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cavecrew

When to delegate to `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit) or `cavecrew-reviewer` (diff review) instead of working inline or using `Explore`. Their output is compressed, so main context lasts longer.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=juliusbrussee-caveman-skills-cavecrew-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
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name cavecrew description When to delegate to `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit) or `cavecrew-reviewer` (diff review) instead of working inline or using `Explore`. Their output is compressed, so main context lasts longer. 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): cavecrew-investigator returns site list. Main thread picks 1-2 sites, hands paths to cavecrew-builder . 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.
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下载的 .skill 包内含以下字段。
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format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
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system_prompt系统提示词正文
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examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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