coarse-classify

coarse-classify is a skill for Claude Code, Codex 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 CodeCodex

Install

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.

agentmods
npx agentmods add skills/felipe-so/coarse-ink-claude-code/coarse-classify
Any agent
npx skills add Felipe-SO/coarse-ink-claude-code --skill coarse-classify
Clone the repo
git clone --depth 1 https://github.com/Felipe-SO/coarse-ink-claude-code

Made for: Claude Code, Codex.

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
[![agentmods](https://agentmods.dev/badge/skills/felipe-so/coarse-ink-claude-code/coarse-classify.svg)](https://agentmods.dev/skills/felipe-so/coarse-ink-claude-code/coarse-classify)
Your own site
<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>
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. Scan, not verified.
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 $0.00044 $0.01135
Opus 5 $0.00022 $0.00567
Sonnet 5 $0.00009 $0.00227
Haiku 4.5 $0.00004 $0.00113

Measured 5d ago against content hash b85f04e7f404, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 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.

.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. 5d 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 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens