SKILL DETAIL
caveman-discover
juliusbrussee/caveman/caveman-discover
This skill is used to identify and label every LLM workflow in the current repository, enabling Caveman Cloud to group spend by the actual job the code performs (e.g., support-reply, nightly-digest) instead of one anonymous bucket. It is triggered when the user pastes the Caveman discovery prompt, says "discover workflows", or asks to break down LLM spend by workflow. The repository should already route through the Caveman gateway (the caveman-setup skill handles that part). The skill is operator-invoked. It inventories workflows from entry points such as HTTP/RPC handlers, scheduled jobs, CLI commands, eval/test harnesses, and distinct agents or chains within frameworks. It names each workflow using slug grammar (lowercase [a-z0-9_-], 1-96 chars), proposes a labeling table, and applies labels only after user approval. Labels are added at call sites using the lightest mechanism available (e.g., SDK options, default headers, or environment variables). It verifies that labeled requests succeed and reports the results, including any unlabeled or review-marked workflows.
Installation
npx skills add https://github.com/juliusbrussee/caveman --skill caveman-discover
Fichiers du skill
SKILL.md
Dernière synchronisation · 29 août 2026
SKILL.md›
---
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).
---
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.