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Found 4,655 Skills
Amend a published CLI from one of two input sources: (1) dogfood mode mines the active Claude Code session transcript for friction (missing flags, hand- rolled API payloads, silent-null returns); (2) direct-input mode accepts user-supplied asks (rename a command, add commands or feeds, fix a named bug, optionally sniff the source site for new endpoints). Confirms scope with the user, plans + executes the fix autonomously, scrubs PII, and opens a PR against mvanhorn/printing-press-library. Two user-in-loop checkpoints: scope after capture, PR draft before open. Trigger phrases: "amend the CLI", "submit a patch", "fix what I just dogfooded", "open a PR for this CLI", "patch this CLI", "add features to my CLI", "rename this command", "add these feeds to <cli>", "sniff for new APIs in <cli>", "amend with these ideas", "use printing-press-amend", "run printing-press-amend".
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation, and full control over Pinecone resources.
Guide for configuring Infisical Secret Syncs to push secrets from Infisical to third-party services. Covers 38+ sync destinations including AWS Secrets Manager, GCP Secret Manager, Azure Key Vault, GitHub, Vercel, HashiCorp Vault, Cloudflare, and more. Use this skill when someone asks about: syncing secrets to AWS/GCP/Azure, pushing secrets to GitHub Actions, Vercel environment variables, secret sync setup, App Connections, mapping behavior, key schemas, or 'how do I get my Infisical secrets into [service]'.
Guides product management for human data platforms—annotation and labeling products, workforce workflows, task design, quality systems (gold sets, adjudication, inter-annotator agreement), customer ML-team project delivery, contributor experience, and privacy-safe handling of human-generated training data. Use when prioritizing roadmap for labeling/RLHF/eval data platforms, writing PRDs for annotation or QA features, defining success metrics for throughput and quality, scoping enterprise customer workflows, or balancing cost-quality-speed tradeoffs—not for hands-on model training (data-scientist), warehouse/analytics pipelines (data-warehouse-engineer), generic BRD workshops without product lens (business-analyst), AI solution architecture for copilots (applied-ai-architect-commercial-enterprise), or control implementation for audits (compliance-engineer). UX flows: product-designer. Eval harnesses: prompt-engineer-agent-prompts-evals. Pricing/packaging for platform: product-management-monetization.
SDFormat/SDF model and world generation, validation, and simulator handoff. Use for `.sdf` files, SDFormat XML, Python `gen_sdf()` sources, models, worlds, links, joints, poses, frames, inertials, visual/collision geometry, mesh URIs, sensors, lights, physics, plugins, includes, Gazebo, CAD Explorer static SDF review, or simulator-specific metadata. Do not use for signed-distance-field geometry.
MoveIt2 SRDF generation, validation, and planning-semantics workflow. Use when creating, editing, regenerating, inspecting, or validating `.srdf` files, `gen_srdf()` sources, MoveIt planning groups, virtual joints, passive joints, end effectors, group states, disabled collisions, URDF-linked planning semantics, or SRDF handoff to CAD Explorer review. Use the URDF skill for robot structure, the SDF skill for simulator descriptions, and the render skill for rendering, Explorer links, and optional MoveIt2 controls.
Use when an SGLang, vLLM, or TensorRT-LLM serving/model optimization task needs prior model-family PR evidence. Query and read the PR-driven history docs under model-pr-optimization-history before choosing source paths, fast paths, kernel/fusion ideas, regression risks, or validation lanes.
Generate a branded slide-by-slide LinkedIn carousel using Gemini. Takes source content, builds a design brief, waits for approval, then outputs per-slide image generation prompts. 1080x1350 vertical format. Use this skill whenever the user says "carousel", "build a carousel", "turn this into a carousel", "gemini carousel", or wants multi-slide LinkedIn content. Always includes an approval gate between brief and image generation.
Audio implementation for Roblox. Both legacy Sound objects and the newer modular audio system (AudioPlayer, AudioEmitter, Wire). Positional audio, music, SFX, SoundGroups, dynamic effects. Sourced from official Roblox creator docs.
Find working Deepgram integration examples with third-party platforms and frameworks. Use whenever someone wants to integrate Deepgram with Twilio, LiveKit, LangChain, Vercel AI SDK, Discord, Vonage, Pipecat, Expo, FastAPI, Cloudflare Workers, Slack, Telegram, LlamaIndex, Zoom, Next.js, Nuxt, Django, SvelteKit, NestJS, Spring Boot, CrewAI, Riverside, SignalWire, and more. Examples are full runnable integration demos, not minimal feature snippets.
Lets end users add, authenticate, and manage MCP servers from the browser in assistant-ui apps with @assistant-ui/react-mcp. Use when building user-managed MCP server UIs: mounting McpManagerResource via useAui({ mcp }), declaring presets with defineConnector, dropping in McpConfigDialog, or composing McpManagerPrimitive (Root, Connectors, CustomServers, AddCustomTrigger), McpServerPrimitive (Root, Name, Icon, Status, ConnectButton, DisconnectButton, OAuthLink, RemoveButton, Error), and McpAddFormPrimitive (NameField, UrlField, AuthSelect, AuthFields, Submit, Cancel). Covers auth modes none/bearer/oauth, the OAuth flow with McpOAuthCallback, connection states, storage via McpLocalStorage/McpMemoryStorage/McpCustomStorage, reading state with useAuiState (s.mcp, s.mcpServer), and imperative addCustomServer/connect/callTool. Distinct from developer-defined backend @ai-sdk/mcp tools in the tools skill. Reach for this when connected-server tools are missing, OAuth never completes, or servers do not persist.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing academic-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.