3 added, 6 removed. Audit A to A.
---
name: caveman-discover
description: >
- Find every LLM workflow in the current repository and label it, so Caveman
- Cloud groups spend by what the code actually does (support-reply,
- nightly-digest) instead of one anonymous bucket. Use when the user pastes
- the Caveman discovery prompt, says "discover workflows", or asks to break
- LLM spend down by workflow. The repo should already route through the
- Caveman gateway (the caveman-setup skill does that part).
+ 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:
```
| workflow | job | where | how it gets labeled |
|---|---|---|---|
| support-reply | answers inbound tickets | src/bot/reply.ts:41 | defaultHeaders on the reply client |
| nightly-digest | 02:00 summary job | jobs/digest.ts:12 | header on the digest client |
| eval-suite (review) | scripts/eval.ts:8 — purpose inferred from filename | scripts/eval.ts:8 | env override at invocation |
```
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
```
## Workflows labeled
| workflow | job | where |
|---|---|---|
| support-reply | answers inbound tickets | src/bot/reply.ts:41 |
| nightly-digest | 02:00 summary job | jobs/digest.ts:12 |
Verified: <the labeled path you actually exercised, and what you observed>
Lands at: <DASHBOARD>/activity?tab=workflows — each row appears as that workflow
next runs. Anything still unlabeled shows as `unlabeled-workflow`.
Not wired (no gateway routing, so no label): <list or "none">
Marked review: <slugs whose purpose was inferred from filenames, or "none">
```
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.