Total 44,203 skills, AI & Machine Learning has 7030 skills
Showing 12 of 7030 skills
Adversarial stress-test of a /think intelligence brief. Reads the think output markdown, then deploys 5-7 of the same analytical frameworks — but each one is hunting exclusively for reasons the recommendation is wrong, the conviction is unearned, and the idea will fail. Every framework becomes a prosecutor, not a judge. Surfaces the strongest kill shots, identifies which parts of the original brief are load-bearing but unverified, and produces a Red Team Report with a survival verdict. Use when the user says "red-team this", "attack this", "poke holes", "steel-man the opposition", "why is this a bad idea", "/red-team", or presents a /think brief they want stress-tested.
Connect AI coding agents (Claude Code, Cursor, VS Code, OpenAI Codex) to Grafana Cloud via the Model Context Protocol (MCP) server. Use when the user asks to connect Claude Code to Grafana, set up MCP for Grafana, use Grafana tools in Cursor, query Grafana from an AI agent, configure the Grafana MCP server, or make AI agents interact with Grafana Cloud APIs. Triggers on phrases like "MCP server", "connect Claude Code to Grafana", "Grafana MCP", "AI agent Grafana", "Claude Grafana tools", "Cursor Grafana", or "agent observability".
Create validated LLM-as-a-Judge evaluators following best practices — binary Pass/Fail judges with TPR/TNR validation for measuring specific failure modes. Use when you need to automate quality checks, build guardrails, or measure a specific failure mode identified during trace analysis. Do NOT use when failures are fixable with prompt changes (use optimize-prompt) or when failure modes are unknown (use analyze-trace-failures first).
Set up orq.ai observability for LLM applications. Use when setting up tracing, adding the AI Router proxy, integrating OpenTelemetry, auditing existing instrumentation, or enriching traces with metadata.
Analyze and optimize system prompts using a structured prompting guidelines framework — AI-powered analysis and rewriting. Use when a prompt needs improvement, experiment results show quality gaps, or you want a structured review of an existing system prompt. Do NOT use when production traces show failures (use analyze-trace-failures first to identify patterns). Do NOT use to build evaluators (use build-evaluator).
Generate and curate evaluation datasets — structured generation via dimensions-tuples-NL, quick from description, expansion from existing data, plus dataset maintenance through deduplication, rebalancing, and gap-filling. Use when creating eval data, expanding test coverage, or cleaning datasets. Do NOT use when sufficient real production data exists (use analyze-trace-failures instead). Do NOT use for evaluator creation (use build-evaluator).
Append project fragment knowledge that is "too short to warrant a separate file but needs to be known by AI every time" to fixed sections of AGENTS.md / CLAUDE.md — such as special compilation flags, services that must be started before running, path pitfalls, command aliases, and environment variable conventions. Triggers: When the user says "make a note", "add to AGENTS", "save to CLAUDE.md", "the project requires X to compile", "must do Y every time from now on", or just encountered a project-specific setting that can be explained in one sentence.
Generates blog post thumbnail images for Orbitant following the brand's visual identity, using Google's Imagen API (Nano Banana 2). Activates when creating blog images, generating thumbnails, designing featured images for articles, or when someone needs a visual for an Orbitant insight/blog post. Use this skill even if the user just says "I need an image for this article", "create a thumbnail", "generate a hero image", or "make a featured image". Also triggers when the user mentions "Nano Banana 2", "image generation", or asks for a prompt for an AI image tool.
Interview the user and inspect coding-agent skill trigger counts to recommend unused K-skills for removal.
Novel Cover Generation. Automatically analyze the genre style based on the book title and author's name, call GPT-Image-2 to directly generate a professional web novel cover with title and signature. Trigger methods: /story-cover, /封面, "Help me make a cover", "Generate cover image", "Make a novel cover", "Cover design"
Extract entities and relations from source files to build a knowledge graph
Standalone multi-agent image generation skill for Hermes. Includes an internal design compiler, GPT-Image-2 generation via apimart.ai, case library reuse, interactive reference selection, batch workflows, and style-consistent series generation.