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Caveman Discover

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JuliusBrussee
caveman-discover

Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

Overview

PublisherJuliusBrussee
Repositorycaveman
Skill namecaveman-discover
Stars
106.3K
Forks
6.2K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by JuliusBrussee on GitHub. Read the source before you install it.

Installation

Install the Caveman Discover AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JuliusBrussee/caveman.git /tmp/caveman
mkdir -p .claude/skills
cp -r /tmp/caveman/skills/caveman-discover .claude/skills/caveman-discover
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Caveman Discover in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Caveman Discover on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Caveman Discover is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)

Names are forever-ish — renaming later splits the spend history. When a job's purpose isn't clear from the code, derive the slug from the file name and mark it review in the table rather than inventing a purpose.

Step 3 — Propose, then apply

Present this table and ask to proceed:

| workflow | job | where | how it gets labeled |
|---|---|---|---|
| support-reply | answers inbound tickets | src/bot/reply.ts:41 | defaultHeaders on the reply client |
| nightly-digest | 02:00 summary job | jobs/digest.ts:12 | header on the digest client |
| eval-suite (review) | scripts/eval.ts:8 — purpose inferred from filename | scripts/eval.ts:8 | env override at invocation |

Then wire each label with the lightest mechanism available at that callsite:

  • @caveman-ai/sdk / caveman_cloud SDK: per-trace workflow option, or defaultWorkflow on the client a single-job service constructs.
  • Raw provider SDKs (OpenAI/Anthropic/LangChain/LiteLLM/Vercel): add "x-cave-workflow": "<slug>" to the same defaultHeaders / default_headers / extra_headers block that already carries x-cave-api-key. Shared client used by several jobs → pass the header per call (every SDK above accepts per-request header overrides), or give each job its own thin client.
  • Wrapped coding agents (caveman wrap): --workflow <slug> flag or CAVE_WORKFLOW=<slug> env at the invocation site (cron line, CI step).
  • Raw HTTP: add the x-cave-workflow header to the request.

Label the callers, keep the diff minimal, match the repo's style. If a callsite is not routed through the Caveman gateway at all, don't label it — list it under "not wired" in the report (labels only travel on gateway traffic; wiring is the caveman-setup skill's job).

Step 4 — Verify

Run whatever the repo already uses to exercise one labeled path (a test, a dev script, one curl). Then confirm: the request still succeeds (the gateway rejects an invalid label with 400 cave_invalid_request_header — fix the slug if so). Labeled spend appears on the dashboard at /activity?tab=workflows as each workflow next runs; jobs on a schedule show up when the schedule fires, and that's worth saying in the report rather than pretending they're live.

Step 5 — Report

## Workflows labeled

| workflow | job | where |
|---|---|---|
| support-reply | answers inbound tickets | src/bot/reply.ts:41 |
| nightly-digest | 02:00 summary job | jobs/digest.ts:12 |

Verified: <the labeled path you actually exercised, and what you observed>
Lands at: <DASHBOARD>/activity?tab=workflows — each row appears as that workflow
next runs. Anything still unlabeled shows as `unlabeled-workflow`.
Not wired (no gateway routing, so no label): <list or "none">
Marked review: <slugs whose purpose was inferred from filenames, or "none">

If you found no LLM entry points at all: say exactly that, and point at the setup skill (<docs origin>/docs/agent-setup.md) instead of manufacturing a table.

Frequently asked questions

What does the Caveman Discover AI skill do?

Find and label every LLM workflow in the repository so Caveman Cloud groups spend by workflow instead of one bucket. Use for "discover workflows" or breaking LLM spend down by workflow.

Why use Caveman Discover on TypingMind?

Because you install it once and use it with any model. Caveman Discover is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Caveman Discover in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JuliusBrussee/caveman/tree/main/skills/caveman-discover. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Caveman Discover?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Caveman Discover?

As many as you like. As long as a model supports skills, you can use Caveman Discover with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Caveman Discover AI skill free?

It is published on GitHub by JuliusBrussee. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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