Total 50,313 skills, AI & Machine Learning has 8452 skills
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Sub-skill for the intake phase of README-first AI repo reproduction. Use when the task is specifically to scan a repository, read README and common project files, extract documented commands, classify inference or evaluation or training candidates, and return a minimum trustworthy plan to the main skill. Do not use for environment setup, asset download, command execution, final reporting, paper lookup, or end-to-end orchestration.
Sub-skill for environment and asset preparation in README-first AI repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.
Sub-skill for the execution-evidence and reporting phase of README-first AI repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files including patch notes when repository files changed. Do not use for initial repo intake, generic environment setup, paper lookup, target selection, or end-to-end orchestration by itself.
Discover, vet, and install agent skills by searching ACROSS every major registry at once — skills.sh, clawhub.ai, and GitHub — presenting each board on its own native metric (installs / stars) with the top entry per board, security-scanning the top candidates' real SKILL.md for risky patterns, and flagging what's already installed. Use when the user asks "how do I do X", "find a skill for X", "is there a skill that…", "what skill should I install for…", or wants to extend the agent with a capability that might already exist as a published skill. Unlike single-registry search, this surfaces the best of every platform side by side, so you recommend the genuinely relevant, popular, well-maintained, and SAFE one — not whatever ranked first on one site.
Decision guide for delegating to caveman-style subagents. Tells the main thread WHEN to spawn `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit), or `cavecrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is caveman-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use cavecrew", "spawn investigator/builder/reviewer", "save context", "compressed agent output".
Show real token usage and estimated savings for the current session. Reads directly from the Claude Code session log — no AI estimation. Triggers on /caveman-stats. Output is injected by the mode-tracker hook; the model itself does not compute the numbers.
Build and deploy GitHub Copilot SDK apps to Azure. USE FOR: build copilot app, create copilot app, copilot SDK, @github/copilot-sdk, scaffold copilot project, copilot-powered app, deploy copilot app, host on azure, azure model, BYOM, bring your own model, use my own model, azure openai model, DefaultAzureCredential, self-hosted model, copilot SDK service, chat app with copilot, copilot-sdk-service template, azd init copilot, CopilotClient, createSession, sendAndWait, GitHub Models API. DO NOT USE FOR: using Copilot (not building with it), Copilot Extensions, Azure Functions without Copilot, general web apps without copilot SDK, Foundry agent hosting (use microsoft-foundry skill), agent evaluation (use microsoft-foundry skill).
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
Use when executing implementation plans with independent tasks in the current session
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Generate images with Google Gemini native image models via inference.sh CLI. Models: Gemini 3 Pro Image, Gemini 2.5 Flash Image. Capabilities: text-to-image, image editing, multi-image input. Triggers: nano banana, gemini image, gemini 3 pro image, gemini 2.5 flash image, google image generation, native image generation, gemini native image