caveman-setup
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.
Install / Use
npx skills add JuliusBrussee/caveman --skill caveman-setupInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AI & Machine LearningSupported Platforms
Tags
Our assessment of caveman-setup
caveman-setup scores 99/100 on our quality scale, 10th of 554 AI & Machine Learning skills we index (top 2%).
Its SKILL.md is 10 KB long, well organised into 10 sections with 5 code examples: a thorough specification that gives an agent plenty to work with.
With 107,653 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated today, so caveman-setup is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
ReviewOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review judged it risky: The skill instructs the agent to reconfigure every LLM callsite to route requests and API keys through a user-supplied gateway base URL, redirecting provider API traffic through a proxy.
AI review: risky
- The skill instructs the agent to reconfigure every LLM callsite to route requests and API keys through a user-supplied gateway base URL, redirecting provider API traffic through a proxy.
- It tells the agent to send a real, billable curl request using CAVE_API_KEY (and provider keys in byok mode) without pausing to ask permission.
- Although secrets are kept in env vars, the integration inherently forwards sensitive provider keys and request data to a third-party gateway, creating exposure if the gateway value is attacker-controlled.
AI review by kimi-k2.7-code on 2026-09-25. Automated pattern scan on 2026-09-24. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
caveman-setup compared with similar skills
All 4 of these similar skills score higher than caveman-setup; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| caveman-setup (this skill)by JuliusBrussee | 99 | 107.7k | today | SKILL.md |
| claude-memby thedotmack | 100 | 94.6k | today | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 84.1k | 12d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.7k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.1k | today | CLAUDE.md |
Frequently asked questions
- How do I install caveman-setup?
- Run
npx skills add JuliusBrussee/caveman --skill caveman-setup. The install tabs above show the steps for each supported agent. - Which AI agents does caveman-setup work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is caveman-setup safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review judged it risky: The skill instructs the agent to reconfigure every LLM callsite to route requests and API keys through a user-supplied gateway base URL, redirecting provider API traffic through a proxy. It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is caveman-setup still maintained?
- The repository was last updated today, so caveman-setup is actively maintained.
Skill content
View source on GitHubname: caveman-setup description: > 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.
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.soorhttp://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) orbyok(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.
Rules (non-negotiable)
- Coherent integration. Wire every live LLM callsite through existing configuration and responsible seams. Touch each layer correctness requires. No drive-by refactors or formatting sweeps; add an abstraction only when it clarifies ownership or lowers lifecycle cost.
- Secrets stay in env vars.
CAVE_API_KEYgoes into the env file the repo already uses (.env,.env.local, …). If that file isn't gitignored, add it to.gitignoreand say so. Never hardcode the key in source. - Report only what you observed. The final report states the HTTP status and usage numbers from the real verification response — never assumed success. If verification fails, report the failure template instead.
- Record mode only. You are adding measurement. You do not enable any optimization, and you do not claim any savings — verified savings are $0 until an optimizer is explicitly turned on and passes its eval gate.
- Provider keys are not your business. With
PROVIDER_KEYS: storedyou never see one. Withbyok, the app's existing provider key stays exactly where it already is.
Step 1 — Find every live LLM callsite
Read dependency files (package.json, requirements.txt, pyproject.toml,
go.mod, lockfiles) and search the source for LLM clients:
- SDK imports:
openai,@anthropic-ai/sdk,anthropic,ai+@ai-sdk/*(Vercel),langchain*,litellm,google-genai/@google/genai,crewai,pydantic_ai,openai-agents/agents - Raw HTTP to
api.openai.com,api.anthropic.com,generativelanguage.googleapis.com - Existing base-URL env vars:
OPENAI_BASE_URL,OPENAI_API_BASE,ANTHROPIC_BASE_URL,GEMINI_BASE_URL,GOOGLE_GEMINI_BASE_URL
List 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.
Step 2 — Pick the app slug
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.
Step 3 — Wire each callsite
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: ${gw}/w/<app>/v1, headers: { ... } }).
LangChain / LangGraph — ChatOpenAI(base_url=f"{gw}/w/<app>/openai/v1", default_headers={...}); ChatAnthropic(base_url=f"{gw}/w/<app>", default_headers={...}). LangGraph inherits whatever model you pass it.
LiteLLM — per call api_base=f"{gw}/w/<app>/openai/v1" +
extra_headers={...}, or fleet-wide in the LiteLLM proxy config.yaml.
Raw HTTP / anything else — swap the host, keep the provider's native path:
GATEWAY/w/<app>/v1/chat/completions (OpenAI protocol) or
GATEWAY/w/<app>/v1/messages (Anthropic protocol), add the header(s).
Concretely, with slug support-bot and the hosted gateway, an OpenAI-SDK base
URL reads https://gateway.caveman.so/w/support-bot/openai/v1. And in stored
mode, drop every x-cave-upstream-key line entirely — it is byok-only.
For frameworks not listed (google-genai, crewai, pydantic-ai, openai-agents),
fetch the matching page under <docs origin>/docs/integrations/ — same origin
this skill came from — and follow it.
Add to the repo's env file (and reference from code — no literals):
CAVE_GATEWAY_URL=<GATEWAY>
CAVE_API_KEY=<CAVE_API_KEY>
Step 4 — Verify with one real request
The user pasted the setup prompt to authorize exactly this: one small verification request. Send it now — do not pause to ask permission for it. An integration that ends unverified because you hesitated is a worse outcome than one tiny request; finishing the verification and the report autonomously is the point of this skill.
Send one minimal request through the wiring you just built — the app's own
cheapest path if it has a script for it, otherwise curl on the path matching
the protocol you just wired with the app's own model and a small cap
(max_tokens ≤ 32):
# OpenAI-protocol wiring:
curl -sS "$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.
Step 5 — Report
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.
Failure templates (use verbatim, filled in — never soften)
- Nothing to wire: "I found no LLM callsites in this repo (searched SDKs,
raw provider HTTP, base-URL env vars). If this repo runs a coding agent
rather than shipping LLM code, use
caveman wrap <agent>instead — see <DASHBOARD>/getting-started." - Gateway unreachable: "The verification request could not reach GATEWAY (<error>). Wiring is in place but unverified — nothing will be measured until the gateway is reachable. Check the URL and network, then re-run the verification curl above."
- 401 cave_invalid_api_key: "The gateway rejected CAVE_API_KEY. Mint a new key at <DASHBOARD>/getting-started and update the env file; the wiring itself is unchanged."
- 404 cave_route_not_found: "The gateway matched no route — usually a malformed /w/<app> slug (lowercase [a-z0-9] first, then [a-z0-9._-], max 64) or a path that doesn't match the SDK's protocol. Fix the URL and re-verify."
- Provider error (4xx/5xx via gateway): report status + body verbatim; the gateway is reachable and auth passed, the upstream call failed — usually a provider key or model-name issue in the app itself.
Never report success on any of these. An unverified integration is reported as unverified.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
