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Found 4,959 Skills
Reliable end-to-end engineering workflow for debugging, root-cause analysis, minimal patching, and verification in production codebases. Use when Codex needs to investigate a failure systematically, trace execution, test hypotheses, implement a correct fix, validate the resolution, and check for regressions before declaring the task complete.
Run the Ouroboros specification-first development loop: reduce ambiguity with a Socratic interview, freeze an immutable seed/spec, execute against that contract, verify before claiming success, and keep looping until completion is actually verified. Use when the user wants spec-first clarification, immutable requirements, drift-aware implementation, or a persistent completion loop that should keep going until tests / checks / acceptance criteria pass. Triggers on: ooo, ouroboros, interview, seed, run workflow, evaluate, evolve, ooo ralph, specification first, socratic interview, ambiguity reduction, persistent completion.
Scan a Cargo workspace or package monorepo and refresh per-member `CLAUDE.md` files plus a thin root `CLAUDE.md`. User-only maintenance workflow for keeping workspace-local AI context accurate after refactors, member additions, export changes, or major architectural shifts.
Background knowledge for droid-control workflows -- not invoked directly. True-input driver mechanics for real terminal emulator automation via headless Wayland compositor.
Background knowledge for droid-control workflows -- not invoked directly. Capture ground-truth byte sequences from real terminal emulators.
GEOFlow open-source GEO/SEO content production system with AI generation, review workflow, and publishing pipeline built on PHP and PostgreSQL.
Grafana Cloud AI and ML features — Grafana Assistant (natural language queries, dashboard generation, incident investigations), Dynamic Alerting (ML forecasting and outlier detection), Sift (automated root cause analysis with 8 analysis types), Knowledge Graph (entity discovery and RCA Workbench), and the LLM Plugin (OpenAI/Anthropic/Azure integration). Use when setting up AI-powered alerting, using natural language to query metrics/logs, automating incident investigation, or integrating LLMs with Grafana panels and workflows.
Run the /check-phoenix-duskmoon-design Claude command workflow in Codex.
This skill guides development of full-stack features on EdgeOne Pages — Edge Functions, Cloud Functions (Node.js / Go / Python runtimes), Middleware, KV Storage, and local dev workflows. It should be used when the user wants to create APIs, serverless functions, middleware, WebSocket endpoints, or full-stack features specifically on EdgeOne Pages — e.g. "create an API", "add a serverless function", "write middleware", "build a full-stack app", "add WebSocket support", "set up edge functions", "use KV storage", "create a Go API", "build a Python backend", "use Flask/FastAPI/Gin on EdgeOne Pages". Do NOT trigger for framework-native features (Next.js API routes, Next.js middleware, Nuxt server routes) or generic Express/Koa development outside an EdgeOne Pages project. Do NOT trigger for deployment — use edgeone-pages-deploy instead. Do NOT trigger for other platforms (Cloudflare Workers, Vercel Functions, AWS Lambda).
Upload screenshots and images to GitHub, returning markdown-ready URLs for PRs, issues, and comments. Use when needing to attach images to GitHub PRs/issues, upload screenshots, embed visuals in markdown, or when a workflow produces images that should be shared on GitHub. Trigger words - upload image, attach screenshot, add image to PR, embed screenshot, visual diff, before/after screenshot.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Expertise in generating clean, correct, and efficient Dataform pipeline code for BigQuery ELT. Use this when creating or modifying Dataform pipelines, actions, or source declarations, when Dataform, SQLX, or BigQuery are mentioned in a transformation, when data needs to be ingested from GCS into BigQuery via Dataform, or when setting up a new Dataform project or configuring workflow_settings.yaml.