Borrowing it
Nothing to install: this file belongs to igrechuhin/Cortex. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/igrechuhin/Cortex/main/.agents/skills/caveman-discover/SKILL.mdgit clone --depth 1 https://github.com/igrechuhin/CortexWrote 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/igrechuhin/cortex/caveman-discover)<a href="https://agentmods.dev/skills/igrechuhin/cortex/caveman-discover"><img src="https://agentmods.dev/badge/skills/igrechuhin/cortex/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/igrechuhin/cortex/caveman-discover"><img src="https://agentmods.dev/badge/skills/igrechuhin/cortex/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 | $0.00044 | $0.01318 |
| Opus 5.5 | $0.00018 | $0.00527 |
| Sonnet 5.5 | $0.00009 | $0.00264 |
| Haiku 4.5 | $0.00004 | $0.00132 |
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
100% identical to caveman-discover — 0 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 — 116 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 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 · 116 lines · 44 tokens per session scan A 0483d17fce14
caveman-discover is a skill published in the GitHub repository igrechuhin/Cortex (3 stars, last pushed 3d ago), licensed MIT. It adds 44 tokens to every session and 1,318 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to caveman-discover, differing in 0 lines, and is treated as a copy.
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