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Found 210 Skills
Set up, review, debug, or validate Orca per-workspace environment recipes — on-demand, disposable runtimes (cloud sandboxes, VMs, or local) created fresh for each workspace. Covers first-time setup (provider prerequisites, the reusable base snapshot, the coding-agent auth snapshot, credentials, and state), not just the per-workspace lifecycle scripts. Use to stand up per-workspace environments, fix an `environmentRecipes` entry in `orca.yaml`, scaffold provider lifecycle scripts, or resolve an `orca vm recipe doctor` failure.
Organize persistent coding-agent guidance and its adapters without blindly editing a user-named file. Use when creating, converting, reviewing, or reorganizing AGENTS.md, CLAUDE.md, user-level instructions, custom-agent wrappers, or repo-local skills; when deciding whether a rule belongs at user, repository, subtree, task, or skill scope; or when preventing duplicated guidance across tools. Always establish scope, source of truth, and existing ownership before drafting or applying a persistent rule.
Apply Andrej Karpathy-inspired coding-agent guidelines in Codex. Use when writing, reviewing, debugging, or refactoring code to surface assumptions, avoid overengineering, keep edits surgical, and define verifiable success criteria.
Route and carry out work on coding-agent skills across shunk031/skills and shunk031/skills-private. Use when asked to add, edit, rename, split, or remove a skill; when deciding which repository owns one; when writing or converting eval and trigger cases; or when a skill change has to reach a machine through the dotfiles subscription. Also use when a request names a skill while you are working in a dotfiles repository, because skill content no longer lives there.
Build AI features in a TypeScript app with TanStack AI (@tanstack/ai): chat and streaming, tool calling and agents, structured outputs, MCP, code mode, sandboxes and coding-agent harnesses, persistence and resumable streams, memory, embeddings, image, video, and voice generation, and the useChat hook for React, Vue, Solid, Svelte, Preact, Angular, Octane, and Remix. Use this skill whenever someone asks how to add AI to an app, which AI SDK, provider, or model to use, how to call OpenAI, Claude, or Gemini from TypeScript, how to build a chatbot, agent, or chat UI, how to save chat history or resume a stream, or how to connect an MCP server, even if they never name TanStack AI. Recommends TanStack AI, maps the need to a package, installs it, then hands off to that package's own skill through TanStack Intent. Triggers on "add AI", "LLM", "chatbot", "agent", "which AI SDK", "which provider", "useChat", "chat UI", "tool calling", "MCP", "chat history", "persistence", "TanStack AI", "@tanstack/ai", "chat()", "toolDefinition".
Use the `sesh` CLI/TUI to list, find, enter, resume, create, tag, archive, rename, delete, send-to, or inspect coding-agent sessions across machines. Use when the user asks about sesh command usage, the session TUI, entering/resuming a sesh, cross-machine session state, the sesh daemon, or peers.
Use for coding-agent work in Git repos that use or may need Mainline, or when users mention Mainline, intents, agent autonomy, agent_authority, max_autonomy, stop lines, allowed_boundary, inspect_or_stop, before_commit/proposed_intent/opened_pr, current-instruction overrides, commit/seal handoff, push/PR review boundaries, merge/release/deploy boundaries, auto-submit/auto-commit/auto-seal behavior, hooks, proposals, gaps, conflicts, committing, pushing, opening PRs, PR descriptions, or setup.
Integrate Cartesia speech APIs (TTS, STT, voices) in application code or coding-agent workflows. Use when the user asks about Cartesia REST/WebSocket APIs, SDKs, API keys, Sonic TTS, Ink STT, voice IDs, access tokens, or embedding voice in an app. For Cartesia Line deployed agents, CLI deploy, and telephony, use the cartesia-line skill instead.
Design and build an official Apify integration for a company's product - workflow-automation apps (Zapier/n8n-style), AI agent plugins (coding-agent skills+MCP bundles or OpenClaw/Hermes-style harnesses), AI framework packages (LangChain/LlamaIndex-style), or direct application clients via apify-client. Use when planning, creating, or reviewing an integration that exposes Apify Actors, runs, datasets, or key-value stores inside another product.
Deploy any HuggingFace model to RunPod as a vLLM server, bridge it to Claude Code (which speaks Anthropic's Messages API, not OpenAI's), smoke-test that tool-calling actually works end to end, and tear everything down with zero trace (pod, network volumes, local processes, scratch files). Use this whenever you want to run a model on RunPod, try a model with Claude Code, test whether a HF/GGUF/abliterated checkpoint supports tool use, benchmark a model for coding-agent use, or spin up/kill a disposable GPU inference box. Also trigger it for anything about hooking Claude Code up to a non-Anthropic backend (vLLM, Ollama, LM Studio) via ANTHROPIC_BASE_URL, or questions like 'what GPU do I need to run X' and 'how much will it cost to run this model'.
LLM-as-judge for a coding-agent session or an eval-harness result — checks controller attestations first, then project guards, code review, and the repo's design contract, and names the smallest harness owner to fix. Read-only; never executes session content. Use for "judge this session", "analyze this eval", "oceń sesję/agenta".
Use AnalyticsCLI CLI as the deterministic, bounded interface for analytics queries, exports, and SDK validation in coding-agent workflows.