easypaper-paper-from-metadata

easypaper-paper-from-metadata is a skill for Claude Code from PinkGranite/EasyPaper. It costs 29 tokens per session (1,673 once invoked), scanned A, original, Apache-2.0.

A tool for generating a complete academic paper from a JSON metadata file, which is a structured file describing the study and its materials.

In plain words
What is it for?
It helps create papers from prepared metadata, including the title, hypothesis, method, data, experiments, references, figures, tables, and optional formatting settings.
Why use it?
It provides a repeatable way to turn study details, research files, figures, tables, and references into a generated paper.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the easypaper plugin — 5 skills, 5 commands shipped together

Good fit It helps create papers from prepared metadata, including the title, hypothesis, method, data, experiments, references, figures, tables, and optional formatting settings.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pinkgranite/easypaper/easypaper-paper-from-metadata
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.

Any agent
npx skills add PinkGranite/EasyPaper --skill easypaper-paper-from-metadata
Clone the repo
git clone --depth 1 https://github.com/PinkGranite/EasyPaper

Made for: Claude Code.

Or install easypaper, the plugin that ships this one along with the rest of its 5 skills, 5 commands.

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 easypaper-paper-from-metadata

README.md
[![agentmods](https://agentmods.dev/badge/skills/pinkgranite/easypaper/easypaper-paper-from-metadata/github.svg)](https://agentmods.dev/skills/pinkgranite/easypaper/easypaper-paper-from-metadata)
Your own site
<a href="https://agentmods.dev/skills/pinkgranite/easypaper/easypaper-paper-from-metadata"><img src="https://agentmods.dev/badge/skills/pinkgranite/easypaper/easypaper-paper-from-metadata/github.svg" alt="Measured on agentmods" height="20"></a>

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 easypaper-paper-from-metadata

Your own site · 80×15
<a href="https://agentmods.dev/skills/pinkgranite/easypaper/easypaper-paper-from-metadata"><img src="https://agentmods.dev/badge/skills/pinkgranite/easypaper/easypaper-paper-from-metadata.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,673 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 $0.00029 $0.01673
Opus 5 $0.00015 $0.00837
Sonnet 5 $0.00006 $0.00335
Haiku 4.5 $0.00003 $0.00167

Measured 12d ago against content hash e8d9297f8b13, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

easypaper-paper-from-metadata 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 12d 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.

plugins/easypaper/skills/easypaper-paper-from-metadata/SKILL.md · 189 lines

How it starts

The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Use this skill when the user wants to generate an academic paper from metadata. It handles both metadata collection and paper generation in one workflow.

Have the user prepare a metadata JSON file that follows examples/meta.json. Treat that file as a schema/template reference, not as a runnable paper. For a runnable project-local sample, use examples/template/meta.json.

Load JSON as PaperGenerationRequest, then convert it to SDK inputs:

import json
from pathlib import Path
from easypaper import EasyPaper, PaperGenerationRequest

metadata_path = Path("metadata.json").resolve()
raw = json.loads(metadata_path.read_text(encoding="utf-8"))

def metadata_relative_path(value: str) -> str:
    candidate = Path(value).expanduser()
    if candidate.is_absolute():
        return str(candidate)
    return str((metadata_path.parent / candidate).resolve())

if not raw.get("materials_root"):
    raw["materials_root"] = str(metadata_path.parent)
if raw.get("template_path"):
    raw["template_path"] = metadata_relative_path(raw["template_path"])
if (
    isinstance(raw.get("code_repository"), dict)
    and raw["code_repository"].get("type") == "local_dir"
    and raw["code_repository"].get("path")
):
    raw["code_repository"]["path"] = metadata_relative_path(raw["code_repository"]["path"])

request = PaperGenerationRequest.model_validate(raw)
paper_metadata = request.to_metadata()
options = {
    "output_dir": request.output_dir,
    "save_output": request.save_output,
    "compile_pdf": request.compile_pdf,
    "figures_source_dir": request.figures_source_dir,
    "target_pages": request.target_pages,
    "enable_review": request.enable_review,
    "max_review_iterations": request.max_review_iterations,
    "enable_planning": request.enable_planning,
    "enable_exemplar": request.enable_exemplar,
    "enable_vlm_review": request.enable_vlm_review,
    "enable_user_feedback": request.enable_user_feedback,
    "artifacts_prefix": request.artifacts_prefix or "",
}

ep = EasyPaper(config_path=str(Path("easypaper_config.yaml").resolve()))
result = await ep.generate(paper_metadata, **options)

Read the full file on GitHub · 189 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. 12d ago First seen · 189 lines · 29 tokens per session scan A e8d9297f8b13

Subscribe to this mod's changes

easypaper-paper-from-metadata is a skill published in the GitHub repository PinkGranite/EasyPaper (5 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 1,673 once invoked, about $0.0001 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

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

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

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