skills/caveman-setup/SKILL.md
Wire a repository through the Caveman Cloud gateway so every LLM request is measured, with no behavior change. Use for "set up caveman" or adding LLM spend observability.
npx skillsauth add JuliusBrussee/caveman caveman-setupInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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You are wiring this repository through the Caveman gateway. Caveman is a byte-preserving LLM proxy: in record mode it measures what your app sends and what it costs, and changes nothing else. Your job is a minimal, verified integration — not a refactor.
The prompt that sent you here provides four values. Refer to them as:
GATEWAY — the gateway base URL (e.g. https://gateway.caveman.so or http://127.0.0.1:8787)CAVE_API_KEY — the gateway auth secret (treat like any API key: env var only, never committed, never printed in full)PROVIDER_KEYS — stored (provider keys live encrypted in Caveman Cloud) or byok (this app sends its own provider key per request)DASHBOARD — the dashboard base URL (e.g. https://app.caveman.so)If any value is missing, stop and ask for it. Do not guess a URL or mint a key.
CAVE_API_KEY goes into the env file the repo
already uses (.env, .env.local, …). If that file isn't gitignored, add it
to .gitignore and say so. Never hardcode the key in source.PROVIDER_KEYS: stored you
never see one. With byok, the app's existing provider key stays exactly
where it already is.Read dependency files (package.json, requirements.txt, pyproject.toml,
go.mod, lockfiles) and search the source for LLM clients:
openai, @anthropic-ai/sdk, anthropic, ai +
@ai-sdk/* (Vercel), langchain*, litellm, google-genai /
@google/genai, crewai, pydantic_ai, openai-agents / agentsapi.openai.com, api.anthropic.com, generativelanguage.googleapis.comOPENAI_BASE_URL, OPENAI_API_BASE,
ANTHROPIC_BASE_URL, GEMINI_BASE_URL, GOOGLE_GEMINI_BASE_URLList what you found (file:line per callsite) before changing anything. If you find no LLM callsites, stop and report the "nothing to wire" template at the end of this file — do not invent an integration.
One slug names this app in the gateway path: GATEWAY/w/<app>. Derive it from
the package/module name (e.g. support-bot, acme-api). Grammar:
lowercase [a-z0-9] first, then [a-z0-9._-], max 64 chars. Spend for this
whole app groups under that slug on the dashboard.
The pattern is always the same: base URL → the gateway with /w/<app>,
plus one auth header. Gateway auth is x-cave-api-key: CAVE_API_KEY
(Authorization: Bearer CAVE_API_KEY also works where a header is awkward).
With PROVIDER_KEYS: byok, also send x-cave-upstream-key: <the provider key the app already uses>.
Two facts that make the wiring safe (both are gateway-enforced, not hopes):
the gateway rebuilds upstream auth headers from scratch, so a client's
Authorization/x-api-key value is never forwarded to the provider; and with
stored, upstream auth comes from the encrypted connection server-side. So in
stored mode, where an SDK insists on an api-key parameter, set it to the
Cave key — it authenticates the gateway and goes no further.
Exact shapes (use the one matching each callsite — these are the product's published recipes, not suggestions):
OpenAI SDK (TS) — Chat Completions and Responses both route through:
const client = new OpenAI({
baseURL: `${process.env.CAVE_GATEWAY_URL}/w/<app>/openai/v1`,
apiKey: process.env.OPENAI_API_KEY, // byok: unchanged · stored: use CAVE_API_KEY
defaultHeaders: {
"x-cave-api-key": process.env.CAVE_API_KEY!,
// byok only:
"x-cave-upstream-key": process.env.OPENAI_API_KEY!,
},
});
OpenAI SDK (Python) — same shape: base_url=f"{gw}/w/<app>/openai/v1",
default_headers={"x-cave-api-key": ..., "x-cave-upstream-key": ...}.
Anthropic SDK (TS/Python) — the SDK appends /v1/messages itself. The
x-cave-api-key header is required here in both modes (this SDK's own key
param rides x-api-key, which is not a gateway-auth header):
client = anthropic.Anthropic(
base_url=f"{os.environ['CAVE_GATEWAY_URL']}/w/<app>",
api_key=os.environ["ANTHROPIC_API_KEY"], # byok: unchanged · stored: use CAVE_API_KEY
default_headers={
"x-cave-api-key": os.environ["CAVE_API_KEY"],
# byok only:
"x-cave-upstream-key": os.environ["ANTHROPIC_API_KEY"],
},
)
Vercel AI SDK — createOpenAICompatible({ baseURL: ${gw}/w/<app>/openai/v1, headers: { "x-cave-api-key": ... } }); Anthropic models via
createAnthropic({ baseURL: ${gw}/w/<app>/v1, headers: { ... } }).
