Run local CI via the in-tree dev build of agent-ci (`pnpm agent-ci-dev`) to verify changes to this repo before completing work. Runs `pnpm agent-ci-dev run --all` in the background, watches the log for step failures, and retries failed runners after fixes. Use before reporting work as complete, or whenever the user asks to validate, run CI, or check that changes pass. Distinct from the published `agent-ci` skill, which targets downstream users via `npx @redwoodjs/agent-ci`.
Run vet immediately after ANY logical unit of code changes. Do not batch your changes, do not wait to be asked to run vet, make sure you are proactive.
Set up and run an autonomous experiment loop for any optimization target. Use when asked to start autoresearch or run experiments.
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BMad Autonomous Development — orchestrates parallel story implementation pipelines. Builds a dependency graph, updates PR status from GitHub, picks stories from the backlog, and runs each through create → dev → review → PR in parallel — each story isolated in its own git worktree — using dedicated subagents with fresh context windows. Loops through the entire sprint plan in batches, with optional epic retrospective. Use when the user says "run BAD", "start autonomous development", "automate the sprint", "run the pipeline", "kick off the sprint", or "start the dev pipeline". Run /bad setup or /bad configure to install and configure the module.
run brew upgrade
Drive the skvm CLI on behalf of a user to profile models, AOT-compile skills, run skill-assisted tasks, run benchmarks, and manage compiled proposals. Trigger when the user asks to "profile", "aot-compile", "bench", "run a skill", or asks about skvm proposals.
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Orchestrate event description audits by delegating chunk work to the event-descriptions-worker subagent. Resolve a project name to projectId via get_context when needed, then spawn worker subagents over cursors for a bounded run window and write outputs into run-scoped directories. Use when auditing missing event descriptions at scale without doing per-event analysis directly in this skill. --- # Event Description Generator Run this skill as an **orchestrator only**. Do not perform per-event filtering, repo search, or description-writing logic in this skill body. Delegate chunk processing to the `event-descriptions-worker` subagent. ## Workflow 1. Resolve project input (`projectId` or project name) 2. Create a run ID with short git SHA (`<projectId>-<sha>`) 3. Create run directories under `runs/` 4. Determine cursor plan (`cursorStart`, `maxEvents`, chunk size) 5. Spawn `event-descriptions-worker` subagents for cursor chunks 6. Collect worker summaries + output paths 7. Compress the run into a single CSV 8. Report concise progress and next cursor ## Execution Rules - Keep this skill as a **dispatcher**; the worker does the heavy lifting. - Do not call `set_event_metadata` from this skill. - Do not manually re-implement worker filtering/search logic here. - Preserve user control over scope (project, cursor range, chunk size, parallelism). ## Prerequisites
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Run agent definitions as sub-agents. Use when the user names an agent or sub-agent to run, references an agent definition, or delegates a task to an agent.
Run ArduPilot SITL autotests (integration/behavior tests). Use when the user asks to run autotests, vehicle tests, or specific test methods.
Run ML training, LLM inference, and ComfyUI workflows on remote NVIDIA GPUs (A100, H100, RTX 4090). Cloud GPU compute with smart file sync — prefix any command with 'gpu' to run it remotely.
