Agent skill

Science Research Writing

by Yila-AI in Yila-AI/awesome-research-skills

A skill your agent uses when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion…

Apache-2.0Auto-check passedResearch & Science

Install Science Research Writing

skills CLI
$ npx skills add Yila-AI/awesome-research-skills --skill science-research-writing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Yila-AI/awesome-research-skills science-research-writing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Yila-AI/awesome-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/science-research-writing .claude/skills/science-research-writing && rm -rf skills-src

Use ~/.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/

Facts

Skill name
science-research-writing
GitHub stars
133
Token cost
~2.1k tokens
SKILL.md length
985 words
Files
17 (incl. scripts, references, assets)
Skills in repo
3
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion…

  • Works in 4 steps: deliver every safe and useful part first; → identify the exact gap or conflict; → ask one highest-impact question; → …
  • Researchers need to plan
  • SKILL.md covers Load only what is needed, First response: inspect before…, Route the task and Build an evidence ledger, plus 4 more sections
  • Runs Python scripts from its folder

What it does

Science Research Writing is an agent skill from Yila-AI/awesome-research-skills. Use when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion, Abstract, and Title, with evidence-preserving and target-journal-aware guidance.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts, reference files and assets (for example `agents/openai.yaml`, `assets/section-function-map.md` and `assets/target-journal-model.json`).

It sits in Research & Science. The repository describes itself as: Open-source Agent Skills for planning, drafting, revising, and polishing SCI/SSCI papers—while preserving evidence, citations, and claim strength. The licence is Apache-2.0.

When your agent uses it

  • Researchers need to plan
  • Audit an empirical research paper from their own materials
  • Including Introduction
  • With evidence-preserving and target-journal-aware guidance

Example prompts

  • “/science-research-writing”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. deliver every safe and useful part first;
  2. identify the exact gap or conflict;
  3. ask one highest-impact question;
  4. wait before drafting only the blocked content.

What it can do on your machine

Read from SKILL.md and the folder at commit 0609e85. 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

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Science Research Writing loads about 2.1k tokens when it runs, and up to ~7.6k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 985 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.6k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from Yila-AI/awesome-research-skills at commit 0609e85, republished under its Apache-2.0 licence (© Yila-AI). 985 words, ~2,092 tokens.

Download SKILL.mdSave it as .claude/skills/science-research-writing/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.
name
science-research-writing
description
Use when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion, Abstract, and Title, with evidence-preserving and target-journal-aware guidance.
license
Apache-2.0

Science Research Writing

Turn the author's research materials into the next useful manuscript deliverable. Guide the writing process without replacing scientific judgment or inventing intellectual content.

Load only what is needed

  • Always read references/input-output-contract.md and references/certainty-and-claim-strength.md.
  • Read references/reverse-engineering-protocol.md when target papers are supplied or the user requests journal adaptation.
  • Read only the relevant section reference: introduction.md, methods.md, results.md, discussion.md, conclusion.md, abstract.md, or title.md.
  • Use assets/section-function-map.md for planning, assets/evidence-ledger.csv for provenance-sensitive drafting, and assets/target-journal-model.json for target-paper modeling.

First response: inspect before asking

Read all supplied materials first. Identify:

  • manuscript stage: idea, research materials, partial draft, or full draft;
  • primary job: lookup, learn, model, plan, draft, revise, or audit;
  • paper section and empirical design when inferable;
  • facts, numbers, citations, technical terms, null findings, limitations, and author judgments that must be protected;
  • the next useful output that can be produced safely now.

Do not ask the user to choose an internal mode. Do not require field, journal, section, or language preferences when a conservative useful result is possible.

If missing information would force an unsupported scientific choice:

  1. deliver every safe and useful part first;
  2. identify the exact gap or conflict;
  3. ask one highest-impact question;
  4. wait before drafting only the blocked content.

If conflicting sources block the entire requested sentence or section, return a diagnosis rather than a provisional scaffold. Do not infer variable roles, direction, reference groups, statistical meaning, table labels, or missing uncertainty from the conflicting numbers.

Route the task

Idea stage

Use plan. Convert the question and intended contribution into a provisional section-function map. Label missing evidence instead of supplying it.

Research-materials stage

Use plan -> draft -> audit. Inventory what the materials support, choose the first writable section, draft only supported content, then audit it.

Partial-draft stage

Use audit -> plan -> revise -> audit. Diagnose structure and evidence boundaries before rewriting.

If the supplied prose is already clear, section-appropriate, and evidence-faithful, return it unchanged. Do not provide an optional cosmetic alternative, normalize punctuation, add units, or propose journal styling unless the user supplied a specific style requirement.

Full-draft stage

Use audit first. Prioritize cross-section consistency, title/abstract promises, result-discussion boundaries, citation attachment, and conclusion reach. Revise only what the user requests or what the audit identifies.

Target papers supplied

Use learn -> model before planning or drafting. Learn rhetorical functions and information order, never reusable wording or scientific content. Follow references/reverse-engineering-protocol.md.

Build an evidence ledger

Before drafting or revising, privately classify every consequential statement as one of:

  • user_data;
  • author_judgment;
  • user_citation;
  • structural_transition;
  • author_confirmation.

Treat the user's materials as the authority. Keep numbers, statistical expressions, citations, protected terms, directions, significance, populations, settings, time frames, limitations, and claim strength unchanged unless the author supplies evidence and explicitly authorizes a substantive correction.

Never silently add:

  • data, results, methods, mechanisms, citations, limitations, interpretations, implications, or recommendations;
  • claims needed only to make a conventional section appear complete;
  • target-paper language, argument content, or field assumptions not present in the author's materials.

Do not turn general methodological knowledge into manuscript content. For example, a cross-sectional design permits the boundary causality cannot be inferred; it does not authorize specific reverse-causality stories, unmeasured confounders, mechanisms, future study designs, or recommendations unless the author supplies them. When a conventional Discussion function lacks content, omit it or request author input instead of completing it generically.

Do not infer a contrast from separate significance tests. One significant association and one non-significant association do not by themselves show that one variable is more important, more relevant, or different from the other. Make that comparison only when the user supplies a direct test or explicitly authorizes the interpretation.

Show full SKILL.md (394 more words)Show less

Write by information function

Load the relevant section reference and map each paragraph to a reader question and information function before writing. Prefer a clear evidence path over ornamental academic language.

  • Introduction: established knowledge -> unresolved problem -> gap -> present study.
  • Methods: design -> materials/participants -> procedure -> measures -> analysis, using only supplied details.
  • Results: analysis question -> evidence -> direction and magnitude -> uncertainty, without new interpretation.
  • Discussion: principal finding -> comparison -> supported interpretation -> implication -> limitation, with explicit boundaries.
  • Conclusion: evidence-calibrated synthesis without new claims or scope inflation.
  • Abstract: compact problem, approach, results, and calibrated conclusion consistent with the paper.
  • Title: a precise promise fully supported by design, population, variables, and evidence.

These are defaults, not a universal template. Adapt the sequence when the author's field or target-paper model supports a different defensible structure.

Audit every draft

Before returning text, compare it with the source materials and check:

  • all numbers, signs, units, ranges, p values, confidence intervals, sample sizes, time points, figure/table references, and citation markers;
  • all protected names, models, instruments, datasets, scales, variables, and group labels;
  • positive, negative, and null directions;
  • association, prediction, contribution, effect, and causation boundaries;
  • uncertainty, exceptions, limitations, setting, population, duration, and validation scope;
  • citation-to-proposition attachment;
  • separation of observation from interpretation;
  • consistency among Title, Abstract, Results, Discussion, and Conclusion;
  • absence of copied target-paper wording.

When local source and draft text are available, run scripts/check_draft_invariants.py as a deterministic first pass. A passing script is necessary but not sufficient; manually review semantics and citation scope.

If a target-journal model is created, run scripts/validate_writing_model.py before using it.

Refusal boundary

Do not fabricate citations, hide null or adverse results, remove limitations, disguise contradictory evidence, or strengthen a claim beyond the supplied evidence. Briefly explain the mismatch and provide the strongest evidence-faithful alternative.

