Total 54,046 skills, AI & Machine Learning has 8989 skills
Showing 12 of 8989 skills
Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.
Use when someone asks to enhance an image, generate AI images, remove background, improve image quality, or create product shots. Also use when the user mentions 'AI image generation,' 'generate an image,' 'enhance my photo,' 'remove background,' 'improve image quality,' 'make this image better,' 'product shot enhancement,' 'generate background,' 'image enhancement,' 'AI photo,' or 'touch up my image.' Uses Sivi's generate API to create or enhance images using AI models. For uploading existing local files, see brand-assets. For generating designs from prompts, see generate-design.
Design NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A* pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the user mentions state machine, behavior tree, blackboard, A*, navmesh, seek, or patrol/chase.
Compatibility router for LangWatch evaluation requests. Use only when the user asks for evaluations without making it clear whether they mean pre-deployment experiments or production online evaluations. Routes the request to the focused companion skill and does not implement either workflow itself.
Create, modify, run, inspect, analyze, and report Python experiments that use liblaf.cherries. Use when Codex needs to work under exp/YYYY/mm/dd/group-name/, write or edit numbered scripts in src/, run them with CHERRIES_NAME and CHERRIES_TAGS, inspect Cherries/Comet logs and generated assets, or write Markdown reports in docs/.
Use when generating Claude Code enforcement hooks from a repo's docs/ structure.
Use when generating agent-native onboarding docs, coverage maps, health baselines, and agent adapters for a repo.
Use when the user wants to measure or set up evals/checks for one of their skills — how fast it is, whether its output is valid, whether it fires when expected, or whether its opening classification/routing gate labels inputs correctly.
Audit how agent context (CLAUDE.md / AGENTS.md / rules / skills) lines up with the code across a set of repositories and generate a self-contained HTML report — a short list of specific "things to check" (context behind the code, thin coverage for the codebase, oversized files, no per-area context), plus per-repo raw metrics and a folder tree comparing folder LOC to context coverage. Use when the user wants to audit context coverage across repos, "which repos are missing CLAUDE.md", "where is our agent context thin or stale", "context coverage across my org / projects folder", or "/context-coverage". Works on a local folder of clones or a whole GitHub org via the gh CLI.
Chinese translation of Google's Agentic Design Patterns book - 21 core AI agent patterns with examples
Build and deploy autonomous AI agents with CowAgent - planning, memory, knowledge base, skills, and multi-channel support
12 research methodology skills. Trigger: study design, methodology selection, scientific reasoning, mentoring. Design: rigorous methods frameworks covering qualitative, quantitative, and mixed approaches.