chisle

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.

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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/:

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

Where each one attaches to a session:

flowchart LR
    subgraph S["Session start"]
        H1["Ruleset injection<br/>once per active session"]
    end
    subgraph T["Every turn"]
        H2["Mode tracking<br/>Claude + Pi"]
    end
    subgraph L["Every tool call"]
        H3["PostToolUse / tool_result<br/>scrub → elide → dedup"]
    end

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

    RE["Read / Edit / Write"] -.->|"never touched,<br/>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):

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 cu