Total 50,615 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Integrate Polpo AI agents into any TypeScript/JavaScript application using @polpo-ai/sdk. Use when the user wants to add AI agent chat, completions API, streaming SSE, session management, memory, webhooks, or any Polpo API integration into their code. Triggers on "polpo", "agent chat", "completions API", "polpo sdk", "@polpo-ai/sdk", "AI agent integration".
Use when the workflow feels over-engineered, has premature optimizations, unnecessary abstraction layers, or complexity beyond actual requirements.
Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.
Exit copilot mode — return to autonomous mode with full worktree enforcement.
Cohere integration. Manage Documents, Models, Datasets, Jobs. Use when the user wants to interact with Cohere data.
2-layer parallel agent hierarchy. Layer 1 deploys 3-50+ agents, each with independent context. Layer 2 adds 2+ sub-agents per member. No upper limit on either layer.
Generate and edit images using Google's Gemini image models (Nano Banana 2 default, Nano Banana Pro legacy). Use when the user asks to generate, create, edit, modify, change, alter, or update images. Also use when user references an existing image file and asks to modify it in any way (e.g., "modify this image", "change the background", "replace X with Y"). Supports text-to-image, image editing with up to 14 reference images, configurable resolution (0.5K-4K), aspect ratio, and adjustable thinking. DO NOT read the image file first - use this skill directly with the --input-image parameter.
Decide when Zoom MCP is the right fit and produce a safe setup plan for Claude. Use when planning AI workflows over Zoom data, deciding between MCP and REST, or defining a hybrid MCP architecture.
Generates structured literature survey reports from collected papers using a multi-stage pipeline: outline generation (query-type adaptive) → draft survey → section-by-section expansion → summary section refinement → final assembly. Produces survey-grade output with taxonomy-based method analysis, LaTeX formalizations, comparative tables, and dense citations. Use when: user wants a literature review, research survey, field overview, or systematic synthesis of multiple papers. Do NOT use for finding/searching papers (use paper-navigator), generating research ideas (use research-ideation), or writing a paper's Related Work section (use paper-writing).
Standard Gear/Vara Sails builder pack for AI agents. Use when building or extending a Sails app on Vara or Gear. NOT for Vara.eth, ethexe, non-Sails programs, or generic protocol research.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory, or memory benchmarks (LoCoMo, LongMemEval). A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of durable agent knowledge and cross-session persistence.
This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of extending context beyond the window via filesystem strategies.