Exploratory Data Analysis
spacering-net/codeg
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.
A skill your agent uses when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript.
Install Cogpsych Data Analysis
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-data-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis .claude/skills/cogpsych-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 "cogpsych-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis into .claude/skills/cogpsych-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-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 cogpsych-data-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis .agents/skills/cogpsych-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 "cogpsych-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis into .agents/skills/cogpsych-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cogpsych-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 cogpsych-data-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis .cursor/skills/cogpsych-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 "cogpsych-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis into .cursor/skills/cogpsych-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cogpsych-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 Cognitive-Psychology-Skills/skills/cogpsych-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 cogpsych-data-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis .gemini/skills/cogpsych-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 "cogpsych-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis into .gemini/skills/cogpsych-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cogpsych-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 cogpsych-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 cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis .github/skills/cogpsych-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 "cogpsych-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis into .github/skills/cogpsych-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cogpsych-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 cogpsych-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 cogpsych-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/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis .opencode/skills/cogpsych-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 "cogpsych-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Cognitive-Psychology-Skills/skills/cogpsych-data-analysis into .opencode/skills/cogpsych-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cogpsych-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
cogpsych-data-analysis- GitHub stars
- 1.2k
- Token cost
- ~2k tokens
- SKILL.md length
- 763 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript.
- Works in 6 steps: Fit and compare models, don't just fit… → Show recovery. Demonstrate parameter… → Use the right hierarchical structure.… → …
- Analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript
- SKILL.md covers When to trigger, Reporting norms Cognitive…, Robustness and Worked micro-example…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Cogpsych Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript. The journal expects principled model fitting and comparison (AIC/BIC/Bayes factors), parameter and model recovery, (generalized) linear mixed models or hierarchical Bayesian estimation where apt, and effect sizes with uncertainty — all reproducible from shared code. Guides the analysis and modeling; it does not fabricate results.
Its SKILL.md is about 2k 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. 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
- Analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript
- Tasks that involve Data analysis
Example prompts
- “/cogpsych-data-analysis”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Fit and compare models, don't just fit one. Report fit for your model and the rival(s) under
- Show recovery. Demonstrate parameter recovery (can the fitting procedure recover known
- Use the right hierarchical structure. Crossed random effects over subjects and items call for
- Effect sizes + uncertainty for behavior. Report standardized/unstandardized effect sizes with
- Confirmatory vs. exploratory. Separate pre-committed model comparisons and tests from
- Reproducible. Model and analysis code, with seeds and pinned versions, regenerate every reported
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
Cogpsych Data Analysis loads about 2k tokens when it runs. Until then it costs about 114 tokens; SKILL.md has 763 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). 763 words, ~2,025 tokens.
Download SKILL.mdSave it as
.claude/skills/cogpsych-data-analysis/SKILL.md (or your agent's skills folder).- name
- cogpsych-data-analysis
- description
- Use when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript. The journal expects principled model fitting and comparison (AIC/BIC/Bayes factors), parameter and model recovery, (generalized) linear mixed models or hierarchical Bayesian estimation where apt, and effect sizes with uncertainty — all reproducible from shared code. Guides the analysis and modeling; it does not fabricate results.
Data Analysis & Model Fitting (cogpsych-data-analysis)
Cognitive Psychology holds analyses to a model-based standard: fit the formal model, compare it to rivals with principled criteria, demonstrate that parameters and models are recoverable, use mixed models or hierarchical Bayesian estimation where the design demands it, and report effect sizes with uncertainty for behavioral results — all regenerable from deposited code. This is the experiment-to-model-fit loop that defines the venue.
When to trigger
- Fitting the formal model and comparing it to rival accounts
- Running the behavioral analyses (mixed models, hierarchical Bayesian, contrasts)
- A reviewer asked for model comparison, recovery, robustness, or fuller disclosure
- Preparing analysis/model code and a data dictionary for deposit
Reporting norms Cognitive Psychology expects
- Fit and compare models, don't just fit one. Report fit for your model and the rival(s) under matched flexibility; compare with AIC/BIC, cross-validation, or Bayes factors as appropriate, and say what the comparison licenses.
