HypoGeniC Hypothesis Generation
K-Dense-AI/scientific-agent-skills
Plans and audits runs of the HypoGeniC and HypoRefine packages, which propose hypotheses from labeled text datasets, with local checks before any model call.
A skill your agent uses when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates…
Install Naacl Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills naacl-reproducibility --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/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/NAACL-Skills/skills/naacl-reproducibility .claude/skills/naacl-reproducibility && 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 "naacl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibility into .claude/skills/naacl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "naacl-reproducibility", 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/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibilityType 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 brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills naacl-reproducibility --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/NAACL-Skills/skills/naacl-reproducibility .agents/skills/naacl-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "naacl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibility into .agents/skills/naacl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "naacl-reproducibility", 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 brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills naacl-reproducibility --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/NAACL-Skills/skills/naacl-reproducibility .cursor/skills/naacl-reproducibility && 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 "naacl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibility into .cursor/skills/naacl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "naacl-reproducibility", 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/brycewang-stanford/Awesome-Journal-Skills.git --path NAACL-Skills/skills/naacl-reproducibility--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 brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills naacl-reproducibility --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/NAACL-Skills/skills/naacl-reproducibility .gemini/skills/naacl-reproducibility && 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 "naacl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibility into .gemini/skills/naacl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "naacl-reproducibility", 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 brycewang-stanford/Awesome-Journal-Skills naacl-reproducibilityInstalls 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 brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/NAACL-Skills/skills/naacl-reproducibility .github/skills/naacl-reproducibility && 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 "naacl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibility into .github/skills/naacl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "naacl-reproducibility", 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 brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills naacl-reproducibility --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/NAACL-Skills/skills/naacl-reproducibility .opencode/skills/naacl-reproducibility && 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 "naacl-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/NAACL-Skills/skills/naacl-reproducibility into .opencode/skills/naacl-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "naacl-reproducibility", 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
naacl-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 569 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates…
- Works in 3 steps: A contamination worry becomes a lookup… → A "results seem fragile" score becomes… → A camera-ready promise becomes credible…
- Hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract
- SKILL.md covers The contract reading of the…, What must be pinned for NLP…, Multilingual runs: the… and A reproducibility manifest…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Naacl Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates, making multilingual evaluation re-runnable, and stating an honest release level instead of an aspirational one.
Its SKILL.md is about 1.4k 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 Research & Science, covering Reproducible research and Natural language processing. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract
- Pinning model versions and API access dates
- Making multilingual evaluation re-runnable
- Stating an honest release level instead of an aspirational one
Example prompts
- “/naacl-reproducibility”
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- A contamination worry becomes a lookup ("test items postdate the
- A "results seem fragile" score becomes challengeable with the seed
- A camera-ready promise becomes credible because the reviewer can see
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. 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 yaml).
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
Naacl Reproducibility loads about 1.4k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 569 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 brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 569 words, ~1,360 tokens.
Download SKILL.mdSave it as
.claude/skills/naacl-reproducibility/SKILL.md (or your agent's skills folder).- name
- naacl-reproducibility
- description
- Use when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates, making multilingual evaluation re-runnable, and stating an honest release level instead of an aspirational one.
NAACL Reproducibility
Reproducibility at NAACL is enforced through a document, not a badge: the Responsible NLP checklist travels with the submission, reviewers read it against the paper, and answers contradicted by the PDF are grounds for rejection without review under current ARR policy. The working stance: every checklist answer is a claim you are prepared to defend in the author response.
The contract reading of the checklist
- Answer from what the paper contains, never from what the repo will eventually contain.
- "No" with a reason is a safe answer; an unsupported "Yes" is a trap you set for yourself.
- Point each answer at a location — section, table, appendix — so a reviewer verifying it lands somewhere specific.
- Recheck the answers after every major revision; checklists rot faster than papers.
What must be pinned for NLP results in 2026
| Moving part | Pin it as | Why it decays |
|---|---|---|
| Hosted LLM APIs | Model identifier + query date range | Providers swap weights behind stable names |
| Open-weights models | Exact checkpoint hash or revision tag | "Latest" changes under you |
| Decoding | Temperature, top-p, max tokens, seed policy, n samples | Unstated sampling makes numbers unrepeatable |
| Prompts | Verbatim strings, all variants, selection rule | "We used a standard prompt" reproduces nothing |
| Tokenizers / normalization | Version + Unicode normalization form | Silent retokenization shifts multilingual scores |
| Eval metrics | Implementation + version (not just the metric name) | Scorer variants disagree by whole points |
| Data splits | Published split files or generation script + seed | Ad-hoc splits are unrecoverable |
Multilingual runs: the NAACL-flavored decay modes
Papers committed to NAACL disproportionately evaluate across languages, and multilingual pipelines decay in language-specific ways: normalization that strips combining diacritics, sentence splitters that fail on Spanish inverted punctuation, tokenizers that fragment agglutinative morphology (Nahuatl, Quechua, Guaraní), and translation-based baselines whose MT system version was never recorded. Log per-language preprocessing explicitly — a single global "we lowercase and tokenize" line hides exactly the steps that differ across the languages you claim to cover.
