Total 56,606 skills, AI & Machine Learning has 9423 skills
Showing 12 of 9423 skills
Run the evo optimization loop with parallel subagents until interrupted.
Use when starting a session, deciding which framework skill applies to the current task, or sequencing them across a feature. Maps the user's intent to one of the five framework skills (ai-driven-prd, init-claude-project, generate-dev-plan, declarative-design, execute-plan) and enforces the cross-skill operating behaviors. Triggers on "which skill should I use", "where do I start", "how do these skills fit together", "I have a PRD now what", "/using-agent-skills".
Use when the user wants to author, refine, or audit a Product Requirements Document for AI coding agents. Walks through an 8-phase pipeline (Socratic discovery → PRD draft → acceptance criteria → adversarial review → task decomposition → AI-readiness gate → test generation → handoff). Triggers on "write a PRD", "spec this feature", "draft requirements", "prepare X for Claude/Cursor/Copilot/Windsurf/Aider to build", "audit my PRD", "is this PRD AI-ready", "score this spec".
AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, Reddit sentiment, news, and multi-market screener. Supports stocks, crypto, ETFs, indices, Turkish (BIST), and Egyptian (EGX) markets.
Generate deep research reports on prediction market events using the Octagon Prediction Markets Agent. Combines real-time Kalshi market data with AI-driven analysis to surface price drivers, compare market vs. model probabilities, and identify potential mispricings across 120+ active markets.
Extract a validated learning from the current session, store it in the central agent learnings file, and sync the resulting Learnings section into the agent definitions used by the supported CLIs. User-only maintenance workflow for durable agent guidance.
Generate a periodic knowledge digest — a human-readable newsletter-style summary of what was learned, updated, and connected in your wiki over a specified period (day/week/month). Use when the user says "what did I learn this week", "give me a digest", "weekly summary", "knowledge report", "what's new in my wiki", "/wiki-digest [period]", "summarize my recent learning", or wants a readable overview of recent wiki activity. Distinct from wiki-status (which reports ingestion delta of sources) — wiki-digest summarizes *knowledge*, not sources.
Edit existing videos using AI — remix style, upscale, remove background, and add audio via fal.ai's hosted video models.
This skill analyzes meeting transcripts to extract decisions, action items, opinions, questions, and terminology using Cerebras AI (llama-3.3-70b). Use this skill when the user asks to analyze a transcript, extract action items from meetings, find decisions in conversations, build glossaries from discussions, or summarize key points from recorded meetings.
Run fact-grounded image generation batches for short-form video production, especially persona images, first-frame candidates, and light consistency edits. Use this when persona and concept inputs already exist and you need local image assets, prompt records, and reusable model-call metadata. This skill should stay anchored to benchmark-backed persona locks and should save both raw provider responses and normalized local asset manifests.
Wire a semantic layer into a nao agent so that metric queries are routed through a single source of truth. Supports dbt MetricFlow (dbt Cloud with Semantic Layer), Snowflake (views or semantic views via MCP), an in-house nao YAML semantic layer, or other tools (via MCP discovery). Installs the right MCP server, updates RULES.md to route metric queries through the semantic layer, and (for the nao YAML option) generates starter metric files. Use after a first round of tests has shown the agent struggling with metric reliability. Do not use for raw rule writing (write-context-rules) or first-time setup (setup-context).
Create, update, and maintain skills in the canonical .skills/internal/ directory. Includes step-by-step directives for agents to work with users, validate skill structure, and sync changes across agent directories. Use when users want to create new skills, update existing ones, or need guidance on skill authoring.