Borrowing it
Nothing to install: this file belongs to Felipe-SO/coarse-ink-claude-code. 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/Felipe-SO/coarse-ink-claude-code/main/.claude/skills/coarse-extract/SKILL.mdgit clone --depth 1 https://github.com/Felipe-SO/coarse-ink-claude-codeWrote 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/skills/felipe-so/coarse-ink-claude-code/coarse-extract)<a href="https://agentmods.dev/skills/felipe-so/coarse-ink-claude-code/coarse-extract"><img src="https://agentmods.dev/badge/skills/felipe-so/coarse-ink-claude-code/coarse-extract.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 | $0.00041 | $0.00694 |
| Opus 5 | $0.00020 | $0.00347 |
| Sonnet 5 | $0.00008 | $0.00139 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
coarse-extract scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/coarse-extract — Extract and Parse Structure
Usage: /coarse-extract papers/paper.pdf
Argument ($ARGUMENTS) is the path to the paper. All paths are relative to the
workspace root d:/Dropbox/Research/Coarse Reviewer/.
Path setup
Derive a slug from the filename: lowercase, spaces→hyphens, no extension.
e.g. papers/my-paper.pdf → slug = my-paper
PAPER_PATH=$ARGUMENTSEXTRACTED=.coarse_cache/<slug>_extracted.mdSECTIONS_JSON=.coarse_cache/<slug>_sections.json
Create .coarse_cache/ if it doesn't exist.
Step 1 — Extract (Python)
Run:
python scripts/coarse_extract.py PAPER_PATH EXTRACTED
This writes the full paper as markdown to EXTRACTED. Uses Mistral OCR via
OpenRouter if OPENROUTER_API_KEY is set; otherwise falls back to pymupdf.
If extraction fails, try the pymupdf fallback directly:
python -c "
import fitz, sys
doc = fitz.open(sys.argv[1])
text = '\n\n'.join(page.get_text() for page in doc)
open(sys.argv[2], 'w', encoding='utf-8').write(text)
" PAPER_PATH EXTRACTED
Then read EXTRACTED into your context with the Read tool.
Garble check: After reading EXTRACTED, scan the first 3000 characters for
OCR artifacts — spaced-out characters (e.g. T h e o r e m), garbled symbols
(ˆ b, ∈ˆ, HTML entities like <), or lines that are clearly noise.
If you find more than a few isolated artifacts, warn the user: the PDF may have
been scanned or image-only. The downstream review steps include an OCR-artifact
notice to the reviewer, but severe garbling will hurt review quality. Advise
the user to check whether a text-native PDF is available.
Step 2 — Parse Structure (Python)
Run:
python scripts/coarse_structure.py EXTRACTED SECTIONS_JSON
This writes a JSON array of section objects to SECTIONS_JSON. Each section has:
number,title,text,section_type(abstract/introduction/related_work/ methodology/results/discussion/conclusion/appendix/references/other)math_content(bool — set tofalseby this script; will be set in classify step)claims(list of theorem/lemma/proposition statements found by regex)definitions(list of definition statements found by regex)
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.
- 7d ago First seen · 79 lines · 41 tokens per session scan A 5fc357fd332b
coarse-extract is a skill published in the GitHub repository Felipe-SO/coarse-ink-claude-code (2 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 694 once invoked, about $0.0002 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 2026-08-31.
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