Scientific Toolkit Skill
zLanqing/codex-claude-academic-skills
Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation…
A skill your agent uses when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical…
Install Jeg Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jeg-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jeg-data-analysis --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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis .claude/skills/jeg-data-analysis && 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 "jeg-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis into .claude/skills/jeg-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jeg-data-analysis", 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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysisType 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 jeg-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jeg-data-analysis --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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis .agents/skills/jeg-data-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jeg-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis into .agents/skills/jeg-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jeg-data-analysis", 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 jeg-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jeg-data-analysis --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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis .cursor/skills/jeg-data-analysis && 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 "jeg-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis into .cursor/skills/jeg-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jeg-data-analysis", 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 Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis--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 jeg-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jeg-data-analysis --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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis .gemini/skills/jeg-data-analysis && 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 "jeg-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis into .gemini/skills/jeg-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jeg-data-analysis", 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 jeg-data-analysisInstalls 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 jeg-data-analysis -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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis .github/skills/jeg-data-analysis && 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 "jeg-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis into .github/skills/jeg-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jeg-data-analysis", 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 jeg-data-analysis -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 jeg-data-analysis --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/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis .opencode/skills/jeg-data-analysis && 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 "jeg-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis into .opencode/skills/jeg-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jeg-data-analysis", 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
jeg-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 663 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical…
- Auditing Journal of Economic Growth (JEG) empirical estimates
- SKILL.md covers When to trigger, Empirical growth checklist, Theory / calibration checklist and Growth-mechanism audit table, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Calibrated growth models
What it does
Jeg Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical datasets, spatial (Conley) inference, robustness, and reproducibility for growth and comparative-development manuscripts.
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 Data & Analytics, covering Data analysis and 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
- Auditing Journal of Economic Growth (JEG) empirical estimates
- Calibrated growth models
- Transition paths
- Cross-country and subnational panels
Example prompts
- “/jeg-data-analysis”
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
Jeg Data Analysis loads about 1.5k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 663 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). 663 words, ~1,521 tokens.
Download SKILL.mdSave it as
.claude/skills/jeg-data-analysis/SKILL.md (or your agent's skills folder).- name
- jeg-data-analysis
- description
- Use when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical datasets, spatial (Conley) inference, robustness, and reproducibility for growth and comparative-development manuscripts.
Data Analysis (jeg-data-analysis)
When to trigger
- You are estimating cross-country, panel, historical, or regional growth models
- A theory paper includes calibration, simulation, or transition dynamics
- Results need robustness, decomposition, or sensitivity checks for JEG
Empirical growth checklist
- Define the growth outcome: level, growth rate, convergence speed, productivity, human capital, fertility, technology, institutions, or development outcome.
- Document the unit and horizon: country-year, region-decade, cohort, household, firm, or historical panel.
- Separate long-run levels from short-run growth dynamics.
- Show sample construction, merge rules, missingness, and influential observations.
- Use specifications that match the question: convergence regressions, panel FE, IV, DID/RDD around reforms, synthetic controls, or structural estimates.
Theory / calibration checklist
- State calibrated parameters, data moments, and source for each moment.
- Separate targeted from untargeted moments.
- Report transition paths and steady states clearly.
- Stress-test key elasticities, discount rates, depreciation, fertility, human capital, and technology parameters.
- Make code reproducible enough to regenerate figures and tables.
Growth-mechanism audit table
Before drafting results, create a table with:
- Mechanism: human capital, fertility, technology, institutions, trade, finance, migration, or OLG channel.
- Object: growth rate, income level, TFP, convergence speed, transition path, or welfare.
- Discipline: data moment, calibration target, theorem assumption, or identification source.
- Main sensitivity: parameter or sample choice most likely to overturn the result.
- Replication artifact: code or file that regenerates the exhibit.
If an estimate or simulation does not map to a mechanism row, it is probably not central enough for JEG.
Spatial and historical inference discipline
Comparative-development empirics at JEG are usually geocoded, which changes the inference defaults:
- Report Conley standard errors at multiple distance cutoffs (e.g., 100/250/500 km) for any gridded or regional outcome; clustered SEs at the modern administrative level are necessary but not sufficient.
- When historical units do not coincide with modern ones, cluster at the historical unit — the level at which the treatment was assigned — and document the crosswalk.
- Pre-empt the critique that persistence t-statistics can be inflated by smooth spatial trends: include flexible geographic controls (latitude-longitude polynomials or macro-region fixed effects) plus a spatial-noise placebo test.
- For very long panels, keep measurement vintages separate: reconstructed pre-1950 series, modern national accounts, and nighttime lights are not interchangeable; show the result within each vintage where feasible.
Show full SKILL.md (301 more words)Show less
Worked vignette — auditing a comparative-development panel
Illustrative setup: 2,400 grid cells in 41 countries; outcome is log light density in 2020; regressor is distance to a historical trade hub; candidate instrument is least-cost-path placement.
