Research Writing
alfonso0512/research-writing-skill
科研论文写作助手，提供 30 个 Prompt 模板覆盖论文写作全流程. An agent skill from alfonso0512/research-writing-skill.
Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation…
Install General Fair Data Review
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-fair-data-review --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/general-fair-data-review .claude/skills/general-fair-data-review && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "general-fair-data-review" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-review into .claude/skills/general-fair-data-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-fair-data-review", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-reviewType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-fair-data-review --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/general-fair-data-review .agents/skills/general-fair-data-review && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "general-fair-data-review" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-review into .agents/skills/general-fair-data-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-fair-data-review", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-fair-data-review --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/general-fair-data-review .cursor/skills/general-fair-data-review && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "general-fair-data-review" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-review into .cursor/skills/general-fair-data-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-fair-data-review", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/general-fair-data-review--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-fair-data-review --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/general-fair-data-review .gemini/skills/general-fair-data-review && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "general-fair-data-review" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-review into .gemini/skills/general-fair-data-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-fair-data-review", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install learningmatter-mit/AtomisticSkills general-fair-data-reviewInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/general-fair-data-review .github/skills/general-fair-data-review && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "general-fair-data-review" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-review into .github/skills/general-fair-data-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-fair-data-review", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-fair-data-review --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/general-fair-data-review .opencode/skills/general-fair-data-review && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "general-fair-data-review" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-fair-data-review into .opencode/skills/general-fair-data-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-fair-data-review", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
general-fair-data-review- GitHub stars
- 176
- Token cost
- ~2.3k tokens
- SKILL.md length
- 630 words
- Files
- 1
- Skills in repo
- 129
- Repo updated
- First seen
- Licence
- MIT
At a glance
Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation…
- Works in 6 steps: Identify Review Scope → Read the Manuscript and Data… → Inspect the Data/Code Repository → …
- Documents & Office work in your project
- SKILL.md covers Goal, Prerequisites, Instructions and Document-Specific Workflows, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
General Fair Data Review is an agent skill from learningmatter-mit/AtomisticSkills. Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation steps.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Documents & Office. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
When your agent uses it
- Documents & Office work in your project
Example prompts
- “/general-fair-data-review”
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 6257444. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
doi.orgrsc.orggithub.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
General Fair Data Review loads about 2.3k tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 630 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 630 words, ~2,256 tokens.
Download SKILL.mdSave it as
.claude/skills/general-fair-data-review/SKILL.md (or your agent's skills folder).- name
- general-fair-data-review
- description
- Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation steps.
- metadata.category
- general
General FAIR Data Review
Goal
Assess whether a manuscript submission or standalone code/data repository satisfies the FAIR Guiding Principles (Wilkinson et al., Sci. Data 2016). The output is a structured reviewer report — analogous to a peer-review report — that scores each FAIR sub-principle, identifies gaps, and provides concrete remediation steps the authors can act on before publication.
This skill is complementary to general-peer-review, which focuses on scientific methodology. Run both in sequence for a complete review.
Prerequisites
- A manuscript (PDF or markdown) and/or a link to a code/data repository (GitHub, Zenodo, Figshare, etc.)
- Read access to any supplementary files, Data Availability Statements (DAS), or README files provided by the authors
Instructions
1. Identify Review Scope
Determine what artifacts are under review. Three modes exist:
| Mode | Input | Focus |
|---|---|---|
| Manuscript + data/code | PDF + repo URL | Full FAIR review |
| Manuscript only | DAS quality, metadata richness, identifier presence | |
| Code/data repo only | Repo URL / directory | Repository-level FAIR compliance |
State the mode explicitly at the start of the review report.
2. Read the Manuscript and Data Availability Statement
Load the manuscript. Locate and extract:
- The Data Availability Statement (DAS) — usually a dedicated section near the end.
- Any Code Availability Statement.
- All data/code repository URLs or DOIs mentioned.
If no DAS exists, flag immediately as a Critical Finding (fails F4, A1, R1.1).
3. Inspect the Data/Code Repository
For each repository URL found, check the following. If no repository exists, mark all sub-principles below as Fail.
