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Get Started Free →hashgraph-online/methodology
Core Cavekit methodology — the master skill that teaches the Hunt lifecycle and routes to all sub-skills. Covers the Specify Before Building principle, the scientific method analogy, the four-phase Hunt lifecycle, decision matrix for when to use Cavekit, and build pipeline analogy. Trigger phrases: "use Cavekit", "cavekit methodology", "start Cavekit project", "cavekit methodology", "how should I structure this project for AI agents"
Passed 21 of 22 test cases with the skill, and 12 of 22 without it.
and 11 more in the full report
9 fixed12 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are conducting an inspection pass over recent automated coding outputs and reviewing git diffs. What precise signal indicates that the inspection phase is complete and the application is ready for monitoring?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies shrinking diff magnitude across successive passes as the convergence signal. | Missed | Passed |
| Requires remaining modifications across passes to be non-functional or cosmetic. | Missed | Passed |
The instructions your model reads when the skill loads.
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:
In both cases, the kits become a living contract that agents consume to continuously build, validate, and refine the application.
| 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.
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 |
test cases passed, vs 12 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.