A reproducibility manifest worth shipping
# repro-manifest.yml — include in the supplement
models:
- id: example-lm-7b, revision: a1b2c3d, dtype: bf16
- id: hosted-model-x, api_dates: 2026-05-02..2026-05-19
decoding: {temperature: 0.0, max_tokens: 512, samples: 1}
prompts: prompts/ # verbatim, one file per task x language
data:
- name: task_es, split_files: splits/es/, license: CC-BY-4.0
- name: task_gn, split_files: splits/gn/, license: community-terms
scoring: eval/score.py (chrF++ via sacrebleu 2.4.x, signature logged)
hardware: 4x A100-80GB, ~310 GPU-hours total
seeds: [13, 42, 2026] # every table reports mean/sd over these
known_gaps: human eval not re-runnable; transcripts includedThe known_gaps line is the point: an honest boundary between re-runnable
and merely documented is what distinguishes a defensible checklist from a
hopeful one.
Show full SKILL.md (225 more words)Show less
Where the investment pays: the response window
Reproducibility rigor is usually sold as ethics; at NAACL it is also tactics. When a reviewer asks "would the result hold with a different prompt phrasing?" a team with a pinned manifest and an experiment ledger answers inside the window with numbers; a team without one answers with adjectives. Concretely, the manifest converts three recurring review moments:
- A contamination worry becomes a lookup ("test items postdate the pinned revision's training cutoff") instead of a speculation.
- A "results seem fragile" score becomes challengeable with the seed spread already computed per table.
- A camera-ready promise becomes credible because the reviewer can see the infrastructure that would fulfill it.
Meta-reviews reward the second answer pattern visibly; the checklist is read as a proxy for whether the authors could defend any number under pressure.
Release levels, stated plainly
- Re-runnable — scripts + outputs + splits; a stranger reproduces the tables from the archive.
- Verifiable — outputs and scoring included; generation requires resources or access the reader may lack.
- Documented — full protocol description; execution not possible (private data, community-restricted corpora, retired APIs).
Name the level in the paper. NAACL reviewers penalize mismatch between claimed and actual level far more than they penalize level 3 honestly held — especially when community data-governance terms, common in Americas-language work, are the stated reason.
Output format
[Checklist audit] <answer -> evidence location -> holds/contradicted>
[Pin table status] <each moving part -> pinned/missing>
[Per-language gaps] <language -> unlogged preprocessing or scorer>
[Release level] re-runnable / verifiable / documented (+ reason)
[Fixes before upload] <ordered>© brycewang-stanford, 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 NAACL-Skills/skills/naacl-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Naacl Reproducibility 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 |
|---|---|---|---|---|---|---|
| Naacl Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.4k | Automated safety check: Pass | MIT | |
| HypoGeniC Hypothesis GenerationK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.6k | Automated safety check: Notes | MIT | |
| Ablation Study Plannerwanshuiyin/Auto-claude-code-research-in-sleep | 17k | — | ~1.3k | Automated safety check: Notes | MIT | |
| Radiology Annotationhuang-sir1/radiology-skills | 1.9k | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| Scholar Openjoshzyj/open-scholar-skill | 168 | — | ~14k | Automated safety check: Pass | Custom licence | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT |
Similar skills
Ablation Study Planner
wanshuiyin/Auto-claude-code-research-in-sleep
Plans the ablation studies a paper needs after main results support its claim: Codex designs them like a reviewer, Claude Code checks feasibility and runs them.
Radiology Annotation
huang-sir1/radiology-skills
Design/audit imaging truth, readers, ROI geometry, reproducibility and label noise; not model training.
Scholar Open
joshzyj/open-scholar-skill
Implement open science practices for a social science study.
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
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Categories
Questions about Naacl Reproducibility
What does Naacl Reproducibility do?
A skill your agent uses when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates…. Naacl Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract, pinning model versions and API access dates, making multilingual evaluation re-runnable, and stating an honest release level instead of an aspirational one.
When should I use Naacl Reproducibility?
Naacl Reproducibility fits situations like: hardening the reproducibility story of a NAACL submission — treating the Responsible NLP checklist as a binding contract; pinning model versions and API access dates; making multilingual evaluation re-runnable; stating an honest release level instead of an aspirational one.
How do I install Naacl Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a claude-code`. Or copy the skill folder (NAACL-Skills/skills/naacl-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/naacl-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Naacl Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a codex`. Or copy the skill folder (NAACL-Skills/skills/naacl-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/naacl-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Naacl Reproducibility 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 brycewang-stanford/Awesome-Journal-Skills --skill naacl-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/naacl-reproducibility, .gemini/skills/naacl-reproducibility, .github/skills/naacl-reproducibility and .opencode/skills/naacl-reproducibility in your project.
What does Naacl Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Naacl Reproducibility is instructions for the agent only.
Does Naacl Reproducibility 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 Naacl Reproducibility 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 Naacl Reproducibility use?
Naacl Reproducibility 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 Naacl Reproducibility use?
About 1.4k tokens (SKILL.md is roughly 5.4k 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 Naacl Reproducibility?
Skills that share tags, products or a category with Naacl Reproducibility: HypoGeniC Hypothesis Generation (K-Dense-AI/scientific-agent-skills, 48k stars), Ablation Study Planner (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars), Radiology Annotation (huang-sir1/radiology-skills, 1.9k stars) and Scholar Open (joshzyj/open-scholar-skill, 168 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Naacl Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.