Repository inspection checklist:
- Does a persistent identifier (DOI, Handle) exist? → F1
- Is metadata present and rich (title, authors, description, keywords, license)? → F2, R1
- Does the metadata explicitly reference the dataset/code identifier? → F3
- Is the repository indexed in a searchable resource (Zenodo, Figshare, OSF, etc.)? → F4
- Can the data/code be accessed via a standard protocol (HTTP/HTTPS, FTP)? → A1
- Is the protocol open and free (no proprietary portal login required)? → A1.1
- If restricted, is there a documented access procedure? → A1.2
- Does metadata remain accessible even if data is removed? → A2
- Are standard, community-recognized formats used (CIF, JSON, CSV, HDF5, not .xlsx or proprietary)? → I1
- Are domain ontologies or controlled vocabularies used for metadata fields? → I2
- Are cross-references to related datasets or publications included? → I3
- Is a clear, machine-readable license present (CC-BY, MIT, Apache 2.0, etc.)? → R1.1
- Is provenance documented (how data was generated, software versions, parameters)? → R1.2
- Do files conform to domain community standards (e.g., CIF for crystal structures, SMILES for molecules, HDF5 for trajectories)? → R1.34. Score Each FAIR Sub-Principle
For every sub-principle (F1–F4, A1–A2, I1–I3, R1–R1.3) assign:
- Pass — requirement fully met
- Partial — requirement partially met; improvement needed
- Fail — requirement not met or artifact absent
- N/A — not applicable to this submission type
5. Atomistic/Computational Science Specific Checks
In addition to the generic FAIR checklist, evaluate the following domain-specific criteria:
Structures & Trajectories
- Crystal structures deposited as
.cif(not as images or in supplementary PDF tables) - MD trajectories deposited in open formats (
.xyz,.extxyz,.h5md,.lammpsdump) with aREADMEspecifying units, timestep, ensemble - Force field / MLIP checkpoints deposited with version, training set provenance, and validation metrics
Computational Parameters
- DFT: INCAR/POTCAR/KPOINTS or equivalent included or described with exact values (not "similar to ref. X")
- MLIP: model architecture, training hyperparameters, and train/val/test split recorded
- MD: timestep, thermostat/barostat settings, equilibration protocol documented
Software Environment
environment.ymlorrequirements.txtpresent with pinned versions- Scripts runnable from the deposited repository without undocumented external dependencies
Show full SKILL.md (242 more words)Show less
6. Generate the Structured Review Report
Produce a report in the following format:
# FAIR Data Review Report
**Manuscript title:** [title]
**Review date:** [date]
**Reviewer:** AI FAIR Data Reviewer (general-fair-data-review skill)
**Review mode:** [Manuscript + data/code | Manuscript only | Code/data repo only]
---
## Summary
[2–4 sentences: overall FAIRness level, most critical gaps, overall recommendation: Ready / Minor Revisions / Major Revisions / Not Acceptable]
---
## FAIR Scorecard
| Principle | Sub-principle | Status | Evidence / Gap |
|-----------|--------------|--------|----------------|
| **Findable** | F1: Persistent identifier | Pass/Partial/Fail | ... |
| | F2: Rich metadata | | |
| | F3: Metadata references data ID | | |
| | F4: Indexed in searchable resource | | |
| **Accessible** | A1: Retrievable via standard protocol | | |
| | A1.1: Protocol open and free | | |
| | A1.2: Auth procedure documented | | |
| | A2: Metadata accessible if data removed | | |
| **Interoperable** | I1: Formal/shared knowledge representation | | |
| | I2: FAIR vocabularies used | | |
| | I3: Qualified references to other data | | |
| **Reusable** | R1: Rich, accurate, relevant attributes | | |
| | R1.1: Clear data usage license | | |
| | R1.2: Detailed provenance | | |
| | R1.3: Domain community standards met | | |
---
## Major Concerns
[Number sequentially. For each: state issue → why problematic → actionable fix.]
1. **[Issue title]**
- *Problem:* ...
- *Impact:* ...
- *Fix:* ...
---
## Minor Concerns
- ...
---
## Atomistic/Computational Specific Findings
[Report on structure formats, trajectory deposits, software environments, parameter completeness.]
---
## Questions for Authors
1. ...
---
## Recommended Repositories (if none provided)
If no repository was deposited, suggest domain-appropriate options:
| Data type | Recommended repository |
|-----------|----------------------|
| Crystal structures | CCDC, ICSD, Materials Cloud, Zenodo |
| Molecular dynamics trajectories | Materials Cloud, Zenodo, NOMAD |
| ML models / checkpoints | Hugging Face, Zenodo, MACE-Models |
| General datasets | Zenodo, Figshare, Dryad |
| Code | GitHub + Zenodo DOI via Zenodo GitHub integration |Document-Specific Workflows
Manuscript Review (PDF)
[!WARNING] Read the PDF text directly. Do not assume data exists unless a DOI or repository URL is explicitly present in the manuscript body or supplement.
Steps:
- Extract DAS and Code Availability Statement verbatim.
- Resolve any DOIs or URLs found.
- If DOIs resolve to a live repository, proceed with repository inspection (Step 3).
- If only "data available upon request" is stated: flag as Fail for F1, F4, A1, R1.1 — this does not meet FAIR standards.
Code/Data Repository Review Only
Steps:
- Read top-level
README.md. - Check for
LICENSE,environment.yml/requirements.txt,CITATION.cff. - Inspect directory structure for data files and their formats.
