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Found 1,668 Skills
Automates the full code release pipeline — branch, commit, push, PR, wait for CI, merge, version bump, release, cleanup.
GitHub CLI operations via `gh` for issues, pull requests, CI/Actions, releases, repos, search, gists, and the REST/GraphQL API. Structured output with `--json` and `--jq` for parsing. Covers `gh issue create/list/view/edit/close`, `gh pr create/review/merge/checks`, `gh run list/view/rerun/watch`, `gh release create`, `gh search repos/issues/prs/code`, `gh api` for REST and GraphQL queries, and `gh gist` operations. Includes error handling for HTTP 401/403/404/422/429, scope troubleshooting, and rate limit management. Trigger phrases: "create an issue", "file a bug", "open a ticket", "submit a PR", "raise a pull request", "check pipeline", "view test results", "CI failing", "why did CI fail", "check CI status", "merge a PR", "manage releases", "query the GitHub API", "search repositories", "triage workflows", "automate GitHub operations". Also triggers when the user pastes a GitHub URL.
Migrate existing Python projects to uv from pip, Poetry, Pipenv, or Conda. Learn how to convert dependency files, preserve development environment setup, validate the migration, and plan team rollout. Use when converting legacy projects to modern uv tooling, consolidating different package managers, or standardizing Python development workflows across teams.
Stream Light Protocol account state via Laserstream gRPC. Covers token accounts, mint accounts, and compressible PDAs with hot/cold lifecycle tracking. Use when building custom data pipelines, aggregators, or indexers.
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Expert MLOps engineering covering model deployment, ML pipelines, model monitoring, feature stores, and infrastructure automation.
Meta-orchestrator (L0): reads kanban board, lets user pick ONE Story, drives it through pipeline 300->310->400->500 via TeamCreate. User-confirmed merge to develop after quality gate PASS.
Add custom local tools to ToolUniverse and use them alongside the 1000+ built-in tools. Use this skill when a user wants to: create their own tool for a private or custom API, add a local tool to their workspace, integrate an internal service with ToolUniverse, or use a custom tool via the MCP server or Python API. Covers both the JSON config approach (easiest, no Python needed) and the Python class approach (full control). Also covers how to verify tools loaded correctly and how to call them. Also covers the plugin package approach for reusable, shareable, pip-installable tool sets.
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Shelf framework guardrails, patterns, and best practices for AI-assisted development. Use when working with Shelf (Dart HTTP server) projects, or when the user mentions Shelf. Provides middleware patterns, request handling, pipeline composition, and server guidelines.
Orchestrate the full edge research pipeline from candidate detection through strategy design, review, revision, and export. Use when coordinating multi-stage edge research workflows end-to-end.