{
    "app": {
        "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.",
        "mode": "advanced-chat",
        "model_config": {
            "provider": "deepseek",
            "model": "deepseek-chat",
            "parameters": {
                "temperature": 0.7,
                "max_tokens": 4096
            }
        }
    },
    "instructions": "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.",
    "variables": [],
    "opening_statement": "你好，我是 cavecrew，When to delegate to `cavecrew-investigator` (locat...",
    "suggested_questions": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=juliusbrussee-caveman-skills-cavecrew-skill-md"
}