Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost…
Install Aaai Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aaai-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/AAAI-Skills/skills/aaai-reproducibility .claude/skills/aaai-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 "aaai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-reproducibility into .claude/skills/aaai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aaai-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/AAAI-Skills/skills/aaai-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 aaai-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aaai-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/AAAI-Skills/skills/aaai-reproducibility .agents/skills/aaai-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 "aaai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-reproducibility into .agents/skills/aaai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aaai-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 aaai-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aaai-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/AAAI-Skills/skills/aaai-reproducibility .cursor/skills/aaai-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 "aaai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-reproducibility into .cursor/skills/aaai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aaai-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 AAAI-Skills/skills/aaai-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 aaai-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills aaai-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/AAAI-Skills/skills/aaai-reproducibility .gemini/skills/aaai-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 "aaai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-reproducibility into .gemini/skills/aaai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aaai-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 aaai-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 aaai-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/AAAI-Skills/skills/aaai-reproducibility .github/skills/aaai-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 "aaai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-reproducibility into .github/skills/aaai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aaai-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 aaai-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 aaai-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/AAAI-Skills/skills/aaai-reproducibility .opencode/skills/aaai-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 "aaai-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/AAAI-Skills/skills/aaai-reproducibility into .opencode/skills/aaai-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aaai-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
aaai-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 760 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost…
- Works in 6 steps: Unzip the submitted package into a clean… → Read only the included README, not local… → Run the smallest command that… → …
- Strengthening an AAAI papers reproducibility checklist (placed after references)
- SKILL.md covers Reproducibility audit, Common AAAI weaknesses, Checklist-to-evidence… and Claim-evidence ledger, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Aaai Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
Its SKILL.md is about 1.5k 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. 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
- Strengthening an AAAI papers reproducibility checklist (placed after references)
- Experimental traceability
- Seed and hyperparameter reporting
- Compute and cost disclosure
Example prompts
- “/aaai-reproducibility”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Unzip the submitted package into a clean directory.
- Read only the included README, not local lab notes.
- Run the smallest command that regenerates one headline table or figure.
- Check that expected runtime, hardware, random seeds, data download/access, and license constraints
- Confirm that output files have deterministic names and map back to paper tables.
- Mark any non-runnable or restricted component as such in both the README and checklist.
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.
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
Aaai Reproducibility loads about 1.5k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 760 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). 760 words, ~1,523 tokens.
Download SKILL.mdSave it as
.claude/skills/aaai-reproducibility/SKILL.md (or your agent's skills folder).- name
- aaai-reproducibility
- description
- Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
AAAI Reproducibility
Use this when a draft needs to survive AAAI review on rigor, not just novelty. AAAI-27 requires the reproducibility checklist to be uploaded separately from the main PDF, in its own field on the submission form (AAAI-26 carried it inside the PDF after the references) — so it is a document a reviewer opens on its own, and it has to agree with the paper and supplement rather than read as an afterthought. AAAI-27 also states that reviewers assess reproducibility from what was actually submitted, and that material promised "after acceptance or publication" is not evidence it exists.
Reproducibility audit
- Map each central claim to submitted evidence: theorem, table, figure, ablation, appendix item, checklist answer, or code/data artifact.
- Record seeds, splits, preprocessing, hyperparameters, model selection, early stopping, prompt selection, and hardware.
- Report variance or uncertainty when stochasticity affects conclusions.
- Document dataset licenses, access constraints, sensitive data, human-subjects issues, and annotation procedures.
- Separate training compute, inference compute, and experiment search cost.
- Check the reproducibility checklist for contradictions with the main text and supplement.
Common AAAI weaknesses
- Checklist says code/data are available but supplement lacks runnable commands.
- Main results rely on one seed, one benchmark, or one prompt family.
- Baselines are weaker than current open-source or widely cited systems.
- Evaluation uses closed data or APIs with no reproducibility substitute.
- Human evaluation omits annotator instructions or quality control.
Checklist-to-evidence consistency grid
AAAI places the reproducibility checklist after the references, and reviewers cross-check each "yes" against the paper and supplement. A "yes" with no backing artifact reads worse than an honest "no", because it signals the checklist was filled in carelessly.
| Checklist answer | Must be backed by | Phase-1 risk if unbacked |
|---|---|---|
| code available | runnable scripts in the ZIP | "claimed but absent" |
| seeds reported | seed list and variance | "single-run cherry-pick" |
| compute disclosed | train vs. inference vs. search cost | "hidden tuning budget" |
| data accessible | license and access path | "irreproducible by anyone" |
Claim-evidence ledger
Create a row for every claim that appears in the abstract, introduction, or conclusion. The ledger should be short enough to audit before submission and concrete enough that a Phase-1 reviewer can see that each headline claim is checkable.
