Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

You are labeling this repository's LLM workflows for Caveman Cloud. A workflow is a job the code performs — "answer a support ticket", "build the nightly digest", "run the eval suite" — not a technology. Every gateway request can carry a workflow label; unlabeled traffic all lands in one unlabeled workflow bucket. Your job: find the workflows, name them well, wire the labels, and verify nothing broke. This changes code, so it goes through the user's normal review: propose the table first, apply after the user agrees. Re running on an already labeled repo must change nothing (idempotent). This skill is operator invoked. An unlabeled traffic Cave Plan observation is review only and does not create an advisory file, proposal, or Draft PR. Do not infer that telemetry selected a callsite or authorized an edit. Independently inventory the repository, present the labeling table, and wait for the user's approval before changing code. Step 1 — Inventory the workflows Walk the repo from its entry points, not from its imports: HTTP/RPC handlers that call an LLM (directly or through layers) Scheduled jobs: cron definitions, queue consumers, workers, GitHub Actions that invoke LLM code CLI commands and scripts ( scripts/ , bin/ , package.json scripts) Eval / test harnesses that burn real tokens Distinct agents or chains inside a framework (each LangGraph graph, each crew, each agent definition is usually its own workflow) One workflow = one job a human would name. Ten callsites inside the same request handler are one workflow; one shared llm.ts helper used by three jobs is three workflows (label at the callers, never the shared helper). Step 2 — Name them Slug grammar (the gateway enforces this): lowercase [a z0 9 ] , 1–96 chars. Name the job, not the tech: Good: support reply , nightly digest , pr review , eval suite , onboarding email Bad: openai calls (tech), main (says nothing), SupportReply (invalid), johns test 3 (won't age) Names are forever ish — renaming later splits the spend history. When a job's purpose isn't clear from the code, derive the slug from the file name and mark it review in the table rather than inventing a purpose. Step 3 — Propose, then apply Present this table and ask to proceed: Then wire each label with the lightest mechanism available at that callsite: @caveman ai/sdk / caveman cloud SDK : per trace workflow option, or defaultWorkflow on the client a single job service constructs. Raw provider SDKs (OpenAI/Anthropic/LangChain/LiteLLM/Vercel): add "x cave workflow": "<slug " to the same defaultHeaders / default headers / extra headers block that already carries x cave api key . Shared client used by several jobs → pass the header per call (every SDK above accepts per request header overrides), or give each job its own thin client. Wrapped coding agents ( caveman wrap ): workflow <slug flag or CAVE WORKFLOW=<slug env at the invocation site (cron line, CI step). Raw HTTP : add the x cave workflow header to the request. Label the callers, keep the diff minimal, match the repo's style. If a callsite is not routed through the Caveman gateway at all, don't label it — list it under "not wired" in the report (labels only travel on gateway traffic; wiring is the caveman setup skill's job). Step 4 — Verify Run whatever the repo already uses to exercise one labeled path (a test, a dev script, one curl). Then confirm: the request still succeeds (the gateway rejects an invalid label with 400 cave invalid request header — fix the slug if so). Labeled spend appears on the dashboard at /activity?tab=workflows as each workflow next runs; jobs on a schedule show up when the schedule fires, and that's worth saying in the report rather than pretending they're live. Step 5 — Report If you found no LLM entry points at all: say exactly that, and point at the setup skill ( <docs origin /docs/agent setup.md ) instead of manufacturing a table.

caveman-discover