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 skills add hdu-ailab/EasyResearch --skill paper-material-packagegit clone --depth 1 https://github.com/hdu-ailab/EasyResearchWrote 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/hdu-ailab/easyresearch/paper-material-package)<a href="https://agentmods.dev/skills/hdu-ailab/easyresearch/paper-material-package"><img src="https://agentmods.dev/badge/skills/hdu-ailab/easyresearch/paper-material-package/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.
<a href="https://agentmods.dev/skills/hdu-ailab/easyresearch/paper-material-package"><img src="https://agentmods.dev/badge/skills/hdu-ailab/easyresearch/paper-material-package.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00063 | $0.01278 |
| Opus 5.5 | $0.00025 | $0.00511 |
| Sonnet 5.5 | $0.00013 | $0.00256 |
| Haiku 4.5 | $0.00006 | $0.00128 |
Grade A, and why
paper-material-package 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 1mo 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Material Package
Purpose
Create ref_papers/paper-notes.md as the readable handoff between Search and
downstream specialists. Keep each paper separate and factual so Writing can
build cross-paper taxonomy and synthesis without guessing what the sources say.
This Skill does not search for candidates, verify metadata by itself, download
PDFs, or convert documents. Use paper-search, arxiv, and
pdf-to-markdown first. It does not write a literature review, compare papers
across the collection, choose a taxonomy, or draft manuscript prose.
Inputs
Inspect these exact-cwd artifacts when available:
ref_papers/source.jsonref_papers/pdf/ref_papers/text/- an existing
ref_papers/paper-notes.md - the dispatch topic, inclusion scope, and expected paper count
Follow another existing layout only when the dispatch explicitly identifies it. Do not infer paper facts from search snippets when readable source text exists.
Stable Paper Keys
Give every selected paper one stable key, using the first available value:
- arXiv id, including version only when version-specific evidence matters;
- DOI;
- OpenReview forum id;
- another verified repository identifier;
- a short normalized title key when no stable identifier exists.
Store the key as note_key on the matching source.json entry and use it in
note headings, citation checks, and later Writing tasks. Preserve every existing
manifest field. Do not invent an identifier.
Output Contract
Maintain one file at ref_papers/paper-notes.md:
# Paper Material Notes
## Material Scope
- Topic:
- Intended use: survey | empirical | hybrid | citation-only
- Requested paper count:
- Selected paper count:
- Full-text-ready count:
- Sources and date range:
- Known coverage gaps:
## <stable-key>: <verified title>
- Citation metadata: <authors; year; venue; DOI/arXiv/OpenReview URL>
- Local sources: <PDF path; readable text path>
- Material status: complete | partial
- Research question: <what problem the paper addresses>
- Method: <factual mechanism or study design>
- Data and evaluation: <datasets, tasks, baselines, metrics, protocol>
- Main findings: <results supported by the source>
- Limitations: <reported or directly evidenced limitations>
- Topic relevance: <why this paper belongs in the requested material set>
- Evidence locators:
- <section/page/table/figure/text heading>: <fact supported there>
- Unresolved material gaps: <none or exact missing evidence>
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.
- 1mo ago First seen · 138 lines · 63 tokens per session scan A 7e01ac5d44cf
paper-material-package is a skill published in the GitHub repository hdu-ailab/EasyResearch (14 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 1,278 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
overleaf
Sync and manage Overleaf LaTeX projects from the command line. Pull projects locally, push changes back, compile PDFs, and download compile outputs like .bbl files for arXiv submissions. Use when working with LaTeX, Overleaf, academic papers, or arXiv.
ai-research-reproduction
Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted…
alterlab-qiime2-amplicon
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier…
alterlab-paper-writer
Drafts and revises academic papers through a 12-agent pipeline with hardened LaTeX output (apa7 class, PDF compiled from LaTeX), supporting IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures, APA 7.0 (default), Chicago, MLA, IEEE, and Vancouver citation formats, bilingual…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-pyhealth
Develops, tests, and validates clinical machine learning models with the PyHealth 2.x healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, length of stay, drug recommendation), medical coding systems (ICD, NDC, ATC, CCS), physiological signals…