| Ledger field | What to record | Common failure |
|---|---|---|
| Claim text | exact sentence or paraphrase from the paper | claim becomes stronger than the evidence |
| Evidence artifact | theorem, table, figure, appendix, code command, data sheet, or log path | evidence exists but is not submitted |
| Reproducibility inputs | seeds, splits, prompts, preprocessing, hardware, hyperparameters, and model versions | rerun cannot recreate the result |
| Variance and controls | confidence interval, standard deviation, multiple seeds, ablation, or matched-compute baseline | single lucky run drives the claim |
| Checklist answer | the checklist item whose answer depends on this artifact | checklist contradicts the supplement |
| Reviewer risk | what a skeptical reviewer would challenge first | rebuttal cannot fix missing evidence |
For each row, choose one of three actions: keep the claim because the artifact is present, weaken the claim to match the evidence, or add the missing artifact before submission. Do not leave a row in "promise later" state.
Show full SKILL.md (259 more words)Show less
Artifact dry-run
Before upload, run the artifact as if the reviewer has no private context:
- Unzip the submitted package into a clean directory.
- Read only the included README, not local lab notes.
- Run the smallest command that regenerates one headline table or figure.
- Check that expected runtime, hardware, random seeds, data download/access, and license constraints are stated before the command.
- Confirm that output files have deterministic names and map back to paper tables.
- Mark any non-runnable or restricted component as such in both the README and checklist.
The dry-run can be small; it does not need to reproduce every experiment. Its purpose is to prove that the submitted artifact is not merely decorative and that the checklist answers are honest.
Reviewer-pushback patterns
- "Checklist says code available but I see only figures." Fix: ship scripts and a one-line driver before the deadline; do not promise the repository in rebuttal.
- "Results may be seed-dependent." Fix: report multiple seeds with spread, and set the checklist seed answer to match the supplement exactly.
- "Closed API, not reproducible." Fix: add an open substitute model or release prompts and outputs so the claim is checkable.
Worked vignette
A vision-language paper checks "code and data available" but the ZIP holds only PDFs of plots. Audit
verdict: reproducibility grade "fragile", with a checklist conflict between the "yes" and the missing
scripts. The smallest fix is a reproduce.sh that regenerates one headline table from seeds plus a
dataset license note, after which the checklist answer becomes truthful and Phase-1 defensible.
Output format
[Reproducibility grade] strong / adequate / fragile / not reviewable
[Checklist conflicts] <answers that contradict paper/supplement>
[Evidence gaps] <claims without submitted verification>
[Compute/data disclosure] complete / incomplete
[Priority fixes] <smallest changes before submission>© 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 AAAI-Skills/skills/aaai-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Aaai 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 |
|---|---|---|---|---|---|---|
| Aaai Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
Similar skills
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.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
More from brycewang-stanford/Awesome-Journal-Skills
All 2,387 skills in this repoAaag Data Analysis
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
Aaag Literature Positioning
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
Aaag Rebuttal
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
Aaag Research Design
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
Aaag Review Process
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
Aaag Submission
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Related
Categories
Questions about Aaai Reproducibility
What does Aaai Reproducibility do?
A skill your agent uses when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost…. Aaai Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening an AAAI paper's reproducibility checklist (placed after references), experimental traceability, seed and hyperparameter reporting, compute and cost disclosure, dataset access and licensing, code/data ZIP readiness, and the claim-to-evidence map that Phase-1 reviewers use to judge rigor across AAAI's broad AI scope.
When should I use Aaai Reproducibility?
Aaai Reproducibility fits situations like: strengthening an AAAI papers reproducibility checklist (placed after references); experimental traceability; seed and hyperparameter reporting; compute and cost disclosure.
How do I install Aaai Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-reproducibility -a claude-code`. Or copy the skill folder (AAAI-Skills/skills/aaai-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/aaai-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Aaai Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill aaai-reproducibility -a codex`. Or copy the skill folder (AAAI-Skills/skills/aaai-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/aaai-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Aaai 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 aaai-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/aaai-reproducibility, .gemini/skills/aaai-reproducibility, .github/skills/aaai-reproducibility and .opencode/skills/aaai-reproducibility in your project.
What does Aaai Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Aaai Reproducibility is instructions for the agent only.
Does Aaai 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 Aaai 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 Aaai Reproducibility use?
Aaai 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 Aaai Reproducibility use?
About 1.5k tokens (SKILL.md is roughly 6.1k 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 Aaai Reproducibility?
Skills that share tags, products or a category with Aaai Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Aaai 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.