Return a novice-readable result

Follow references/input-output-contract.md. Use this order:

  1. Draft or diagnosis
  2. How it is organized
  3. Author confirmation
  4. Next step

Put the usable manuscript text or diagnosis first. Keep explanations brief. Write None required when no author confirmation is needed. Show Risk flags only when a real academic risk exists.

The next step must advance evidence or author review. Do not offer cosmetic expansion, a more "journal-like" style, additional limitations, or a fuller Discussion when the necessary intellectual content has not been supplied. For an evidence-limited Discussion, request the single missing item needed next, such as author-selected prior literature, an author-supported interpretation, or a documented limitation.

© Yila-AI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 16 other files (scripts, references, assets) in skills/science-research-writing of Yila-AI/awesome-research-skills.

  • SKILL.md
  • agents/openai.yaml
  • assets/evidence-ledger.csv
  • assets/section-function-map.md
  • assets/target-journal-model.json
  • references/abstract.md
  • references/certainty-and-claim-strength.md
  • references/conclusion.md
  • references/discussion.md
  • references/input-output-contract.md
  • references/introduction.md
  • references/methods.md
  • references/results.md
  • references/reverse-engineering-protocol.md
  • references/title.md
  • scripts/check_draft_invariants.py
  • scripts/validate_writing_model.py

Open the folder on GitHubat commit 0609e85

Compare with similar skills

Science Research Writing 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.

Science Research Writing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Science Research Writing this skillYila-AI/awesome-research-skills133—~2.1kAutomated safety check: PassApache-2.0
Hypothesis Generationspacering-net/codeg3.9k14 repos~3.6kAutomated safety check: NotesMIT
GitHub Deep Researchbytedance/deer-flow84k4 repos~1.3kAutomated safety check: PassMIT
Nature Paper CardYuan1z0825/nature-skills47k2 repos~2.1kAutomated safety check: PassApache-2.0
Content Research Writerweapp-tailwindcss/weapp-tailwindcss1.9k25 repos~3.5kAutomated safety check: PassMIT
Last30daysmvanhorn/last30days-skill64k—~7.9kAutomated safety check: NotesMIT

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Questions about Science Research Writing

What does Science Research Writing do?

A skill your agent uses when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion…. Science Research Writing is an agent skill from Yila-AI/awesome-research-skills. Use when researchers need to plan, draft, revise, or audit an empirical research paper from their own materials, including Introduction, Methods, Results, Discussion, Conclusion, Abstract, and Title, with evidence-preserving and target-journal-aware guidance.

When should I use Science Research Writing?

Science Research Writing fits situations like: researchers need to plan; audit an empirical research paper from their own materials; including Introduction; with evidence-preserving and target-journal-aware guidance.

How do I install Science Research Writing in Claude Code?

Run `npx skills add Yila-AI/awesome-research-skills --skill science-research-writing -a claude-code`. Or copy the skill folder (skills/science-research-writing in Yila-AI/awesome-research-skills) into .claude/skills/science-research-writing in your project. Claude Code loads it when a task matches its description.

How do I install Science Research Writing in Codex?

Run `npx skills add Yila-AI/awesome-research-skills --skill science-research-writing -a codex`. Or copy the skill folder (skills/science-research-writing in Yila-AI/awesome-research-skills) into .agents/skills/science-research-writing in your project. Codex loads it when a task matches its description.

Can I use Science Research Writing 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 Yila-AI/awesome-research-skills --skill science-research-writing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/science-research-writing, .gemini/skills/science-research-writing, .github/skills/science-research-writing and .opencode/skills/science-research-writing in your project.

What does Science Research Writing need to run?

Going by SKILL.md and its folder, Science Research Writing needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Science Research Writing access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Science Research Writing 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Science Research Writing use?

Science Research Writing is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Science Research Writing use?

About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.5k tokens, read only when the agent opens those files.

What are the alternatives to Science Research Writing?

Skills that share tags, products or a category with Science Research Writing: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Science Research Writing?

Yila-AI (a GitHub organization) maintains it in Yila-AI/awesome-research-skills, which has 133 GitHub stars. The repository holds 3 skills in this directory. The repository was last updated on September 4, 2026.

Source: Yila-AI/awesome-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.