caveman-discover

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha ee5548342062 · 29 Oct 2026Report an issue or request a re-scan

• Publisherjuliusbrussee GitHub userOwner of the repository this skill is read from
• Source folderjuliusbrussee/caveman/skills/caveman-discoverThe exact folder we audited
• LicenceApache-2.0As the repository states it
• Commit pinned for this verdictee5548342062A new audit runs when the source changes
• Repository statusNot archivedPushed today
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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.

---
name: caveman-discover
description: >
  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.

Excerpt, first 5,247 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/juliusbrussee/caveman
git -C caveman checkout ee5548342062

Works in

1. 2026-10-29
   ✓SAFE B 89 / 100

   first audit · source ee5548342062

Questions about caveman-discover

• github.com/juliusbrussee/caveman/tree/main/skills/caveman-discov — the audited source at commit ee5548342062 and every finding aboveObserved · 29 Oct 2026

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