cavekit is a Claude Code plugin that organizes software changes through a compact, spec-driven workflow stored in a SPEC.md file. It is for developers who want to plan, research, review, and build changes while preserving project context across sessions; the catalogue entries are its skills, commands, plugin configuration, and workflow rules.
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 JuliusBrussee/cavekit --skill deepengit clone --depth 1 https://github.com/JuliusBrussee/cavekitWrote 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/cavekit/deepen)<a href="https://agentmods.dev/skills/juliusbrussee/cavekit/deepen"><img src="https://agentmods.dev/badge/skills/juliusbrussee/cavekit/deepen/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/juliusbrussee/cavekit/deepen"><img src="https://agentmods.dev/badge/skills/juliusbrussee/cavekit/deepen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00137 | $0.00975 |
| Opus 5.5 | $0.00055 | $0.00390 |
| Sonnet 5.5 | $0.00027 | $0.00195 |
| Haiku 4.5 | $0.00014 | $0.00097 |
Grade A, and why
deepen 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 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.
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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
deepen — make modules deep
Behavior is sacred: tests green before AND after. Every change shrinks an interface or hides a decision — deepen, don't churn.
A deep module hides a lot behind a small interface; a shallow one's interface costs as much to use as writing the code yourself. Complexity = dependencies + obscurity, and it compounds. Deepen spends spare usage paying that down before it becomes a §B. Run it when the build is green & you have budget to drain — not under deadline.
WHEN TO DEEPEN
- Build is green, tests pass, & you have token budget spare.
- A module's interface feels as complex as its implementation (shallow smell).
- The same change keeps touching many files (change amplification).
- User explicitly asks to improve design quality.
⊥ run mid-feature or under pressure. Deepen is the deliberate pass, not the reflex.
FIVE STEPS
1. PICK THE SHALLOW
Scan the modules the spec touches. Rank by shallowness — interface surface vs work done. Pick the one worst offender. Tells:
- Pass-through method that only forwards to one other (shallow layer).
- Caller must set 5 flags right to use it (config leakage).
- Same abstraction repeated at two layers (no information hiding).
- A
?or §B that traces back to a confusing interface.
One module per pass. Deepening is surgical, ⊥ a codebase sweep.
2. DIAGNOSE
Name the design defect in caveman, citing file:line:
src/auth/token.go: 6-arg ctor leaks rotation policy to every caller. shallow. Complexity is real only if it shows: change amplification, high cognitive load, or an unknown-unknown (caller must know a hidden fact to call it right).
3. RESEARCH THE DEEPENING
What does a deeper version look like? Pull a known pattern (hand to research for the external case → §R) or derive from the codebase's own better modules. Moves that deepen:
- Pull complexity down — hide the hard part inside, give callers the simple path.
- Define errors out of existence — design the interface so the edge can't occur.
- Information hiding — one decision, one module; callers don't learn it.
- General-purpose interface over a pile of special-case methods.
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 · 85 lines · 137 tokens per session scan A 5b8c9a74f0b6
deepen is a skill published in the GitHub repository JuliusBrussee/cavekit (1,156 stars, last pushed 2mo ago), licensed MIT. It adds 137 tokens to every session and 975 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
review
Uses a fresh agent to review an implementation change without editing it. Checks behavior, security, regressions, complexity, tests, docs, and missing proof. Use for code, PR, diff, security, second-opinion, or pre-merge reviews.
code-reviewer
Review completed implementation batches for spec compliance and code quality. Invoke after execution batches complete, before merging, or when a review gate is reached in the workflow.
architecture-review
Reviews a technical proposal before implementation. Use for system architectures, feature specs, RFCs, ADRs, and issues that define how a system change should work. Finds material ambiguity and flaws in correctness, scalability, performance, security, operations, and proof.
task-to-pr
Delivers specs or decided tasks as tested, independently reviewed pull requests. Runs the CI and review repair loop, then leaves passing pull requests open unless merging was explicitly authorized.
improve
Makes existing code easier to understand without changing behavior. Use to simplify structure, remove duplication or dead code, improve names, or remove unnecessary abstractions.
speq-code-review
Code review tag taxonomy and findings output format — guardrail violations, dead code, test quality, bad comments, optimizations, YAGNI/over-engineering, error handling, and design depth. Used by the speq code-reviewer agent during a speq implement run. Not for general code review.