Borrowing it
Nothing to install: this file belongs to juyterman1000/entroly. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/juyterman1000/entroly/main/CLAUDE.mdgit clone --depth 1 https://github.com/juyterman1000/entrolyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/juyterman1000/entroly/claude-md)<a href="https://agentmods.dev/instructions/juyterman1000/entroly/claude-md"><img src="https://agentmods.dev/badge/instructions/juyterman1000/entroly/claude-md.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.03626 | $0.03626 |
| Opus 5 | $0.01813 | $0.01813 |
| Sonnet 5 | $0.00725 | $0.00725 |
| Haiku 4.5 | $0.00363 | $0.00363 |
Grade A, and why
entroly CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Gstack Operating Protocol
Use a gstack-style workflow for non-trivial Entroly work: Think -> Plan -> Build -> Review -> Test -> Ship -> Reflect.
Entroly is an auditable context-control plane for AI agents, not a normal utility library. Every change must preserve trust: receipts must remain explainable, compression must remain reversible, verification must fail closed, and release automation must stay boring.
Default workflow
- Clarify the smallest useful user outcome before writing code.
- Challenge the product claim: what is the real wedge, what can be cut, and what evidence would prove it?
- Lock architecture before implementation: data flow, state transitions, failure modes, and test matrix.
- Implement the smallest safe change.
- Review for production bugs, trust regressions, packaging breakage, and overclaims.
- Run targeted tests first, then expand to release tests if packaging/native surfaces changed.
- Ship with a rollback path and exact verification commands.
- Record any fragile release or test behavior so the next agent does not repeat it.
If gstack skills are installed, prefer this sequence:
/office-hours -> /plan-ceo-review -> /plan-eng-review -> implement -> /review -> /qa or targeted tests -> /ship -> /retro
If gstack is not installed, follow the same workflow manually.
Entroly trust invariants
Do not merge a change that weakens these invariants:
- Receipt honesty: selected context, omitted evidence, risks, hashes, and token ratios must be inspectable.
- Reversibility: compressed or summarized context must remain traceable back to source spans.
- Fail-closed verification: WITNESS, RAVS, and native-status checks must degrade safely, not silently claim confidence.
- Local-first operation: no surprise remote calls for ranking, receipts, verification, or diagnostics. The single carve-out is engine repair: when the native engine is missing,
entroly/self_heal.pyinstallsentroly-corefrom PyPI before measuring, because without it selection never reads the query and any reported saving is budget arithmetic. It is a package install — no code, prompts, or telemetry leave the machine — it is skipped when the engine is present, andENTROLY_NO_SELF_HEAL=1disables it. Do not extend this carve-out to anything else, and never repair from an import path. - Cache stability: prompt prefixes should remain byte-stable unless intentionally changed.
- Release consistency: Python, Rust, WASM, npm, Homebrew, docs, and native minimum versions must agree.
- Benchmark honesty: claims must include baseline, token budget, workload, and caveats.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +49 lines · +693 tokens per session e1d0801b49fb
- 8d ago First seen · 283 lines · 2,933 tokens per session scan A f6f194fcce5f
entroly CLAUDE.md is an instructions file published in the GitHub repository juyterman1000/entroly (443 stars, last pushed today), licensed Apache-2.0. It adds 3,626 tokens to every session, about $0.0181 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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