cavecrew

cavecrew is a skill for Claude Code, Codex from mrDesign-ww/vault-os. It costs 134 tokens per session (984 once invoked), scanned A, a copy of cavecrew, MIT.

A guide for assigning code-search, editing, and review tasks to specialised helper agents. It explains when to use each helper and when to keep the work in the main coding session.

In plain words
What is it for?
It helps locate definitions and usages, make small edits, and review files, branches, or code changes.
Why use it?
It helps avoid sending large, unsuitable answers into the main conversation and gives different tasks an appropriate helper or workflow.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit It helps locate definitions and usages, make small edits, and review files, branches, or code changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mrdesign-ww/vault-os/cavecrew
Install

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.

Any agent
npx skills add mrDesign-ww/vault-os --skill cavecrew
Clone the repo
git clone --depth 1 https://github.com/mrDesign-ww/vault-os

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cavecrew

README.md
[![agentmods](https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/cavecrew/github.svg)](https://agentmods.dev/skills/mrdesign-ww/vault-os/cavecrew)
Your own site
<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/cavecrew"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/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.

agentmods 80×15 button for cavecrew

Your own site · 80×15
<a href="https://agentmods.dev/skills/mrdesign-ww/vault-os/cavecrew"><img src="https://agentmods.dev/badge/skills/mrdesign-ww/vault-os/cavecrew.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 134 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 984 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00134 $0.00984
Opus 5 $0.00067 $0.00492
Sonnet 5 $0.00027 $0.00197
Haiku 4.5 $0.00013 $0.00098

Measured 7d ago against content hash b74f374f6aae, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 7d 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.

Origin

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.

shell/claude/skills/cavecrew/SKILL.md · 83 lines

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

Read the full file on GitHub · 83 lines

Files

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.

Changes

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.

  1. 7d ago First seen · 83 lines · 134 tokens per session scan A b74f374f6aae

Subscribe to this mod's changes

cavecrew is a skill published in the GitHub repository mrDesign-ww/vault-os (2 stars, last pushed 4d ago), licensed MIT. It adds 134 tokens to every session and 984 once invoked, about $0.0007 per session on Opus 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.

Related

Other skills, from other repositories

go-testing

Trigger: Go tests, go test coverage, Bubbletea teatest, golden files. Apply focused Go testing patterns.

Gentleman-Programming/gentle-ai · 26 tokens

security-review

Perform a focused security review of pending git changes to identify high-confidence security vulnerabilities with real exploitation potential. Use this skill when the user asks for a security review, security audit, vulnerability scan, or wants to check pending changes on a branch for security issues before merging.…

waybarrios/opencode-power-pack · 64 tokens

huggingface-llm-trainer

Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion. Use for cloud LLM training; use huggingface-vision-trainer for vision tasks.

waybarrios/opencode-power-pack · 65 tokens

feature-dev

Guide a feature implementation through a structured seven-phase workflow with deep codebase understanding, clarifying questions, parallel architecture design, and quality review. Use this skill when the user asks to build a new feature, add functionality, or wants a methodical approach to implementation rather than…

waybarrios/opencode-power-pack · 62 tokens

review

Validate plans, execution, or PRs against wish criteria — returns SHIP / FIX-FIRST / BLOCKED with severity-tagged gaps.

automagik-dev/genie · 29 tokens

reply-to-pr-threads

Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draft PR reply messages".

tobihagemann/turbo · 71 tokens