Borrowing it
Nothing to install: this file belongs to zzallirog/weighted-compact. 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/zzallirog/weighted-compact/master/CLAUDE.mdgit clone --depth 1 https://github.com/zzallirog/weighted-compactWrote 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/zzallirog/weighted-compact/claude-md)<a href="https://agentmods.dev/instructions/zzallirog/weighted-compact/claude-md"><img src="https://agentmods.dev/badge/instructions/zzallirog/weighted-compact/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/zzallirog/weighted-compact/claude-md"><img src="https://agentmods.dev/badge/instructions/zzallirog/weighted-compact/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 | $0.03490 | $0.03490 |
| Opus 5.5 | $0.01396 | $0.01396 |
| Sonnet 5.5 | $0.00698 | $0.00698 |
| Haiku 4.5 | $0.00349 | $0.00349 |
Grade A, and why
weighted-compact CLAUDE.md scanned grade A with 1 finding 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 2mo ago.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
3. `curl http://127.0.0.1:18890/api/progress` — is the labeler alive? How it starts
The opening of the file, as written. The whole thing — 307 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md — weighted-compact root
Navigator for this repo. If you are Claude (or any LLM assistant) reading this checkout, read this file first and then jump to the relevant subdir. Do not read every file — use the map below.
Repo version: v0.3.0a1 (alpha; baseline run 2026-05-21)
Status: alpha — the prior beta label was premature. Architectural
invariants locked; numbers tunable. The one consumer with a positive,
re-checkable result is recap.py (see README); the importance mixture has no
measured fidelity edge and is documented as such.
Substrate framing
weighted-compact is not a compression tool. It is a substrate for self-distillation from your own Claude Code sessions.
The user's ~/.claude/projects/ already contains a record of every
correction they pushed back on, every number they had to restate, every
constraint the model lost track of. weighted-compact reads those files,
runs them through a pipeline of measurable modules (extract_pairs →
feature_extract → importance mixture → reconstruction-QA), and returns
a compact memory shaped by the user's vocabulary, corrections, and reasoning.
Each module is a defined black box: known input, known output, documented in its own file. The boxes are replaceable independently. The quality metric driving development is reconstruction fidelity (can the compacted context answer questions about what was hidden from it?), not compression ratio.
If you are an LLM reading this repo to answer a question or make a change:
- The framing is "substrate" and "distillation", not "compressor" or "summarizer"
- The pipeline is a sequence of black boxes; treat them as independent
- The locked invariants (vectors-first, CAPTCHA labeling, no-harness-dep) are in the section below — do not propose changes that violate them
TL;DR in 30 seconds
- What this is: a trainable substrate that compacts Claude Code conversation history using vectors first, classifier as a refinement layer, with a CAPTCHA-style labeler for human-in-the-loop tuning.
- Architecture: three independent layers — substrate (
extract_pairs+feature_extractover~/.claude/projects/) → importance mixture (six signals composed continuously, plus a topic-decay multiplier on top) → reconstruction-QA (compression-fidelity gate, default local gemma3 judge with Sonnet 4.6 ground-truth calibration runs reported indocs/05-roadmap.md). Each layer disables cleanly if its dependencies are missing. - Where it runs: localhost only, FastAPI on
:18890, no external services. - Privacy: substrate carries raw conversation text. Stays under
$XDG_DATA_HOME/weighted-compact/, gitignored, never uploaded.
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.
- 2mo ago First seen · 307 lines · 3,490 tokens per session scan A 410e241283a3
weighted-compact CLAUDE.md is an instructions file published in the GitHub repository zzallirog/weighted-compact (5 stars, last pushed 4mo ago), licensed MIT. It adds 3,490 tokens to every session, about $0.0140 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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