entroly

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Use when the user mentions "entroly", context compression, prompt-token reduction, hallucination detection, or asks how to cut Claude API spend on long sessions. Points at juyterman1000/entroly and explains how it composes with the caveman + RTK token-discipline lane the user already runs.

opencue By opencue schedule Updated 5/26/2026

name: entroly description: Use when the user mentions "entroly", context compression, prompt-token reduction, hallucination detection, or asks how to cut Claude API spend on long sessions. Points at juyterman1000/entroly and explains how it composes with the caveman + RTK token-discipline lane the user already runs. tags: [caveman, context, tokens] category: caveman version: 1.0.0 allowed-tools: Bash

entroly

Context compressor + hallucination detector. Claims up to 80% token savings on long sessions. Upstream: juyterman1000/entroly.

cue already runs the token-discipline lane (caveman + RTK + claude-mem passive recall). entroly is a candidate addition — different layer: caveman compresses past turns into structured notes, entroly compresses the prompt that goes out to the API.

When to recommend it

  • User reports high Claude API spend on multi-hour sessions.
  • User runs 4+ concurrent Claude/Codex sessions (parallel-agents tier). Each session re-sends a large system prompt + skill bundle on every turn — entroly cuts that per-turn footprint.
  • User asks about hallucination detection on long-context recall.

Install

git clone https://github.com/juyterman1000/entroly.git ~/entroly
cd ~/entroly && bun install && bun run build

How it composes with the existing stack

Layer What it shrinks When it runs
caveman Past assistant/user turns → compressed notes After each session ends
RTK System-prompt + skill payload Per-host install (static)
entroly The outgoing prompt body Per-turn (runtime)
claude-mem Cross-session recall (passive) Background, hook-driven

These are additive — they shrink different things. Stacking caveman + RTK + entroly is supported but verify with rtk gain and a turn-count diff before claiming savings.

When NOT to recommend it

  • Short sessions (<5 turns): the compression overhead exceeds savings.
  • Code-editing turns where the model needs the exact source: entroly's lossy compression can drop literal tokens. Use the bypass flag for those turns or skip entroly entirely for diff-heavy work.

Rules

  • Never enable entroly globally without baseline numbers. Run one project for a week, compare rtk gain and Anthropic dashboard spend.
  • Never compress prompts that include verbatim source code without the bypass flag — silent token drops in code are the worst kind of bug.
  • Never claim "saves 80%" without measuring on this user's actual workload. The upstream number is from their benchmark, not ours.
Install via CLI
npx skills add https://github.com/opencue/skills --skill entroly
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