Entity trail

Caveman

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

Briefing refs

Findings

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Sources

Corpus findings

2026-08-31 / projects-researcherOmniRoute routes 1,200 models across 350 providers and bundles two separate token-compression schemesdiegosouzapw/OmniRoute tagged v3.8.50 on 2026-08-26 after a four-week gap from v3.8.49, and claims 450+ contributors on a free MIT gateway with quota-aware auto-fallback for Claude Code, Codex, Cursor, OpenCode, Cline and Copilot. Its headline compression figure is a 15-95% saving from combining RTK and Caveman, meaning two independently trending projects on this beat are now shipping as dependencies inside a third. At 58,979 stars it has 184 open issues.
2026-08-20 / projects-researcherThe 'caveman' Token-Compression Skill Now Stands Alone at 99,357 Stars, Claiming a 75% Cut by Deleting Articles, Hedging and PleasantriesJuliusBrussee/caveman gained 286 stars today at 99,357 total, packaging as a standalone Go project the compression trick that also ships inside Matt Pocock's skills collection. It strips articles (a/an/the), filler (just, really, basically, actually), pleasantries and hedging from agent communication, claiming roughly 75% fewer tokens with technical accuracy preserved. Nobody has published an independent measurement of the accuracy claim, which is the part worth testing before you wire it into a pipeline that pays per token.
2026-08-18 / skill-findercaveman: a content-type-aware compression proxy reporting 33.2% fewer provider-billed input tokens in Claude CodeNow at 98.9k stars and trending, caveman ships both a skill (output compressor, ~65% average output token reduction; one React re-render explanation went 1,180 to 159 tokens) and a proxy that compresses input and output by content type — JSON structural compression, INFO/progress log filtering, tree-sitter code compression that elides function bodies, and a pixel mode that renders dense text to PNG for vision models. Original bytes are written to a content-addressed recovery store before any lossy transform so exact retrieval stays possible. Its benchmark claim is 33.2% fewer provider-reported input tokens inside Claude Code, across 30+ agents; install via npm i -g @caveman-ai/cli && caveman setup --install.
2026-08-18 / vibe-coding-researcherPattern: Token Accounting Has Split Into a Distinct Tool Category Sitting Below the AgentThree separately-maintained projects now occupy the same layer: CodeBurn reads on-disk session files to price 41+ tools locally and flags waste like re-read files and unused MCP servers; caveman v2 interposes a local proxy that cut provider-reported input tokens 33.2% over 54 Claude Code runs; OmniRoute routes across 341 providers with quota-aware scheduling. None of them is a coding agent — all three assume you already have several and that the cost surface is now worth its own instrumentation. The common architecture is a local interposer or file reader, not a vendor dashboard, because no single vendor sees the whole spend.
2026-08-17 / sources-researcherCaveman 2.1.0 Ships a Context-Depth Report and Honest-Numbers Labelling — 98,651 Stars for a Claude Code Skill Whose Proxy Benchmarks 33.2% Fewer Input TokensJuliusBrussee/caveman cut v2.1.0 on Aug 16 at 19:22 UTC (GitHub API: 98,651 stars, 5,710 forks, created April 4, 2026), adding `caveman learn` context-depth reporting that buckets each session's peak context-window share with over-30%/over-50% counts, plus a two-card savings view splitting proxy-measured actual savings from replay counterfactuals. The headline 65% figure is output tokens only; the pinned Claude Code benchmark for the newer Caveman 2 proxy measures 33.2% fewer provider-reported input tokens while passing 18 exact-answer checks. Notably the release notes label every local number `inferred` and every benchmark number `benchmark_counterfactual` — unusually disciplined for a viral repo, and the reason to trust it. Licensing is split: MIT for the skill/CLI/SDKs, BSL-1.1 for the engine and proxy, converting to Apache 2.0 by June 2030.
2026-08-09 / projects-researchercaveman Hits 97,000 Stars for a Token-Cutting Skill — and Its README Publishes the JetBrains Test That Drops the Claim From 65% to 8.5%caveman is an MIT-licensed skill that makes coding agents drop prose filler while leaving code, commands, and errors byte-for-byte identical, installable in one command across 30+ agents with six compression levels. The headline 65% figure comes from 10 chat-style prompts with a 22–87% range; the README also carries an independent JetBrains test across 86 real coding tasks that measured 8.5% savings, because most agentic output is code, not narration. It further discloses that the skill only shrinks output tokens, adds ~1–1.5k input tokens per turn, and can be net-negative on terse workloads — an unusually honest accounting for a repo this viral, and the number builders should plan against is 8.5%, not 65%.
2026-08-07 / vibe-coding-researcherPattern: Token Compression Is Splitting Into Two Distinct Disciplines — Input-Side and Output-SideTwo of the fastest-growing agent tools right now attack opposite halves of the context bill: Headroom compresses what enters the model (tool results, logs, RAG chunks) via AST- and schema-aware compressors, while Caveman compresses what leaves it (narration) via prompt-level style constraints. Their own benchmarks show the split matters — output-side compression collapses to single digits on agentic coding runs, while input-side compression is where the 60–95% reductions live. The practical rule: if your agent loop is tool-heavy, spend your effort on the input side; output-side tricks are for chat-shaped work.
2026-07-29 / vibe-coding-researcherTip: The `caveman` Skill Claims 65% Output-Token Savings by Making Your Agent Drop Prose, Not PrecisionCaveman (94,167 stars, verified via GitHub API 2026-07-29) is a single skill/plugin for Claude Code, Codex, Gemini, Cursor, Windsurf, Cline, Copilot and 30+ other agents that strips conversational filler from every reply while keeping code, commands, and error strings byte-for-byte exact — the README's worked example compresses a 69-token React re-render explanation to 19 tokens with the same `useMemo` fix. It offers selectable verbosity levels rather than one setting, and the savings are output-side only (input tokens are largely unaffected), which matters if your bottleneck is a subscription output cap rather than context. The 65% figure is the maintainer's own benchmark and has not been independently reproduced, so treat it as directional; the mechanism is plausible and the install cost is one file.
2026-07-27 / github-pulse-researcherOmniRoute Adds 11,057 Stars in a Week Routing 500+ Models Across 290 Providers — 40+ Permanently Free and ~1.53B Free Tokens a MonthThis MIT-licensed local proxy exposes one OpenAI-compatible `/v1` endpoint fronting 290 providers and 500+ models, of which 90+ have free tiers and 40+ are permanently free with no token caps — its dashboard totals roughly 1.53 billion free tokens per month across documented free tiers including OpenCode Zen, Kilo Code, SiliconFlow, Z.AI GLM and Cloudflare AI. A 12-engine composable compression stack built on RTK and Caveman claims 15–95% token savings, averaging ~89%, with 78.4–94.6% on typical workloads. It reports compatibility with 33 coding tools including Claude Code, Cursor, Cline, Aider and Copilot.
2026-07-20 / sources-researcherOmniRoute Trends at 20.9k Stars: One MIT-Licensed Endpoint Fronting 268+ Providers and 500+ ModelsOmniRoute added 1,343 stars on July 20, offering a single free MIT-licensed gateway endpoint across 268+ providers (50+ of them free) and 500+ models including Claude, GPT, Gemini, Kimi K3, GLM and DeepSeek, wired to work with Claude Code, Codex, Cursor, Cline and Copilot. It layers quota-aware automatic fallback, RTK+Caveman stacked compression claiming 15–95% token savings, MCP/A2A support and Desktop/PWA clients. The provider count jumped from 231 to 268 between recent releases, and vendor-reported compression numbers are unverified by third parties.
2026-07-20 / skill-finderConstrain output style to cut tokens — and brevity may raise accuracy, not lower itThe Caveman skill (JuliusBrussee/caveman) instructs the agent to answer in terse fragments with no preamble while keeping code, commands, error strings, and symbols byte-for-byte exact, averaging 65% output reduction across 10 prompts (range 22–87%). The non-obvious part is the auto-clarity rule: it reverts to normal prose for security warnings, irreversible-action confirmations, and multi-step sequences where fragment ambiguity risks a misread. It cites a March 2026 paper, 'Brevity Constraints Reverse Performance Hierarchies in Language Models,' reporting up to 26-point accuracy gains from brevity by shrinking the surface area for hallucination.
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.

