splitty

splitty (full lifecycle)

The user has given you a goal in natural language. You will:

1. Decide whether to design a new pipeline or reuse an existing one.
2. Run it to completion, dispatching sub-agents step by step.

This skill composes splitty-design (pipeline authoring) and splitty-run (orchestration). If the user supplied a .yaml pipeline path or asked you to use one of the examples in pipelines/examples/, skip design and go straight to run.

Step 1 — locate or design the pipeline

Decision tree:

• If the user named a YAML file (or pipelines/examples/<name>.yaml): use it as-is. Validate first with:

  python3 ${CLAUDE_PLUGIN_ROOT}/scripts/splitty.py validate <path>
  

• Otherwise: invoke the splitty-design skill body (read its SKILL.md and follow the procedure) to author a new pipeline file from the user's request. Save it under .splitty/pipelines/<name>.yaml in the workspace.

State your intent in one sentence before proceeding (e.g., "Designing a classify-then-extract pipeline for your notes/ folder.").

Step 2 — run the pipeline

Invoke the splitty-run skill body (read its SKILL.md and follow the procedure exactly). Do not improvise the orchestration loop — it is deterministic by design.

Step 3 — show the result

After splitty finalize, read the result file from the path it printed and present a brief summary to the user (1-3 sentences) plus the path. Do not re-summarize the entire result; the user can read it.

Notes on scope

• This skill orchestrates one pipeline run per invocation. If the user wants multiple unrelated runs, run them serially or ask which to start.
• The orchestrator is Claude itself in the main loop. There is no daemon, no MCP server, no background process. Each Task() call you make spawns one filter sub-agent for one step.
• Sub-agents never call Task themselves. Fan-out is your job.