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 GktuOktay/ai-skills --skill cavecrewgit clone --depth 1 https://github.com/GktuOktay/ai-skillsWrote 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/gktuoktay/ai-skills/cavecrew)<a href="https://agentmods.dev/skills/gktuoktay/ai-skills/cavecrew"><img src="https://agentmods.dev/badge/skills/gktuoktay/ai-skills/cavecrew/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/gktuoktay/ai-skills/cavecrew"><img src="https://agentmods.dev/badge/skills/gktuoktay/ai-skills/cavecrew.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 | $0.00134 | $0.00984 |
| Opus 5.5 | $0.00054 | $0.00394 |
| Sonnet 5.5 | $0.00027 | $0.00197 |
| Haiku 4.5 | $0.00013 | $0.00098 |
Grade A, and why
cavecrew 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.
This is a copy
100% identical to cavecrew — 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cavecrew = three subagent presets that emit caveman output. Same job as Anthropic defaults (Explore, edit-style agents, reviewer); difference is the tool-result they return is compressed, so main context shrinks per delegation.
When to use cavecrew vs alternatives
| Task | Use |
|---|---|
| "Where is X defined / what calls Y / list uses of Z" | cavecrew-investigator |
| Same but you also want suggestions/architecture commentary | Explore (vanilla) |
| Surgical edit, ≤2 files, scope obvious | cavecrew-builder |
| New feature / 3+ files / cross-cutting refactor | Main thread or feature-dev:code-architect |
| Review diff, branch, or file for bugs | cavecrew-reviewer |
| Deep code review with rationale + alternatives | Code Reviewer (vanilla) |
| One-line answer you already know | Main thread, no subagent |
Rule of thumb: if you'd want the subagent's output in 1/3 the tokens, pick cavecrew. If you'd want prose, pick vanilla.
Why this exists (the real win)
Subagent tool results get injected into main context verbatim. A vanilla Explore that returns 2k tokens of prose costs 2k tokens of main-context budget every time. The same finding from cavecrew-investigator returns ~700 tokens. Across 20 delegations in one session that's the difference between context exhaustion and finishing the task.
Output contracts
What main thread can rely on per agent:
cavecrew-investigator
<Header>:
- path:line — `symbol` — short note
totals: <counts>.
Or No match. Always file-path-first, line-number-attached, backticked symbols. Safe to grep with path:\d+.
cavecrew-builder
<path:line-range> — <change ≤10 words>.
verified: <re-read OK | mismatch @ path:line>.
Or one of: too-big. / needs-confirm. / ambiguous. / regressed. (terminal first token).
cavecrew-reviewer
path:line: <emoji> <severity>: <problem>. <fix>.
totals: N🔴 N🟡 N🔵 N❓
Or No issues. Findings sorted file → line ascending.
Chaining patterns
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 83 lines · 134 tokens per session scan A b74f374f6aae
cavecrew is a skill published in the GitHub repository GktuOktay/ai-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 134 tokens to every session and 984 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to cavecrew, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
github
Manages all git operations (commit, push, branch management, PR creation) in a standardized, safe, and consistent way. Automatically runs the code-review skill before any commit. Enforces Conventional Commits standard. Handles the full lifecycle: branch → review → commit → push → PR.
prompt-enhancer
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, belirsiz veya eksik bir kullanıcı isteğini alır ve onu daha net, daha spesifik, daha uygulanabilir bir prompt'a dönüştürür. Agent'ın yanlış anlayarak iş yapmasını önler. "Şunu düzelt" gibi muğlak bir istek, "hangi koşulda ne bekleniyor, başarı kriteri ne" formatına çevrilir.
architecture-review
Before committing to an implementation plan, run this skill to stress-test the proposed architecture. Catches over-engineering, circular dependencies, missing failure modes, security gaps, and scalability cliffs — before any code is written. Acts as a "second eye" on the plan.
brainstorming
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, bir fikir veya problemi Sokratik yöntemle rafine eder. Agent sana cevap vermez — sorular sorar. Bu sorular aracılığıyla fikrin netleşir, varsayımlar sorgulanır, kapsam belirlenir ve gerçek ihtiyaç ortaya çıkar. "Ne yapalım?" sorusunu "Tam olarak ne yapmamız gerekiyor ve…
code-review
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
dependency-audit
Periodically scan project dependencies for security vulnerabilities, outdated packages, and unused dependencies. Produces a prioritized action report. Run before every major release and at least once per month.