Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mrDesign-ww/vault-os --skill caveman-setupgit clone --depth 1 https://github.com/mrDesign-ww/vault-osWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mrdesign-ww/vault-os/caveman-setup)<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/caveman-setup"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/caveman-setup/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/caveman-setup"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/caveman-setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00082 | $0.02875 |
| Opus 5.5 | $0.00033 | $0.01150 |
| Sonnet 5.5 | $0.00016 | $0.00575 |
| Haiku 4.5 | $0.00008 | $0.00287 |
Grade A, and why
caveman-setup scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
cheapest path if it has a script for it, otherwise curl **on the path matching This is a copy
95% identical to caveman-setup — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 225 lines · 82 tokens per session scan A 254ddc40c355
caveman-setup is a skill published in the GitHub repository mrDesign-ww/vault-os (2 stars, last pushed 4d ago), licensed MIT. It adds 82 tokens to every session and 2,875 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to caveman-setup, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…