- Show recovery. Demonstrate parameter recovery (can the fitting procedure recover known parameters from simulated data) and model recovery (does the comparison criterion pick the generating model) — without these, a fit edge is not interpretable.
- Use the right hierarchical structure. Crossed random effects over subjects and items call for (generalized) linear mixed models; for cognitive models, hierarchical Bayesian estimation pools strength across participants. Justify the structure; don't aggregate away the variance.
- Effect sizes + uncertainty for behavior. Report standardized/unstandardized effect sizes with confidence/credible intervals for key behavioral results, not just p-values and stars.
- Confirmatory vs. exploratory. Separate pre-committed model comparisons and tests from exploratory model exploration; do not present a post hoc winning model as predicted.
- Reproducible. Model and analysis code, with seeds and pinned versions, regenerate every reported
fit, figure, and table in a fresh session (see
cogpsych-open-science-and-transparency).
Robustness
- Show the conclusion survives reasonable alternative model specifications, priors (for Bayesian fits), and exclusion choices; report sensitivity, not a single fragile fit. Report convergence diagnostics (e.g., R-hat, ESS) for Bayesian models.
Worked micro-example (illustrative numbers)
A preregistered three-experiment recognition-memory program fitting UVSD vs. DPSD to confidence-ROC data.
Model comparison (preregistered) — pooled across Exps 1-3
Fit (hierarchical Bayesian, matched flexibility):
UVSD favored: dBIC = 14 vs. DPSD; Bayes factor ~ 30 in favor of UVSD
Recovery (required): parameter recovery good (recovered d', sigma within
credible intervals); model recovery ~ 92% correct at the design's N/trials
Diagnostic signature: z-ROC slope 0.78, 95% CrI [0.72, 0.84], and linear
(no reliable curvature) — the qualitative pattern UVSD predicts and DPSD
forbids, consistent across all three experiments
Behavioral effect (mixed model)
List-strength manipulation on d': b = 0.31, 95% CI [0.18, 0.44]
Exploratory (labeled)
A small response-bias drift surfaced post hoc; reported as exploratoryWhy this passes Cognitive Psychology scrutiny: the model is compared (not just fit), recovery makes the comparison interpretable, the qualitative signature corroborates the fit index, hierarchy respects subject/item variance, and the exploratory drift is honestly demoted.
Analysis-stage reviewer pushback and the venue fix
| Reviewer pushback | What it signals here | Cognitive Psychology fix |
|---|---|---|
| "You only fit your model" | one-model storytelling | fit the rival under matched flexibility; report AIC/BIC/BF and what it licenses |
| "Better fit may be overfitting" | flexibility imbalance | add model recovery + cross-validation; penalize complexity |
| "Can you recover these parameters?" | identifiability doubt | run and report parameter + model recovery simulations |
| "Aggregated means hide variance" | wrong error structure | refit with crossed-random-effects mixed model / hierarchical Bayesian |
| "Is this the model you predicted?" | post hoc selection | pre-commit the comparison; relabel post hoc fits exploratory |
| "I can't rerun your fits" | reproducibility gate | ship seeded model code + a fresh-session run log |
Show full SKILL.md (281 more words)Show less
Calibration anchors
- A model that is fit, compared, and recovered is the unit of evidence here — a single fit with a good index but no rival and no recovery is not persuasive.
- Trust a crossed qualitative prediction over a marginal fit advantage; report both and lead with the signature the rival forbids.
- Respect the data's hierarchy: aggregating over subjects or items inflates false positives and can bias parameter estimates; use mixed/hierarchical models and justify the random-effects structure.
