cavecrew

JuliusBrussee/caveman · Agent Skill

用於程式碼查詢、編輯和審查的三個子代理。它們返回壓縮輸出,為更長的任務節省上下文。

前往安裝 ↓

總覽

發布者JuliusBrussee社群
儲存庫JuliusBrussee/caveman ↗
技能資料夾cavecrew
星105,532
分支6,112
附帶檔案1
授權Apache-2.0
最近更新2026年9月14日
連結技能原始碼 ↗ · 儲存庫 ↗

Mozgi 檢查

我們在技能檔案中發現的內容。詳細資訊需訂閱。

安全性
中等風險 · 發現 1 個需要注意的地方
相容性
僅含說明 — 適用於多數代理
相似技能
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訂閱另含

安裝

技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。

把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。

Install the “cavecrew” skill from mozgi.io (https://mozgi.io/skills/cavecrew/) for me.

1. Download the archive: https://mozgi.io/skills/cavecrew/download
   If it fails, take the skill folder from the source: https://github.com/JuliusBrussee/caveman/tree/main/plugins/caveman/skills/cavecrew
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “cavecrew” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/cavecrew/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

技能檔案

1 個檔案 下載 ZIP

安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。

SKILL.md 內容

作者原文 · 唯讀

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

Locate → fix → verify (most common):

1. cavecrew-investigator returns site list.

2. Main thread picks 1-2 sites, hands paths to cavecrew-builder.

3. cavecrew-reviewer audits the diff.

Parallel scout (when investigation is broad):

Spawn 2-3 cavecrew-investigator calls in one message (different angles: defs vs callers vs tests). Aggregate in main thread.

Single-shot edit (when site is already known):

Skip investigator. Hand exact path:line to cavecrew-builder directly.

What NOT to do

  • Don't use cavecrew-builder when you don't already know the file. Spawn investigator first or main thread will eat tokens passing context.
  • Don't chain cavecrew-investigator → cavecrew-builder for a 5-file refactor. Builder will return too-big. and you'll have wasted a turn.
  • Don't ask cavecrew-reviewer for "general feedback" — it returns findings only, no architecture opinions. Use Code Reviewer for that.
  • Don't expect prose. Cavecrew output is structured, sometimes terse to the point of cryptic. If a human will read it directly, paraphrase.

Auto-clarity (inherited)

Subagents drop caveman → normal English for security warnings, irreversible-action confirmations, and any output where fragment ambiguity could be misread. Resume caveman after.

常見問題

cavecrew 技能有什麼用途?

用於程式碼查詢、編輯和審查的三個子代理。它們返回壓縮輸出,為更長的任務節省上下文。

如何安裝 cavecrew?

打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。

cavecrew 適用哪些 AI 模型?

技能是純 Markdown 指示(Agent Skills 格式),因此 cavecrew 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。

cavecrew 是免費的嗎?

此技能由 JuliusBrussee 公開發布,採用 Apache-2.0 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。

安裝技能安全嗎?

技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。

技能和 MCP 伺服器或外掛有何不同?

MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。

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