coarse-ink-claude-code: Skill for Claude Code

.claude/skills/coarse-classify/SKILL.md

coarse-classify is a skill for Claude Code from Felipe-SO/coarse-ink-claude-code. It costs 44 tokens per session (1,135 once invoked), scanned A, original, MIT.

A research workflow for classifying academic papers, extracting their contributions, and finding related literature.

In plain words
What is it for?
Use it to classify a paper's field and type, mark sections containing mathematics, summarize contributions, and cache related-literature searches.
Why use it?
It organizes early paper review work into saved files, reducing repeated reading and keeping classification, calibration, contributions, and literature searches together.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

This is Felipe-SO/coarse-ink-claude-code's own configuration. It tells Claude Code how to work on coarse-ink-claude-code itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything coarse-ink-claude-code configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/Felipe-SO/coarse-ink-claude-code/main/.claude/skills/coarse-classify/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Felipe-SO/coarse-ink-claude-code

Made for: Claude Code.

Wrote 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.

agentmods badge for coarse-classify

README.md
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Your own site
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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.

agentmods 80×15 button for coarse-classify

Your own site · 80×15
<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="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,135 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.01135
Opus 5.5 $0.00018 $0.00454
Sonnet 5.5 $0.00009 $0.00227
Haiku 4.5 $0.00004 $0.00113

Measured 2mo ago against content hash b85f04e7f404, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 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.

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.

.claude/skills/coarse-classify/SKILL.md · 131 lines

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.md
  • SECTIONS_JSON = .coarse_cache/<slug>_sections.json
  • CLASSIFICATION = .coarse_cache/<slug>_classification.json
  • CALIBRATION = .coarse_cache/<slug>_calibration.json
  • CONTRIBUTION = .coarse_cache/<slug>_contribution.json
  • LITERATURE = .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_content should be true. 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:

  1. methodology_concerns: The key methodological concerns for this type of paper
  2. assumption_red_flags: Assumptions that commonly fail in this domain
  3. what_not_to_check: What is irrelevant for this paper type
  4. evaluation_standards: What a top-tier journal in this field expects

Read the full file on GitHub · 131 lines

Changes

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.

  1. 2mo ago First seen · 131 lines · 44 tokens per session scan A b85f04e7f404

Subscribe to this mod's changes

coarse-classify is a skill published in the GitHub repository Felipe-SO/coarse-ink-claude-code (2 stars, last pushed 6mo ago), licensed MIT. It adds 44 tokens to every session and 1,135 once invoked, about $0.0002 per session on Opus 5.5. 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.

Related

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coarse-classify next to 6 skills from other repositories
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