LangChain / LangGraph — ChatOpenAI(base_url=f"{gw}/w/<app>/openai/v1", default_headers={...}); ChatAnthropic(base_url=f"{gw}/w/<app>", default_headers={...}). LangGraph inherits whatever model you pass it.
LiteLLM — per call api_base=f"{gw}/w/<app>/openai/v1" +
extra_headers={...}, or fleet-wide in the LiteLLM proxy config.yaml.
Raw HTTP / anything else — swap the host, keep the provider's native path:
GATEWAY/w/<app>/v1/chat/completions (OpenAI protocol) or
GATEWAY/w/<app>/v1/messages (Anthropic protocol), add the header(s).
Concretely, with slug support-bot and the hosted gateway, an OpenAI-SDK base
URL reads https://gateway.caveman.so/w/support-bot/openai/v1. And in stored
mode, drop every x-cave-upstream-key line entirely — it is byok-only.
For frameworks not listed (google-genai, crewai, pydantic-ai, openai-agents),
fetch the matching page under <docs origin>/docs/integrations/ — same origin
this skill came from — and follow it.
Add to the repo's env file (and reference from code — no literals):
CAVE_GATEWAY_URL=<GATEWAY>
CAVE_API_KEY=<CAVE_API_KEY>
The user pasted the setup prompt to authorize exactly this: one small verification request. Send it now — do not pause to ask permission for it. An integration that ends unverified because you hesitated is a worse outcome than one tiny request; finishing the verification and the report autonomously is the point of this skill.
Send one minimal request through the wiring you just built — the app's own
cheapest path if it has a script for it, otherwise curl on the path matching
the protocol you just wired with the app's own model and a small cap
(max_tokens ≤ 32):
# OpenAI-protocol wiring:
curl -sS "$CAVE_GATEWAY_URL/w/<app>/v1/chat/completions" \
-H "x-cave-api-key: $CAVE_API_KEY" \
-H "content-type: application/json" \
-d '{"model":"<model the repo already uses>","max_tokens":16,"messages":[{"role":"user","content":"ping"}]}'
# Anthropic-protocol wiring:
curl -sS "$CAVE_GATEWAY_URL/w/<app>/v1/messages" \
-H "x-cave-api-key: $CAVE_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "content-type: application/json" \
-d '{"model":"<model the repo already uses>","max_tokens":16,"messages":[{"role":"user","content":"ping"}]}'
(byok: add -H "x-cave-upstream-key: $PROVIDER_KEY".) This is one real,
billable provider request — that is the point: real traffic, real measurement.
Read the response. Success = HTTP 200 with a usage block. Anything else =
the matching failure template below.
End with exactly this shape, values filled from what you actually did and saw:
## Caveman is live in this repo
Wired: <n> callsite(s) in <n> file(s)
- <file> — <one-line what changed>
App slug: <app> — spend for this app groups under it
Verified: HTTP 200 · model <model> · <in> in / <out> out tokens (one real request)
Mode: record — measured only. No model-visible bytes changed, no optimization
enabled. Verified savings are $0 until you turn an optimizer on and it passes
its eval gate. That honesty is the product.
See the dollars: <DASHBOARD>/traces — your request is the top row, priced from
the public catalog. <DASHBOARD>/getting-started flips to "First request received."
Want spend split by workflow (e.g. support-reply vs nightly-digest), not just
by app? Say "discover workflows" — I'll fetch <docs origin>/docs/discover-workflows.md
and label every callsite by the job it does.
caveman wrap <agent> instead — see
<DASHBOARD>/getting-started."Never report success on any of these. An unverified integration is reported as unverified.
testing
Turn a Caveman optimization observation into an operator-chosen candidate with a paired baseline evaluation. Use when asked to inspect or evaluate a Caveman optimization report. Needs explicit approval.
devops
Inspect Caveman Cloud's experiment lifecycle and block unsafe execution. Use when asked to start, approve, cancel, promote or roll back a Caveman experiment.
data-ai
Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.
testing
Read-only repository explorer for cold-start orientation, broad cross-file localization, or when a direct search failed. Skip it when the exact file or symbol is already named. Returns path:line citations only; its reads stay out of main context.