Hypothesis Generation
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
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…
$ npx skills add Yila-AI/awesome-research-skills --skill science-research-writing -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Yila-AI/awesome-research-skills science-research-writing --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/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-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 "science-research-writing" agent skill from https://github.com/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writing into .claude/skills/science-research-writing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "science-research-writing", 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/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writingType 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 Yila-AI/awesome-research-skills --skill science-research-writing -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Yila-AI/awesome-research-skills science-research-writing --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Yila-AI/awesome-research-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/science-research-writing .agents/skills/science-research-writing && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "science-research-writing" agent skill from https://github.com/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writing into .agents/skills/science-research-writing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "science-research-writing", 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 Yila-AI/awesome-research-skills --skill science-research-writing -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Yila-AI/awesome-research-skills science-research-writing --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Yila-AI/awesome-research-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/science-research-writing .cursor/skills/science-research-writing && 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 "science-research-writing" agent skill from https://github.com/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writing into .cursor/skills/science-research-writing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "science-research-writing", 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/Yila-AI/awesome-research-skills.git --path skills/science-research-writing--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 Yila-AI/awesome-research-skills --skill science-research-writing -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Yila-AI/awesome-research-skills science-research-writing --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Yila-AI/awesome-research-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/science-research-writing .gemini/skills/science-research-writing && 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 "science-research-writing" agent skill from https://github.com/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writing into .gemini/skills/science-research-writing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "science-research-writing", 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 Yila-AI/awesome-research-skills science-research-writingInstalls 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 Yila-AI/awesome-research-skills --skill science-research-writing -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Yila-AI/awesome-research-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/science-research-writing .github/skills/science-research-writing && 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 "science-research-writing" agent skill from https://github.com/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writing into .github/skills/science-research-writing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "science-research-writing", 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 Yila-AI/awesome-research-skills --skill science-research-writing -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Yila-AI/awesome-research-skills science-research-writing --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Yila-AI/awesome-research-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/science-research-writing .opencode/skills/science-research-writing && 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 "science-research-writing" agent skill from https://github.com/Yila-AI/awesome-research-skills/tree/main/skills/science-research-writing into .opencode/skills/science-research-writing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "science-research-writing", 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.
science-research-writingA 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.
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.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 0609e85. It shows what the files ask for, not the result of running them.
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.
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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
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.
.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.Turn the author's research materials into the next useful manuscript deliverable. Guide the writing process without replacing scientific judgment or inventing intellectual content.
references/input-output-contract.md and references/certainty-and-claim-strength.md.references/reverse-engineering-protocol.md when target papers are supplied or the user requests journal adaptation.introduction.md, methods.md, results.md, discussion.md, conclusion.md, abstract.md, or title.md.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.Read all supplied materials first. Identify:
lookup, learn, model, plan, draft, revise, or audit;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:
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.
Use plan. Convert the question and intended contribution into a provisional section-function map. Label missing evidence instead of supplying it.
Use plan -> draft -> audit. Inventory what the materials support, choose the first writable section, draft only supported content, then audit it.
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.
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.
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.
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:
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.
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.
These are defaults, not a universal template. Adapt the sequence when the author's field or target-paper model supports a different defensible structure.
Before returning text, compare it with the source materials and check:
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.
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.
Follow references/input-output-contract.md. Use this order:
Draft or diagnosisHow it is organizedAuthor confirmationNext stepPut 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
SKILL.md and 16 other files (scripts, references, assets) in skills/science-research-writing of Yila-AI/awesome-research-skills.
Open the folder on GitHubat commit 0609e85
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Science Research Writing this skillYila-AI/awesome-research-skills | 133 | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Hypothesis Generationspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Notes | MIT | |
| GitHub Deep Researchbytedance/deer-flow | 84k | 4 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Nature Paper CardYuan1z0825/nature-skills | 47k | 2 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Last30daysmvanhorn/last30days-skill | 64k | — | ~7.9k | Automated safety check: Notes | MIT |
spacering-net/codeg
Structured hypothesis formulation from observations. An agent skill from spacering-net/codeg.
bytedance/deer-flow
Researches a GitHub repository over four rounds using the GitHub API and web search, then writes a structured markdown report with timeline, metrics and Mermaid diagrams.
Yuan1z0825/nature-skills
Builds a structured deep-reading card for one scientific paper, covering methods, how experiments support claims, limitations and research ideas, with a script to prepare the source.
weapp-tailwindcss/weapp-tailwindcss
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section.
mvanhorn/last30days-skill
Research what people actually say about any topic in the last 30 days.
spacering-net/codeg
Structured manuscript/grant review with checklist-based evaluation.
Yila-AI/awesome-research-skills
A skill your agent uses when researchers ask to remove generic, templated, or AI-like patterns from Chinese or English academic prose, make an AI-assisted draft sound more like the author's own…
Yila-AI/awesome-research-skills
Create, revise, and quality-check source-grounded research presentations from papers, research notes, data, or manuscripts.
Categories
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.
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.
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.
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.
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.
Going by SKILL.md and its folder, Science Research Writing needs Python for the scripts in its folder. Our summary lists: Python 3.
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.
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.
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.
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.
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.
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.