mcpField
C
cavecrew

cavecrew

github.com/rheinmirAgent workflows10262 installsUpdated 2w ago
Repository

About cavecrew

cavecrew is a skill providing three subagent presets for code investigation, small edits, and review, with compressed structured outputs. It is intended for delegation when concise results help limit the context used by the main agent.

Key features

  1. 01Investigation, builder, and reviewer presets
  2. 02Compressed, structured subagent results
  3. 03File-and-line investigation findings
  4. 04Scoped edits with verification status
  5. 05Review findings sorted by file and line

Use cases

  1. 01Locate definitions and callers
  2. 02Delegate a small, well-scoped edit
  3. 03Review a diff for bugs
  4. 04Run parallel codebase investigations

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/rheinmir/setup --skill cavecrew

After installing, type/cavecrewin Claude Code to confirm it loaded.

Details

From the project README

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

TaskUse
"Where is X defined / what calls Y / list uses of Z"cavecrew-investigator
Same but you also want suggestions/architecture commentaryExplore (vanilla)
Surgical edit, ≤2 files, scope obviouscavecrew-builder
New feature / 3+ files / cross-cutting refactorMain thread or feature-dev:code-architect
Review diff, branch, or file for bugscavecrew-reviewer
Deep code review with rationale + alternativesCode Reviewer (vanilla)
One-line answer you already knowMain 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.


Output Report

After all main skill tasks complete, write a propose draft to the wiki.

Steps

1. Build the filename:

  • Format: DDMMYY-<ten>.md
  • DDMMYY = today (e.g., 020626 for 2 June 2026)
  • <ten> = 2–4 kebab-case words summarising what was done (e.g., landing-page-coteccons, brand-kit-fintech, ingest-auth-spec)

2. Write llmwiki/wiki/draft/cave/DDMMYY-<ten>.md:

# DDMMYY-<ten>
**Type:** draft
**Status:** proposed
**Tags:** <skill-name>, output-report
**Proposed:** YYYY-MM-DD

## What
<One sentence — what this skill invocation produced or decided>

## Output
<Key artefacts, files created/modified, or decisions made>

## Files
| File | Action |
|------|--------|
| `path/to/file` | created / modified |

## Notes
- Invoked via: `/<skill-name>` skill

## Origin
- **Draft:** `wiki/draft/cave/DDMMYY-<ten>.md`
- **Commit:** _(filled by verify-before-commit)_
- **Date promoted:** _(filled by verify-before-commit)_

3. Update wiki index & log:

  • llmwiki/wiki/index.md — append one row: | [DDMMYY-<ten>](draft/cave/DDMMYY-<ten>.md) | draft | YYYY-MM-DD |
  • llmwiki/wiki/log.md — append: ## YYYY-MM-DD — <skill-name> — <ten>

Skip only when the skill produces zero artefacts and zero decisions (e.g., a pure display mode like /caveman-stats).

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