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-discovergit 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-discover)<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/caveman-discover"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/caveman-discover/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-discover"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/caveman-discover.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 | $0.00095 | $0.01374 |
| Opus 5.5 | $0.00038 | $0.00550 |
| Sonnet 5.5 | $0.00019 | $0.00275 |
| Haiku 4.5 | $0.00010 | $0.00137 |
Grade A, and why
caveman-discover 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.
dev script, one curl). Then confirm: the request still succeeds (the gateway This is a copy
86% identical to caveman-discover — 9 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
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 · 119 lines · 95 tokens per session scan A d1efb1d986c8
caveman-discover is a skill published in the GitHub repository mrDesign-ww/vault-os (2 stars, last pushed 4d ago), licensed MIT. It adds 95 tokens to every session and 1,374 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to caveman-discover, differing in 9 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…