Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/felipe-so/coarse-ink-claude-code/coarse-classifynpx skills add Felipe-SO/coarse-ink-claude-code --skill coarse-classifygit 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-classify)<a href="https://agentmods.dev/skills/felipe-so/coarse-ink-claude-code/coarse-classify"><img src="https://agentmods.dev/badge/skills/felipe-so/coarse-ink-claude-code/coarse-classify.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 | $0.00044 | $0.01135 |
| Opus 5 | $0.00022 | $0.00567 |
| Sonnet 5 | $0.00009 | $0.00227 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
coarse-classify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/coarse-classify — Classify, Calibrate, Contribute, Literature
Usage: /coarse-classify <slug>
Argument ($ARGUMENTS) is the paper slug (e.g. my-paper). All paths are relative
to the workspace root d:/Dropbox/Research/Coarse Reviewer/.
Path setup
EXTRACTED=.coarse_cache/<slug>_extracted.mdSECTIONS_JSON=.coarse_cache/<slug>_sections.jsonCLASSIFICATION=.coarse_cache/<slug>_classification.jsonCALIBRATION=.coarse_cache/<slug>_calibration.jsonCONTRIBUTION=.coarse_cache/<slug>_contribution.jsonLITERATURE=.coarse_cache/<slug>_literature.txt
Read SECTIONS_JSON and the first 2000 chars of EXTRACTED before starting.
Step 3 — Classify
Determine:
- title: exact paper title from the first page
- domain: e.g.
social_sciences/economics,computer_science/machine_learning,statistics/causal_inference,natural_sciences/biology - taxonomy: e.g.
academic/research_paper,academic/review_paper,academic/working_paper - abstract: the paper's abstract text
- math_sections: list of section indices (0-based) whose
math_contentshould betrue. A section needs math verification if it contains ANY of: proofs (formal or informal), theorem/lemma/proposition/corollary statements with arguments, formal definitions or assumptions, algebraic manipulations, estimator definitions, asymptotic expressions.
Save to CLASSIFICATION using the Write tool:
{
"title": "...",
"domain": "...",
"taxonomy": "...",
"abstract": "...",
"math_sections": [0, 2, 4]
}
Step 4 — Domain Calibration
Using the paper's title, domain, abstract, and section list, produce a domain-specific review calibration.
You are an expert academic reviewer. For each field below, provide 3-5 concise items tailored to this paper's specific domain and methodology:
- methodology_concerns: The key methodological concerns for this type of paper
- assumption_red_flags: Assumptions that commonly fail in this domain
- what_not_to_check: What is irrelevant for this paper type
- evaluation_standards: What a top-tier journal in this field expects
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.
- 5d ago First seen · 131 lines · 44 tokens per session scan A b85f04e7f404
coarse-classify is a skill published in the GitHub repository Felipe-SO/coarse-ink-claude-code (2 stars, last pushed 4mo ago), licensed MIT. It adds 44 tokens to every session and 1,135 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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