Full end-to-end validation of Temporal distributed execution (Phases 2-5 + v1 routing + v2 queue options / worker-runtime profiles). Two modes: (1) pytest against a real Temporal server for detailed assertions on all test cases, and (2) true 3-process setup (server + separate worker process + submitter) that validates the actual deployment topology including cross-process serialization, LibraryCrate propagation, deferred hydration, concurrent isolation, image payload storage, and cross-worker graph tracing with GraphSpec assembly. Step 8 validates v1 per-activity routing. Step 9 validates v2 per-queue submitter options (timeouts, retry, rate-limit), per-handle option overrides, named worker-runtime profiles selected via `--profile`, and the strict `--task-queue` CLI typo check with "did you mean?" suggestion. Mode 1 also folds in the error-handling suite (activity error boundary, workflow error-report full chain, local parity arm); Mode 2 Tiers 13-16 validate that a worker-side failure — from an LLM, extract,
Bump the pinned `@pipelex/mthds-ui` (and `elkjs`) version that the generated ReactFlow HTML loads from jsDelivr. Re-fetches the bundle, recomputes SRI hashes, and updates the Python constants in `standalone_assets.py`. Use when user says "update graph ui", "bump mthds-ui", "update graph viewer", "new version of mthds-ui", or any variation of updating the CDN-pinned graph viewer assets.
Run and diagnose the LibraryCrate integration tests for Temporal. Tests that PipeSequence controllers execute on Temporal workers via LibraryCrate propagation, including concurrent isolation tests for conflicting concepts and pipes. Use when the user says 'test temporal crate', 'run crate tests', 'isolation tests', or wants to verify LibraryCrate on Temporal works.
Automates the Pipelex release workflow: bumps the version in pyproject.toml, finalizes the CHANGELOG.md Unreleased section, runs quality checks, creates a release/vX.Y.Z branch, commits, pushes, and opens a PR to main. Use when user says "release", "cut a release", "bump version", "prepare a release", "make a release", "ship it", "create release branch", or any variation of shipping a new version of pipelex. The user can optionally provide changelog content inline when invoking the skill (e.g. "/release Added new extract backend"), which will be used as the changelog entry for this version.
Add a new AI model to the Pipelex inference system. Guides through all required steps: backend TOML configuration (OpenAI, Azure, Anthropic, Google, etc.), kit sync, test profile collections, and fixture regeneration. Use when the user says "add a model", "add GPT-X", "add Claude X", "new model", "register a model", "add Gemini X", "support model X", "add model to backend", or any variation of introducing a new AI model to the inference configuration. Also use when the user mentions a model name that doesn't exist in the backend configs yet and wants to add it.
Test an AI model on a specific backend using the Pipelex inference test infrastructure. Handles test profile creation, fixture regeneration, and running the right test class for the model type (LLM, image gen, extract, search). Use when the user says "test model X", "test gpt-5.4 on openai", "test model on gateway", "run inference test for model", "try model X on backend Y", "verify model X works", or any variation of running inference tests against a specific model on a specific backend. Also use when the user mentions testing a model after adding it, or wants to verify a model works end-to-end with real API calls.