- Check metadata on the repository platform (Zenodo record, GitHub About section, etc.).
Constraints
- Scope: Focus strictly on data/code FAIRness. Scientific methodology critique belongs in general-peer-review.
- Tone: Objective and constructive. Every Fail must include a concrete, actionable fix.
- "Data available upon request": Always flag as non-FAIR. Not acceptable per RSC Digital Discovery and FAIR principles.
- Proprietary formats:
.xlsx,.mat, Gaussian.chk, VASPWAVECARwithout open alternatives are I1/R1.3 failures. - License absence: Unlicensed ≠ open. Always flag missing licenses as R1.1 Fail.
References
- Wilkinson, M. D. et al., "The FAIR Guiding Principles for scientific data management and stewardship", Sci. Data 3, 160018 (2016). DOI: 10.1038/sdata.2016.18
- RSC Digital Discovery Data Review Guidelines. rsc.org/publishing
See Also
Author: Magdalena Lederbauer Contact: GitHub @mlederbauer
© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/general-fair-data-review of learningmatter-mit/AtomisticSkills.
Open the folder on GitHubat commit 6257444
Compare with similar skills
General Fair Data Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| General Fair Data Review this skilllearningmatter-mit/AtomisticSkills | 176 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Research Writingalfonso0512/research-writing-skill | 487 | 1 repos | ~818 | Automated safety check: Pass | MIT | |
| Paper WritingMLNLP-World/Paper-Writing-Tips | 4.7k | — | ~630 | Automated safety check: Pass | None | |
| PaperjurySpark-To-Paper-Skills/paperjury | 1.2k | — | ~5.3k | Automated safety check: Pass | MIT | |
| Literature Surveyai4s-research/ai4s-skills | 237 | 2 repos | ~2k | Automated safety check: Pass | MIT | |
| Venue TemplatesK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~2.9k | Automated safety check: Pass | MIT |
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Paper Writing
MLNLP-World/Paper-Writing-Tips
学术论文写作检查与优化助手。基于 MLNLP-World 社区整理的论文写作技巧，帮助检查和优化学术论文。Use when: (1) 检查论文 LaTeX 格式和排版, (2) 优化公式符号使用, (3) 改进图表设计, (4) 润色英文学术表达, (5) 检查参考文献格式, (6) 投稿前终稿检查, (7) 用户询问论文写作技巧或规范。
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Spark-To-Paper-Skills/paperjury
Three modes for CS-conference papers (CVPR/ICCV/ECCV vision, ACL/EMNLP/NAACL NLP, ICLR/NeurIPS/ICML/AAAI ML).
Literature Survey
ai4s-research/ai4s-skills
A skill your agent uses when the user wants a comprehensive literature survey on a specific research topic.
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Related
Categories
Questions about General Fair Data Review
What does General Fair Data Review do?
Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation…. General Fair Data Review is an agent skill from learningmatter-mit/AtomisticSkills. Review a manuscript or code repository for FAIR data compliance (Findable, Accessible, Interoperable, Reusable), producing a structured report with pass/fail per principle and actionable remediation steps.
When should I use General Fair Data Review?
General Fair Data Review fits situations like: documents & Office work in your project.
How do I install General Fair Data Review in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a claude-code`. Or copy the skill folder (skills/general-fair-data-review in learningmatter-mit/AtomisticSkills) into .claude/skills/general-fair-data-review in your project. Claude Code loads it when a task matches its description.
How do I install General Fair Data Review in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a codex`. Or copy the skill folder (skills/general-fair-data-review in learningmatter-mit/AtomisticSkills) into .agents/skills/general-fair-data-review in your project. Codex loads it when a task matches its description.
Can I use General Fair Data Review in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill general-fair-data-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/general-fair-data-review, .gemini/skills/general-fair-data-review, .github/skills/general-fair-data-review and .opencode/skills/general-fair-data-review in your project.
What does General Fair Data Review need to run?
SKILL.md names no scripts, command-line tools or credentials: General Fair Data Review is instructions for the agent only.
Does General Fair Data Review access the network?
SKILL.md names 3 domains. As links in the text: doi.org, rsc.org and github.com. This is read from the text; nothing was executed.
Is General Fair Data Review safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does General Fair Data Review use?
General Fair Data Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does General Fair Data Review use?
About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to General Fair Data Review?
Skills that share tags, products or a category with General Fair Data Review: Research Writing (alfonso0512/research-writing-skill, 487 stars), Paper Writing (MLNLP-World/Paper-Writing-Tips, 4.7k stars), Paperjury (Spark-To-Paper-Skills/paperjury, 1.2k stars) and Literature Survey (ai4s-research/ai4s-skills, 237 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains General Fair Data Review?
learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 2026.
Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.