- For Bayesian fits, report priors, convergence, and sensitivity — a fit without diagnostics is not reproducible evidence.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. Cognitive Psychology is experimental — within-subject designs and mixed models dominate; report the model, the effect size, and multiple-comparison control.
- Many outcomes / specifications:
romano_wolf(step-down FWER) orbenjamini_hochberg— report the adjusted threshold. - OVB sensitivity:
oster_delta/sensemakr. - Inference:
wild_cluster_bootstrap(few clusters),twoway_cluster/conley; multilevel data → cluster at the right level. - Re-fit off one handle:
audit_result(result_id)lists the missing checks and the exactsuggest_functionfor each. - Exhibits:
etable/did_summary_to_latexfrom the handle — no retyped numbers.
Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.
Anti-patterns
- Fitting only your model with no rival and no comparison criterion
- Claiming a fit advantage without matched flexibility, recovery, or cross-validation
- Aggregating to cell means and ignoring crossed subject/item variance
- p-values and stars with no effect size or interval for behavioral results
- Presenting a post hoc winning model as a predicted result
- Model/analysis code that does not regenerate the reported fits
Output format
【Model comparison】rivals fit under matched flexibility + criterion (AIC/BIC/BF)? [Y/N]
【Recovery】parameter + model recovery reported? [Y/N]
【Hierarchy】mixed model / hierarchical Bayesian where apt + diagnostics? [Y/N]
【Behavioral effects】effect sizes + intervals? [Y/N]
【Confirmatory vs exploratory】separated? [Y/N]
【Reproducible】seeded code + data dictionary + fresh-session check? [Y/N]
【Next】cogpsych-tables-figuresSupplementary resources
../../resources/external_tools.md— modeling, model-comparison,lme4/brms/Stan, JAGS, recovery simulation../../resources/official-source-map.md— statistical and modeling expectations
© 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 Cognitive-Psychology-Skills/skills/cogpsych-data-analysis of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cogpsych 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 |
|---|---|---|---|---|---|---|
| Cogpsych Data Analysis this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~2k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Excel and CSV Data Analysisbytedance/deer-flow | 84k | 4 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Exploratory Data AnalysisOleafly/Oleafly | 212 | 2 repos | ~3.4k | Automated safety check: Notes | MIT | |
| Pandas ProJeffallan/claude-skills | 12k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Python Executorcortega26/chile-hub | 113 | 2 repos | ~1.5k | Automated safety check: Pass | MIT |
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Questions about Cogpsych Data Analysis
What does Cogpsych Data Analysis do?
A skill your agent uses when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript. Cogpsych Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript.
When should I use Cogpsych Data Analysis?
Cogpsych Data Analysis fits situations like: analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript; tasks that involve Data analysis.
How do I install Cogpsych Data Analysis in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-data-analysis -a claude-code`. Or copy the skill folder (Cognitive-Psychology-Skills/skills/cogpsych-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cogpsych-data-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Cogpsych Data Analysis in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-data-analysis -a codex`. Or copy the skill folder (Cognitive-Psychology-Skills/skills/cogpsych-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cogpsych-data-analysis in your project. Codex loads it when a task matches its description.
Can I use Cogpsych 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 cogpsych-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/cogpsych-data-analysis, .gemini/skills/cogpsych-data-analysis, .github/skills/cogpsych-data-analysis and .opencode/skills/cogpsych-data-analysis in your project.
What does Cogpsych Data Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Cogpsych Data Analysis is instructions for the agent only.
Does Cogpsych 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 Cogpsych 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 Cogpsych Data Analysis use?
Cogpsych 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 Cogpsych Data Analysis use?
About 2k tokens (SKILL.md is roughly 8.1k 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 Cogpsych Data Analysis?
Skills that share tags, products or a category with Cogpsych Data Analysis: Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars), Exploratory Data Analysis (Oleafly/Oleafly, 212 stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Cogpsych Data Analysis?
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.