| name | prompt-compress |
| description | Numeric-aware prompt compression for long ENGLISH prose you're about to feed another LLM — meeting transcripts, paper intros/discussion, web-article bodies, logs. Uses LLMLingua-2 (local, CPU, no API) but FIRST triages numeric-sentence density: if the document is number-heavy (FEA results, datasheets, spec tables, PN lists), it REFUSES to compress, because protecting the numbers caps the ratio near 1.3x and blind compression silently corrupts figures ("13,700 MPa"->"13,000"). Number-bearing sentences are protected verbatim (≈100% numeric recall); only the prose between them is compressed. Use when you have a large low-numeric-density English text blob to shrink before sending to a model. Triggers: "이 컨텍스트 압축", "프롬프트 토큰 줄여줘", "긴 문서 압축해서 넣어줘", "llmlingua", "compress this context". NOT for: spec/datasheet/results tables (it'll refuse), Korean text (compressor is English/MeetingBank-trained — quality unverified), or static repeated context (prompt caching is the lossless, cheaper answer there).
|
prompt-compress
Selective, numeric-safe LLM prompt compression with a density triage gate.
When to reach for this
- You have a long English prose blob (transcript, article, paper section, log)
headed into another LLM and want to cut input tokens.
- The text is narrative-heavy, number-light. That's the sweet spot: protecting
numbers costs almost nothing and you still get 2.5–3x.
When NOT to (the gate enforces this)
- Number-dense docs (FEA results, datasheets, MIL-STD tables, BOM/PN lists):
the tool refuses. Protected numeric span becomes a non-compressible floor
(Amdahl) — measured 58%-numeric FEA paper caps at 1.72x ceiling, real ~1.45x.
Just send raw text.
- Static / repeated context (system prompts, CLAUDE.md, fixed docs): use
prompt caching — lossless and cheaper than any compression.
- Korean / non-English: the mBERT compressor is MeetingBank(EN)-trained;
Korean compression quality is unverified. Triage still works; compression risky.
Why the gate exists (measured, not asserted)
Blind LLMLingua-2 on an ankle-FEA paper (394 numeric tokens):
| mode | ratio | numeric recall |
|---|
| blind rate=0.33 | 3.3x | 29.9% (silent loss) |
| protect-numbers rate=0.33 | 1.45x | 100% |
| ceiling (protected verbatim) | 1.72x | 100% |
Blind compression mangled "13,700 MPa" -> "13,000 MPa" with no error. Protection
fixes recall but the ratio collapses because the doc is number-heavy — which is
exactly why triage refuses number-heavy docs instead of pretending to help.
Usage
cd ~/.claude/skills/prompt-compress
[ -d venv ] || (python3 -m venv venv && ./venv/bin/pip install -q llmlingua)
./venv/bin/python compress.py --in doc.txt --triage-only
./venv/bin/python compress.py --in doc.txt --rate 0.33 --out doc.min.txt
cat doc.txt | ./venv/bin/python compress.py --rate 0.5
Flags: --rate keep-rate for prose span (0.33≈3x on the prose part),
--skip-threshold protected-share cutoff (default 0.40), --force compress anyway,
--triage-only report density and stop.
Notes
- CPU-only, fully local, no API cost. First run downloads ~1.1GB mBERT once.
- Protection is sentence-level (coarse): any sentence containing a digit is kept
whole. This is deliberate — fine-grained (number-token-only) protection lifts the
ratio but reintvites boundary-token corruption. Coarse = safe.
- Always read back the
[done] numeric recall line; if <100%, a number leaked into
the compressed prose span — inspect before trusting.
- Output is plain text; feed it as context to the target model.
