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 agentmods add skills/juliusbrussee/caveman-code/cavekit-methodologynpx skills add JuliusBrussee/caveman-code --skill cavekit-methodologygit clone --depth 1 https://github.com/JuliusBrussee/caveman-codeWrote 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/juliusbrussee/caveman-code/cavekit-methodology)<a href="https://agentmods.dev/skills/juliusbrussee/caveman-code/cavekit-methodology"><img src="https://agentmods.dev/badge/skills/juliusbrussee/caveman-code/cavekit-methodology.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.02806 |
| Opus 5 | $0.00030 | $0.01403 |
| Sonnet 5 | $0.00012 | $0.00561 |
| Haiku 4.5 | $0.00006 | $0.00281 |
Grade A, and why
cavekit-methodology scanned grade A with 0 findings 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 5d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cavekit Methodology
Core Principle: Specify Before Building
Always define what you want before telling agents how to build it. Go through a cavekit stage — never jump straight from raw requirements to implementation.
Cavekit is a methodology for building software with AI coding agents that puts kits at the center of the development process — code is derived from them, not the other way around. Whether starting from scratch or modernizing an existing system, the principle is the same:
- Greenfield projects: reference material → kits → code
- Rewrites: old code → kits → new code
In both cases, the kits become a living contract that agents consume to continuously build, validate, and refine the application.
Why Kits Are the First-Class Citizen
| Property | Benefit |
|---|---|
| Structured | Organized as a navigable tree, enabling agents to load only what they need |
| Human-legible | Engineers can audit requirements at a higher level than code |
| Stack-independent | Decoupled from any single framework or language |
| Independently evolvable | Kits can be refined without touching implementation |
| Verifiable | Every requirement includes acceptance criteria agents can check |
Key Insight: Well-written kits with strong validation make your application reproducible — any agent can rebuild it from the kits alone. Think of it as continuous regeneration.
The Scientific Method Analogy
LLMs are inherently non-deterministic — like running an experiment, each individual call may yield different results. But through the right methodology — clear hypotheses, controlled conditions, and repeated trials — we extract reliable, reproducible outcomes from a stochastic process.
Cavekit applies the scientific method to software construction — hypothesize, test, observe, refine.
| Layer | Analogy | What It Does |
|---|---|---|
| LLM calls | Individual experiments | Each run may produce different results; no single output is authoritative |
| Kits | Hypotheses | Define what you expect to observe — the predicted behavior |
| Validation gates | Controlled conditions | Ensure reproducibility by constraining what counts as a valid outcome |
| Convergence loops | Repeated trials | Build statistical confidence through successive passes |
| Implementation tracking | Lab notebook | Record what was tried, what worked, and what failed |
| Revision | Revising the hypothesis | When results contradict expectations, update the theory upstream |
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.
- 5d ago First seen · 265 lines · 60 tokens per session scan A 765e21ebc2a2
cavekit-methodology is a skill published in the GitHub repository JuliusBrussee/caveman-code (931 stars, last pushed 21d ago), licensed MIT. It adds 60 tokens to every session and 2,806 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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