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Found 6,283 Skills
A specialized skill for Gemini CLI that provides high-performance, fail-fast monitoring of GitHub Actions workflows and automated local verification of CI failures. It handles run discovery automatically—simply provide the branch name.
Agent-native CLI for Exa web search and content retrieval workflows.
Inspect, operate, and troubleshoot ERDA runtimes through erda-cli-backed workflows. Use when users need help with runtime status, deployment behavior, logs, scaling, restarts, or environment-oriented ERDA troubleshooting.
Use Birdeye MCP through UXC for token market data, trending and discovery workflows, price monitoring, and DEX-related reads with help-first live tool discovery and API-key auth.
Use Crypto.com MCP through UXC for exchange market data workflows with help-first discovery and read-only guardrails.
Use Gate MCP through UXC for public spot and futures market data workflows with a fixed streamable-http endpoint and read-first guardrails.
Operate MEXC Spot REST APIs through UXC with a curated OpenAPI schema, HMAC query signing, and separate public/signed workflow guardrails.
The root entry of the CodeStable workflow family — introduces the overall system to users and routes users' specific requests to the correct cs-* sub-skills. Trigger scenarios: users only input `cs` / `/cs`, say "introduce codestable", "do something with codestable", "I want to do X, which skill should I use", "don't know which one to use", or users' described requests are open-ended (e.g., "start working") and haven't converged to a specific sub-skill. This skill itself **does not perform actual tasks** — it doesn't write specs, write code, or read/write content products in the codestable/ directory — it only performs scanning, routing, prompting, and then transfers control to the target sub-skill.
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.
Cleft Notes platform help — Apple-native AI voice-to-notes app with on-device transcription that turns spoken thoughts into organized markdown notes with auto-headings. Use when setting up Cleft Notes for capturing voice memos and converting rambling thoughts into structured notes, configuring Obsidian or Notion sync to route Cleft notes into an existing knowledge base, troubleshooting recordings that fail after a couple minutes or produce garbled transcription output, setting up Zapier automations to send Cleft notes to project management or CRM tools, choosing between Cleft free and Plus plans, deciding whether Cleft or Voicenotes or AudioPen fits your voice capture workflow, or evaluating Cleft for ADHD-friendly voice-first note-taking on Apple devices. Do NOT use for comparing AI meeting note-takers across platforms (use /sales-note-taker) or reviewing a sales call for coaching (use /sales-call-review).
Remote cloud workflow with explicit fqdn selection and deployment verification.
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.