugging tests, pure architecture discussion, prose writing, data analysis, or test-code review (use test-guard).
Edit a vigiles .spec.ts to change a compiled instruction file (CLAUDE.md / AGENTS.md) — add, modify, or remove a rule, section, command, or key file. Use whenever you need to change a CLAUDE.md/AGENTS.md that carries a vigiles hash (edit the spec, never the artifact), including adding a new enforce()/check()/guidance() rule.
Survey a whole React codebase as a senior React engineer, using React Doctor's scan as evidence, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the React code", "audit this codebase", "make this app faster / more robust", or wants a roadmap of fixes rather than a review of a single diff. For a regression check or a fix-it-now pass, use the `react-doctor` skill instead.
Use when writing git commit messages for non-trivial changes — captures decision context (constraints, rejected alternatives, confidence, directives) as structured git trailers so future agents and developers can query project knowledge via git log --trailer=