Total 55,893 skills, AI & Machine Learning has 9298 skills
Showing 12 of 9298 skills
Socratic drilling — it asks, you answer, it pushes back. Does NOT give you the answer until you've earned it. Use when the user says "drill me on", "quiz me", "socratic", "test me on [subject]", or wants to study actively.
Persist learnings to memory or maintain existing memories. Triggers on "extract learnings", "save this for next time", "remember this pattern", "consolidate memories", "dream", "clean up memories".
This skill should be used when the user asks to "improve a skill", "make this skill better", "add features to a skill", "this skill is missing something", "upgrade my skill", "what's missing from this skill", "the skill doesn't do X", "make this more useful", or wants to improve skill effectiveness rather than structural correctness. Not for structural fixes — use repair-skill. Not for agents.
Autonomous research agent that reads RESEARCH.md, infers what's needed, dynamically adjusts TODOs, and delegates to the right skill. Supports opt-in BFS mode for autonomous design space search. Respects a configurable supervision policy (presets: manual / checkpointed / autonomous / wild) governing notifications, approval gates, resource limits, and idea-change handling. Proactively surfaces gaps and asks before acting. Trigger phrases: "start research", "continue project", "what's next?", "explore design space", "autoresearch".
Configure the project's supervision policy in RESEARCH.md. Uses a preset-first flow (`manual`, `checkpointed`, `autonomous`, `wild`), then lets the user adjust notification events, approval gates, stop limits, resource rules, and idea-change handling. Trigger phrases: "configure supervision", "set supervision", "automation settings", "change autonomy", "/supervision".
Scaffold the Mimas agent instruction file tree for any repository — AGENTS.md at root, subdomain CONTEXT.md files, and the full agents-docs/ hierarchy (a sibling of any existing docs/, kept separate so human-maintained project docs stay untouched). Every file is tailored to the repo's actual tech stack, git platform, and conventions. Use this skill whenever someone wants to set up agent instructions, onboard a repo for AI-assisted development, add AGENTS.md / CONTEXT.md files, create engineering docs for agents, or mentions "set up agentic repository" or "mimas template". Even if they just say "set up this repo for agents" or "add agent docs", this is the skill to use.
MCP server for querying and analyzing Facebook Ads Library data with batch processing and AI-powered video/image analysis
NPC pathfinding, enemy AI, state machines, and spawn systems for Roblox. PathfindingService usage, modifiers, waypoint handling, blocked paths. Sourced from official Roblox creator docs and production patterns.
Control video generation requests before execution. Use this when the user asks for a simple clip, storyboard video, UGC video, podcast clip, reference video, talking-head, image-to-video, text-to-video, or research-handoff video and the skill must classify the request before handing it to video-request-architect and a runner such as seedance-submitter or video-batch-runner.
使用 parallel sub-agents 为 module 生成多个 radically different interface designs。Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Elastic ML anomaly detection skill — investigation/RCA, score explanation, job operations (create, datafeed, start/stop, results), and troubleshooting (missing docs, memory limits, datafeed health, lifecycle). Operates against Kibana Agent Builder MCP tools (`ad_*`) on `.ml-anomalies-*`, `.ml-config`, `.ml-notifications-*`, `.ml-annotations-*`. Use when answering "what broke?"/"which entity?"/RCA, "why is score high/low?"/renormalization, "datafeed stopped"/"memory limit", or any request to set up or configure an ML anomaly detection job.
Bootstrap evaluators from production traces — emit SDK code, a framework-agnostic JSON spec, or publish online LLM-judge evaluators directly to Datadog. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.