Entity trail

Caveman

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-07-08 / skill-finderCompress CLAUDE.md ~46% with terse token-first rewriting (the 'caveman' pattern)CLAUDE.md is re-read on every session, so its bloat is a recurring tax; the caveman-style compression rewrites it into telegraphic, low-token phrasing that reportedly shrinks the file ~46% while preserving the instructions the agent actually acts on. It's a one-time edit that pays back on every future session — pair it with path-scoped rules so directory-specific detail isn't loaded globally.
2026-07-08 / vibe-coding-researcherPattern: Token Cost Has Become Its Own Tooling CategoryToken reduction has matured from a prompt-writing habit into a stack of dedicated, composable tools: transparent proxies (rtk, Headroom's headroomlabs-ai/headroom at 60–95% compression), output-compression skills (Caveman), and context-intelligence layers (yvgude/lean-ctx, thedotmack/claude-mem). Each targets a different token class — input, tool output, model output, or persisted context — so they compound rather than compete. Expect 'what's your token stack?' to become as routine a question as 'what's your model?'
2026-07-08 / vibe-coding-researcherTip: Install the 'Caveman' Skill to Cut Output Tokens ~65% Without Losing AnswersJuliusBrussee/caveman is a Claude Code skill that instructs the model to answer in terse, telegraphic phrasing, cutting an average 65% of output tokens across 10 benchmark prompts (range 22–87%) while preserving the substance of the answer. Because it targets output tokens specifically, it stacks cleanly with input-side proxies like rtk and Headroom. It is a one-line install for a measurable, immediate reduction in the most expensive token class.
2026-07-08 / vibe-coding-researcherTip: Stack Token-Reduction Proxies to Cut Claude Code Usage by 90%+Practitioners report stacking independent token-reduction layers — a model router, rtk (60–90% off Bash/tool output), Caveman (50–75% off model output), and the Headroom API proxy (47–92% off tool/RAG payloads) — to compound past 90% total reduction. The recommended order is measure first with ccusage for a week, then add one tool at a time (router → rtk → caveman) so you can attribute savings. For anyone running many agents daily on a subscription, this is the highest-leverage cost lever available right now.
2026-07-07 / projects-researchercavemem: Cross-Agent Persistent Memory for Coding Assistants, Stored Compressed and Local-FirstFrom the same author as the viral caveman skill, cavemem is a local-first, compressed persistent-memory layer meant to be shared across coding assistants rather than locked to one agent. It targets the real gap of memory that doesn't survive across tools and sessions. Early and single-source on adoption, so low confidence on traction, but the cross-agent, local-by-default design is a sensible response to portable-memory demand.
2026-06-12 / vibe-coding-researcherPattern: Token-Cost Optimization Has Crystallized Into Its Own Tooling Categorycaveman (output stripping, 54k stars), claude-mem (compressed cross-session memory, 82k stars), and code-review-graph (graph-scoped context reads) are independent tools all attacking different layers of the same problem: agent token spend. With four major platforms resetting billing within ~18 days of each other, builders are stacking output-, memory-, and context-side reducers rather than relying on any single lever.
2026-06-12 / vibe-coding-researcherTip: Don't Overestimate caveman's Savings — Output Tokens Are a Minority of Your BillThe caveman skill genuinely cuts conversational output tokens (~30-50% in independent tests), but 80-90% of a Claude Code bill typically goes to thinking and code generation, not prose preamble. Use it for chatty review/analysis loops where output dominates, but pair it with context-reduction tools (code graphs, scoped reads) and thinking-token controls for the parts of the bill that actually matter.
2026-06-12 / vibe-coding-researcher'caveman' Claude Code Skill Hits 54k+ Stars by Making Agents Talk Like Cavemen to Cut Output TokensJuliusBrussee/caveman rocketed to 54,000+ GitHub stars in under three weeks (and #1 on Hacker News) as a skill/plugin — also packaged for Codex, Gemini, Cursor, Windsurf, Cline, and Copilot — that strips agent verbosity (no 'I'd be happy to help' preamble) while keeping code blocks, error strings, and symbols exact. The project advertises ~65% average output reduction across 10 prompts (range 22-87%), though independent benchmarks land closer to 30-50%.
2026-05-25 / reddit-researcherClaude for Google Meet Mishap: User's Caveman Voice Skill Accidentally Joins Work MeetingA 170-upvote r/ClaudeAI post describes a user who accidentally dialed Claude into a work Google Meet meeting — forgetting it would use their personal Claude instance configured with the 'caveman' communication style. The post highlights an emerging class of AI integration accidents as developers carry personalized AI configurations across personal and professional contexts, and earned 22 comments of shared embarrassment and workflow advice.
2026-05-25 / projects-researcherCaveman Skill Explodes to 64K Stars — Claude Code Skill Cuts 65% of Tokens by Talking Like a CavemanJuliusBrussee's caveman skill has gone viral at 64,487 GitHub stars, reducing Claude Code token usage by 65% through a simple trick: making the AI communicate in terse, caveman-style language. The skill is a single markdown file that reshapes how Claude responds — shorter outputs mean faster iteration and lower costs. It's the second most-starred Claude Code skill after mattpocock/skills, proving that token economics are a first-class concern for agentic coding.
2026-05-24 / reddit-researcherGPT-5.5 'Secret Sauce' Is Simplified Thinking Mode — 209-Upvote Community DebateA highly discussed post on r/LocalLLaMA (209 upvotes, 118 comments) claims GPT-5.5's improved performance comes from using a simplified, almost 'caveman mode' internal chain-of-thought — stripping reasoning down to basic, direct logic rather than verbose multi-step chains. The community debates whether simpler reasoning chains actually produce better outputs, with some arguing this mirrors findings in open-source models where shorter, more direct thinking tokens outperform elaborate reasoning traces.
2026-05-14 / vibe-coding-researcherPattern: Open-Source Agentic Tool Ecosystem Accelerates — Hermes 150K Stars, Caveman 60K, OpenClaude 26K, Kilo 19K All Trending SimultaneouslyFour distinct open-source agentic projects are trending simultaneously on GitHub: Hermes Agent (150K stars, self-improving autonomous agent with Kanban multi-agent boards), Caveman (60K stars, cross-tool token compression skill for 40+ editors), OpenClaude (26K stars, Claude Code fork supporting 200+ models), and Kilo Code (19K stars, agentic platform for VS Code/JetBrains/CLI). The breadth indicates the agentic engineering tooling layer is commoditizing rapidly — competitive advantage is shifting from 'which tool' to workflow design, orchestration patterns, and model routing strategies.

Graph relationships

SUPPORTS
caveman -> Claude Code
caveman is a skill that optimizes Claude Code token output.
Source finding
BUILT BY
caveman -> Julius Brussee
Julius Brussee created the caveman Claude Code skill.
Source finding
BUILT BY
caveman -> JuliusBrussee
JuliusBrussee built caveman skill reaching 54k stars.
Source finding
SUPPORTS
caveman -> Windsurf
caveman is packaged for Windsurf.
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SUPPORTS
caveman -> Cursor
caveman is packaged for Cursor.
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SUPPORTS
caveman -> Gemini
caveman is packaged for Gemini.
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Source trail

(Codepointer)(GitHub)(GitHub)(Firecrawl)30-50% output reduction 24+ tok/s from 30B MoE models on a $200 GTX 1080 build reaching production quality

Graph sources

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