JuliusBrussee/caveman

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Skill	Same answer	Tokens
`/caveman`	New object ref each render, so React re-renders. Wrap the prop in `useMemo`.	20
`/ultracave`	Inline object prop, new ref, re-render. `useMemo`.	14
`/megacave`	新參照致重繪。`useMemo`。	13

Rock	What it does	Measured
Say less	Agent answers short. Code stays exact	63 → 20 tokens, same fix
Read less	Logs, JSON, CSV, test output shrink before the model reads them	33.2% fewer input tokens per session
Auto · new	Picks the model and the effort for each ask	54% cheaper than Opus at xhigh, same score
Blocks · new	Code mode: agent keeps the scripts it writes and runs them again	397 → 76 tokens for one test run
Waste fixes	Finds where your tokens go and fixes it. New: keeps the prompt cache warm while you pause	Not measured yet
Browse	Web pages come back compressed	129.8× smaller page

Who	Setup	Result
Adobe Research, CAVEWOMAN paper (cites this repo)	Caveman-style output, eight models, five datasets	Cost cut 1.4 to 2.4× per model, up to 3×
Elastic, Elasticsearch Labs	Caveman mode remade for Elasticsearch, eight live MCP scenarios	63.6% fewer response tokens. "Zero information loss."
JetBrains	86 real coding tasks, paired A/B	No measurable quality loss (p = 0.82). 8.5% fewer output tokens

Rule	What it means
Answer first	`[thing] [action] [reason]. [next step].` No greeting, no "let me", no recap
One idea per sentence	Built on ASD-STE100, the controlled English of aircraft maintenance manuals: 20 words max, active voice, one term per thing
Meaning never dropped	Articles can go. not, never, no, only never go. Numbers and units stay exact
Payload verbatim	Code, commands, paths, and error messages untouched, character for character
No theater	No "me think". No chatter between tool calls. If caveman phrasing isn't shorter, plain wins
Knows when to stop	Security warnings, irreversible actions, and confused users get full sentences. Then grunt resumes
Your prompts stay yours	Never rewritten. Research say that backfire

why many token when few do trick

File type	The file, through caveman	File saved	Whole Claude Code session, 3 runs	Session saved
CSV	28,041 → 314	98.9%	165,823 → 74,484	55.1%
Logs	22,810 → 348	98.5%	148,807 → 74,068	50.2%
YAML	20,447 → 178	99.1%	132,124 → 71,027	46.2%
Test output	18,806 → 203	98.9%	150,377 → 108,514	27.8%
JSON	18,837 → 281	98.5%	147,975 → 108,939	26.4%
HTML	21,670 → 21,670	none yet	140,687 → 154,641	9.9% worse
All six	130,611 → 22,994	82.4%	885,793 → 591,673	33.2%

Setup	Tasks solved	Cost per try	Median time
Opus 5.5, xhigh effort, every ask	44.3%	$7.83	35.2 min
Auto, Claude models only	43.7%	$3.59	15.9 min
Auto, plus OpenAI and open models	42.9%	$3.47	16.7 min

Input	Raw	What agents type now	Block
`go test -v ./...`, 326 tests	10,561	397 · `\| tail -20`	76 · `test-summary`
1.4 MB JSON file	251,893	236 · their own heredoc	227 · `json-peek`
366-line Go file	3,111	250 · `grep -n`	489 · `grep-defs`

Rock	What it does	Measured
`caveman learn`	Ranks where your tokens go, from agent history already on disk. `caveman learn implement` applies fixes one diff at a time, only on your yes	Not yet
Warm cache · new	Step away and the provider forgets your session, so the next ask pays full price. Caveman keeps Anthropic's prompt cache warm while you pause, only when your own history says you come back in time. Every warm is listed in `caveman stats`	Not yet
`caveman browse <url>`	A compressed web page for the agent	121 tokens instead of 15,704 for a Playwright snapshot of a 200-row table. Tiny forms lose 2.3×. Bench
`/caveman-compress <file>`	Shrinks memory files like `CLAUDE.md`, keeps a backup	4,138 tokens instead of 6,198 across five files, 22.8% to 49.1% each, every heading, code block, and path intact. Bench
Middleware	Same shrinking inside the Vercel AI SDK, LangChain, OpenAI, or Anthropic call you already make. TypeScript · Python · Every framework · One container for the team	Not yet

Command	What it does
`/caveman` · `/ultracave` · `/megacave`	The voice, the grunt, the 文言文. `/caveman status` shows the mode, `/caveman off` stops it
`/caveman-commit`	One-line Conventional Commit
`/caveman-review`	One finding per line: `L42: 🔴 null deref. Guard it.`
`/caveman-compress <file>`	Shrinks memory files and backs up the original
`/caveman-stats`	Real token usage for this Claude Code session
`/caveman-help`	Every mode and command on one screen
`cavecrew`	Subagents that find, edit, and review code, then report back in caveman
`investigate-first` · `lean-build` · `surgical-patch` · `safe-refactor` · `migration` · `verify-and-stop`	Work patterns that write less code. Your agent picks them up when a task fits
`caveman-setup` · `caveman-discover` · `caveman-learn` · `caveman-manage` · `caveman-optimize` · `caveman-explore` · `caveman-evidence-review`	Drive the proxy from inside your agent

Cloud does	What you get
Observability	Every run traced to the work and what it cost. Sets itself up from your repo, your keys, and the logs or OpenTelemetry you already have
Evals	Grades how good the answers are, on your own tasks, from your real traffic
Autopilot	Proposes a cheaper change, tests it on your recorded work, ships only when the evals say answers are still as good. You approve the pull request
Proof	Every number says if it is measured, inferred, or verified. A saving counts only after real traffic shows it