- Unit/horizon: cell-level cross-section answering a long-run level question, so convergence-dynamics machinery is unnecessary; the persistence design applies.
- Inference: coefficient 0.21; country-clustered SE 0.05, Conley 250 km SE 0.08, Conley 500 km SE 0.09 — report all three; the claim survives the widest cutoff.
- Mechanism row: schooling in 1960 absorbs roughly 40% of the coefficient (illustrative), so human capital becomes a lead exhibit, not a robustness afterthought.
- Main sensitivity: dropping cells within 50 km of modern capitals moves the estimate to 0.17; capital proximity goes into the audit table as the result's weakest joint.
Estimator defaults by growth question
- Long-run level question (deep determinants, persistence) → cross-sectional or grid design + Conley inference + mechanism decomposition.
- Convergence-speed question → panel estimation alert to Nickell bias; system GMM only with instrument-count discipline and Hansen/AR(2) reporting.
- Reform-timing question → modern staggered-adoption DID estimators with pre-trend evidence, never naive TWFE.
- Theory-driven quantitative question → calibrated model with targeted and untargeted moments kept visibly separate.
- Demographic or fertility question → cohort or census microdata aggregated to the mechanism's unit; verify the transition timing is identified by the data rather than assumed by the specification.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JEG (growth) uses cross-country and long-run panels with deep endogeneity; foreground identification and robustness to alternatives.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley. - Re-fit off one handle:
audit_result(result_id)lists missing checks + the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.
Output format
[Paper type] empirical / theory / mixed
[Data or model object] ...
[Main estimator/calibration] ...
[Robustness or sensitivity] ...
[Reproducibility gaps] ...
[Next step] jeg-tables-figures© 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 Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jeg Data Analysis 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 |
|---|---|---|---|---|---|---|
| Jeg Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Scientific Toolkit SkillzLanqing/codex-claude-academic-skills | 4.7k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Data Validationw95/awesome-claude-corporate-skills | 239 | 1 repos | ~2.4k | Automated safety check: Pass | MIT | |
| Conbio Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~1k | Automated safety check: Pass | None | |
| Ors Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~993 | Automated safety check: Pass | None | |
| Poq Data Analysisfranklee16/academic-research-skills | 223 | 1 repos | ~1.1k | Automated safety check: Pass | None |
Similar skills
Data Validation
w95/awesome-claude-corporate-skills
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection.
Conbio Data Analysis
franklee16/academic-research-skills
A skill your agent uses when executing and reporting the analysis for a Conservation Biology manuscript so it survives expert, double-blind review — appropriate ecological/statistical models, honest…
Ors Data Analysis
franklee16/academic-research-skills
A skill your agent uses when running and reporting the computational study for an Operations Research (OR) manuscript — benchmark instances, baselines, reproducible experiments, statistical care for…
Poq Data Analysis
franklee16/academic-research-skills
A skill your agent uses when executing and reporting the analysis for a Public Opinion Quarterly (POQ) manuscript so it survives expert, double-blind review — design-based inference that respects…
Rof Data Analysis
franklee16/academic-research-skills
A skill your agent uses when auditing Review of Finance empirical or theoretical analysis: sample construction, identification, asset-pricing tests, corporate-finance variables, robustness, code…
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 Jeg Data Analysis
What does Jeg Data Analysis do?
A skill your agent uses when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical…. Jeg Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical datasets, spatial (Conley) inference, robustness, and reproducibility for growth and comparative-development manuscripts.
When should I use Jeg Data Analysis?
Jeg Data Analysis fits situations like: auditing Journal of Economic Growth (JEG) empirical estimates; calibrated growth models; transition paths; cross-country and subnational panels.
How do I install Jeg Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jeg-data-analysis -a claude-code`. Or copy the skill folder (Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jeg-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Jeg Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jeg-data-analysis -a codex`. Or copy the skill folder (Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jeg-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Jeg Data Analysis 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 jeg-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jeg-data-analysis, .gemini/skills/jeg-data-analysis, .github/skills/jeg-data-analysis and .opencode/skills/jeg-data-analysis in your project.
What does Jeg Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Jeg Data Analysis is instructions for the agent only.
Does Jeg Data Analysis 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 Jeg Data Analysis 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 Jeg Data Analysis use?
Jeg Data Analysis 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 Jeg Data Analysis 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 Jeg Data Analysis?
Skills that share tags, products or a category with Jeg Data Analysis: Scientific Toolkit Skill (zLanqing/codex-claude-academic-skills, 4.7k stars), Data Validation (w95/awesome-claude-corporate-skills, 239 stars), Conbio Data Analysis (franklee16/academic-research-skills, 223 stars) and Ors Data Analysis (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jeg Data Analysis?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.