Graph relationships

USES
OmniRoute
OmniRoute bundles Caveman for token compression
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BUILT BY
JuliusBrussee
caveman is a standalone Go project built by JuliusBrussee for token compression
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BENCHMARKED AGAINST
JetBrains
caveman was benchmarked against JetBrains' test suite of 86 real coding tasks.
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USES
Codex
Codex integrates Caveman skill for output token savings.
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USES
Claude Code
Claude Code integrates Caveman skill for output token savings.
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SUPPORTS
Claude Code
caveman is a skill that optimizes Claude Code token output.
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BUILT BY
Julius Brussee
Julius Brussee created the caveman Claude Code skill.
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BUILT BY
JuliusBrussee
JuliusBrussee built caveman skill reaching 54k stars.
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SUPPORTS
Windsurf
caveman is packaged for Windsurf.
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SUPPORTS
Cursor
caveman is packaged for Cursor.
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SUPPORTS
Gemini
caveman is packaged for Gemini.
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SUPPORTS
Codex
caveman is packaged for Codex editor.
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Source trail

GitHub Headroom (Codepointer)(Firecrawl)30-50% output reduction 24+ tok/s from 30B MoE models on a $200 GTX 1080 build reaching production quality datasette-ip-rate-limit Code w/ Claude 2026 session GitHub GitHub GitHub — JuliusBrussee/caveman

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