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 the reproducibility of a SIGIR paper or preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline, seeds and variance for neural…
Install Sigir Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigir-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-reproducibility .claude/skills/sigir-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 "sigir-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-reproducibility into .claude/skills/sigir-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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/SIGIR-Skills/skills/sigir-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 sigir-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-reproducibility .agents/skills/sigir-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 "sigir-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-reproducibility into .agents/skills/sigir-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 sigir-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-reproducibility .cursor/skills/sigir-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 "sigir-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-reproducibility into .cursor/skills/sigir-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 SIGIR-Skills/skills/sigir-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 sigir-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-reproducibility .gemini/skills/sigir-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 "sigir-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-reproducibility into .gemini/skills/sigir-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 sigir-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 sigir-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/SIGIR-Skills/skills/sigir-reproducibility .github/skills/sigir-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 "sigir-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-reproducibility into .github/skills/sigir-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 sigir-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 sigir-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/SIGIR-Skills/skills/sigir-reproducibility .opencode/skills/sigir-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 "sigir-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-reproducibility into .opencode/skills/sigir-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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
sigir-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 796 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening the reproducibility of a SIGIR paper or preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline, seeds and variance for neural…
- Works in 5 steps: Target selection with stakes — results… → Faithful reimplementation first:… → Divergence analysis as the contribution:… → …
- Strengthening the reproducibility of a SIGIR paper
- SKILL.md covers Why IR results drift: the…, Minimum reporting block for…, Writing a Reproducibility… and Decay planning: the three-year…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Sigir Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of a SIGIR paper or preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline, seeds and variance for neural rankers, documenting collection versions and index settings, and structuring a reproduction study of published IR results with honest divergence analysis.
Its SKILL.md is about 1.8k 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 Retrieval-augmented generation. 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 the reproducibility of a SIGIR paper
- Preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline
- Seeds and variance for neural rankers
- Documenting collection versions and index settings
Example prompts
- “/sigir-reproducibility”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Target selection with stakes — results the community builds on (a widely
- Faithful reimplementation first: reproduce with the original artifacts where
- Divergence analysis as the contribution: when numbers differ, isolate the
- Generalization probes: does the original conclusion survive new collections,
- Respectful register: the genre convention is scientific, not gotcha — criticize
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
Sigir Reproducibility loads about 1.8k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 796 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). 796 words, ~1,783 tokens.
Download SKILL.mdSave it as
.claude/skills/sigir-reproducibility/SKILL.md (or your agent's skills folder).- name
- sigir-reproducibility
- description
- Use when strengthening the reproducibility of a SIGIR paper or preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline, seeds and variance for neural rankers, documenting collection versions and index settings, and structuring a reproduction study of published IR results with honest divergence analysis.
SIGIR Reproducibility
Reproducibility is unusually load-bearing at SIGIR for two reasons. First, the venue runs a dedicated Reproducibility track (its own track in 2026, split out of the former combined Resource & Reproducibility track — budget and dates 待核实 on the current page), so reproduction studies are publishable first-class work. Second, the field's own literature documents how often reported IR gains fail to replicate under matched tuning — reviewers of regular papers therefore read reproducibility signals as a proxy for whether the gains are real.
Why IR results drift: the usual suspects
| Drift source | Typical symptom | Pin it by |
|---|---|---|
| Collection version | "MS MARCO" numbers off by points | Exact version/split ids, ir_datasets identifiers, checksums |
| Index-time analysis | BM25 baseline differs across papers | Scripted index build; record stemmer, stopwords, k1/b |
| Doc processing for neural models | Recall@k shifts | Max length, stride, title concatenation recorded as config |
| Truncated vs judged pools | Inflated dense-retrieval scores | State pooling; report judged@k alongside nDCG |
| Seeds and nondeterminism | ±0.005 nDCG run-to-run | Multiple seeds; report mean ± sd, not the best run |
| Eval tool discrepancies | MAP differs at 4th decimal | One canonical scorer (trec_eval/ir_measures) with flags recorded |
| Hyperparameter asymmetry | Baselines lose by under-tuning | Equal tuning budget, documented per system |
A reproducibility-strong SIGIR paper closes each row with an artifact, not a promise: the config file is the documentation.
Minimum reporting block for any empirical SIGIR paper
Put this in the paper (it fits in ~0.3 page and pre-empts three review objections):
- Collections with versions; train/dev/test usage; any filtering.
- Index/build settings for every system, baselines included.
- Tuning protocol: search space, budget, selection metric, dev split — symmetric across systems.
- Seeds: how many, and whether tables show mean, sd, and the significance test.
- Compute: hardware, wall-clock for index+train+retrieve, and query latency setup.
- Pointer to the repository with run files (see
sigir-artifact-evaluation).
