Chisle

Chisle

Your AI talks less, builds less, reads less, and says more. Like a senior dev who bills by the syllable.

The only tool in this class that publishes the runs where it lost. Here's why.

Built for Claude Code: coding answers come back 33% shorter and 24% cheaper, while caveman and ponytail make them longer · 12 agents · zero dependencies · one command

Chisle is a Claude Code plugin that makes Claude cheaper to run without making it dumber. It cuts what Claude writes: no filler, no hedging, no speculative abstractions, just the smallest code that works. It also cuts what Claude reads: a PostToolUse hook trims oversized tool output by ~46% before it re-enters the context window, where it would be re-billed on every later request. In agent-loop tests Claude with Chisle passes the same tasks as Claude without it. One npx chisle installs it, and the same ruleset ships to Pi, Cursor, Codex, Gemini, Copilot, OpenCode, Antigravity and four more agents. Every number here comes from committed raw transcripts, including the runs where it lost.

Claude Code 2.1.285 on Haiku 4.5, the 14 coding cells of a 26-cell run. Across all 26 prompts Chisle bills 83% where caveman bills 102% and ponytail 105%, the only one under 100%; on short answers it breaks even. Full numbers below.

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"Add debounce to a search input that currently fires an API call on every keystroke." Same model, same prompt, one difference: the injected ruleset. Both answers below are the verbatim committed output from benchmarks/results/raw/:

bare agent: 142 lines, 1506 tokensChisle: 35 lines, 602 tokens

Opens with "Let me show you the most common approaches", then ships a reusable generic useDebounce<T> hook in its own file…

// useDebounce.ts
export function useDebounce<T>(
  value: T, delay: number
): T {
  const [debouncedValue, setDebouncedValue]
    = useState<T>(value);
  useEffect(() => { /* … */ }, [value, delay]);
  return debouncedValue;
}

…then Option 2 and Option 3, a comparison table, and a caveats section.

Asks which framework, then answers the question that was actually asked: setTimeout in the effect you already have, no new file, no generic:

useEffect(() => {
  const timer = setTimeout(async () => {
    if (query.trim()) { /* fetch */ }
  }, 300);
  return () => clearTimeout(timer);
}, [query]);

Then two lines on why it works, and "use lodash.debounce if already installed."

Not golfed, boring. Same behaviour, one less abstraction, no second file, and it names the dependency you might already have instead of reinventing it.

What it compresses

axis	what	how
Output: prose	filler, hedging, manufactured structure	zero-fluff ruleset, injected per session
Output: code	speculative abstractions, unrequested boilerplate	YAGNI efficiency ladder
Input: context	oversized tool output flooding the window	Claude PostToolUse / Pi tool_result: scrub, elide, dedup, plus prevention rules

Where each one attaches to a session:

flowchart LR
    subgraph S["Session start"]
        H1["Ruleset injectiononce per active session"]
    end
    subgraph T["Every turn"]
        H2["Mode trackingClaude + Pi"]
    end
    subgraph L["Every tool call"]
        H3["PostToolUse / tool_resultscrub → elide → dedup"]
    end

    H1 --> M(["Model"])
    H2 --> M
    M -->|writes| O["Output:terser prose,YAGNI-first code"]
    M -->|calls a tool| TOOL[["Bash / grep / web / extension tools"]]
    TOOL -->|raw output| H3
    H3 -->|"compressed, rebuilt intothe tool's own shape"| M

    RE["Read / Edit / Write"] -.->|"never touched,exact bytes feed later edits"| M

    style M fill:#1f2937,stroke:#d78a3c,color:#e6edf3
    style O fill:#14532d,stroke:#2da44e,color:#e6edf3
    style H3 fill:#1f2937,stroke:#2da44e,color:#e6edf3
    style RE fill:#3f1d1d,stroke:#cf3b3b,color:#e6edf3

The loop on the right is the input axis: tool output is billed again on every later request in the session, so shrinking it once pays repeatedly. Read, Edit, and Write are deliberately outside it.

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Install

One command. Auto-detects your agents (Claude Code, Pi, Cursor, Windsurf, Cline, Kiro, Antigravity, Codex, Gemini, Copilot, OpenCode, Hermes) and wires each one. --uninstall puts everything back.

npx chisle

# or via curl
curl -fsSL https://raw.githubusercontent.com/JayPokale/Chisle/main/install.sh | bash

# Windows
irm https://raw.githubusercontent.com/JayPokale/Chisle/main/install.ps1 | iex

Preview first with npx chisle --dry-run, scope with --only claude or --only pi, see everything with npx chisle --help. Remove with npx chisle --uninstall.

Upgrading, per-agent setup, --stats and config: docs/usage.md.

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Benchmarks

Claude Code 2.1.285 on Haiku 4.5, 13 live prompts × 2 seeds = 26 cells per arm, billed output tokens vs the same model with no ruleset (writeup + raw cells):

	total bill	95% CI	visible answer	worst cell	backfires
caveman	102%	79–128%	105%	305%	15 / 26
ponytail	105%	86–129%	97%	493%	16 / 26
Chisle	83%	69–95%	76%	170%	11 / 26

Chisle is the only arm below a bare model. It pays on long answers (77%) and coding prompts (76%); on short answers it breaks even (106%).

On Pi + GPT-5.5 Chisle again gives the shortest answers, 37% the length of a bare model's (caveman 56%, ponytail 49%), though on total billed tokens ponytail edges it, 59% to 61%:

Input side: tool output is 67.5% of context in 171 measured Claude Code sessions, and it is re-billed on every later request. The compressor cut ~46% off every eligible output there, and 27.4% of tool out