# config-as-artifact: one file per reported system, committed to the repo
system: ours-dense-v2
collection: msmarco-passage/dev/small # ir_datasets id
index: {tokenizer: bert-base-uncased, max_len: 256, stride: 128}
train: {seeds: [13, 42, 71], batch: 64, lr: 2e-5, epochs: 3}
eval: {tool: ir_measures, metrics: [nDCG@10, RR@10, R@1000], qrels: official}
significance: {test: paired-t, correction: bonferroni, alpha: 0.05}Writing a Reproducibility track paper
A reproduction study is not a re-run; it is an investigation. The track rewards:
- Target selection with stakes — results the community builds on (a widely cited ranker, a standard baseline configuration, a claimed efficiency win).
- Faithful reimplementation first: reproduce with the original artifacts where they exist; document every forced deviation and why.
- Divergence analysis as the contribution: when numbers differ, isolate the cause (collection version? tuning? eval tool?) with controlled toggles — the drift table above is your experimental design.
- Generalization probes: does the original conclusion survive new collections, matched tuning, or current baselines? "Holds, but the margin halves under equal tuning" is a publishable, community-serving finding.
- Respectful register: the genre convention is scientific, not gotcha — criticize configurations, not authors, and give original authors' artifacts credit where due.
Anti-patterns the track's reviewers flag: reproducing only the headline number while skipping ablations; declaring "failure to reproduce" without exhausting configuration space; and shipping a reproduction whose own pipeline is unpinned (the irony reject).
Show full SKILL.md (333 more words)Show less
Decay planning: the three-year audience
A SIGIR paper's reproducibility has two audiences with different failure modes: the reviewer this spring (needs the 10-minute runnable path) and the researcher in three years (meets link rot, dataset takedowns, deprecated toolkit APIs, and vanished model checkpoints). Plan for the second audience explicitly:
- Cite collections by stable identifiers (
ir_datasetsids, TREC track names, dataset DOIs), never by lab-server URLs. - Freeze an archival copy of your own artifacts (Zenodo/institutional DOI) at camera-ready; GitHub is a working mirror, not an archive.
- Record toolkit versions in the paper text, not only in the lockfile — the paper outlives the repository more often than authors expect.
- If a resource you depend on has restrictive terms (query logs, commercial APIs), state what a future reproducer can do without it: which tables survive, which die.
- Leave the qrels/run checksums in the repo README; three years later they are the only way to prove a re-download matches the evaluated state.
Reproducibility as review defense for regular papers
- Close comparisons without variance are the most common SIGIR experimental criticism; three seeds with mean ± sd is cheap insurance for neural systems.
- An honest "we could not reproduce baseline X's published number; we report our best faithful configuration (details in repo)" reads as strength, not weakness — the community knows the drift problem intimately.
- Never copy baseline numbers across collections or eval setups from other papers' tables without saying so; mixed-provenance tables are a known reject trigger.
Quick self-audit before submission
- Every number in every table regenerates from a shipped run file plus one documented command.
- Every system row has a config file; diffs between systems are visible as config diffs, not prose.
- The seeds behind each neural row are enumerable, and the shipped run is identified (which seed, or the per-topic mean).
- A colleague outside the project reproduced Table 1 from the repo README without asking questions (the strongest cheap test available).
- The paper's reporting block and the repo's configs agree — reviewers diff them when suspicious.
Output format
[Mode] hardening a regular paper / Reproducibility track study
[Drift audit] rows closed with artifacts: <k>/7 (collection/index/processing/pool/seed/tool/tuning)
[Reporting block] present in paper y/n; missing items <list>
[For repro studies] target + stakes / faithfulness log / divergence causes isolated
[Variance] seeds <n>, mean±sd shown y/n, test named y/n
[Biggest residual risk] <the one unpinned thing a reviewer will find>© 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 SIGIR-Skills/skills/sigir-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigir 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 |
|---|---|---|---|---|---|---|
| Sigir Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | 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 |
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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.
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Questions about Sigir Reproducibility
What does Sigir Reproducibility do?
A skill your agent uses when strengthening the reproducibility of a SIGIR paper or preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline, seeds and variance for neural…. Sigir Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of a SIGIR paper or preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline, seeds and variance for neural rankers, documenting collection versions and index settings, and structuring a reproduction study of published IR results with honest divergence analysis.
When should I use Sigir Reproducibility?
Sigir Reproducibility fits situations like: strengthening the reproducibility of a SIGIR paper; preparing a SIGIR Reproducibility track submission — pinning the retrieval pipeline; seeds and variance for neural rankers; documenting collection versions and index settings.
How do I install Sigir Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigir-reproducibility -a claude-code`. Or copy the skill folder (SIGIR-Skills/skills/sigir-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigir-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Sigir Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigir-reproducibility -a codex`. Or copy the skill folder (SIGIR-Skills/skills/sigir-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigir-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Sigir 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 sigir-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/sigir-reproducibility, .gemini/skills/sigir-reproducibility, .github/skills/sigir-reproducibility and .opencode/skills/sigir-reproducibility in your project.
What does Sigir Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Sigir Reproducibility is instructions for the agent only.
Does Sigir 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 Sigir 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 Sigir Reproducibility use?
Sigir 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 Sigir Reproducibility use?
About 1.8k tokens (SKILL.md is roughly 7.1k 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 Sigir Reproducibility?
Skills that share tags, products or a category with Sigir 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 Sigir 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.