Master Agreement Generator
affaan-m/ECC
Builds DRAFT counterparty agreements from one markdown template and a small JSON spec per party, with clauses picked by the party's role.
Agent skill
Colm Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public…
Install Colm Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-artifact-evaluation --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/COLM-Skills/skills/colm-artifact-evaluation .claude/skills/colm-artifact-evaluation && 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 "colm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-artifact-evaluation into .claude/skills/colm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-artifact-evaluation", 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/COLM-Skills/skills/colm-artifact-evaluationType 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 colm-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-artifact-evaluation --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/COLM-Skills/skills/colm-artifact-evaluation .agents/skills/colm-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "colm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-artifact-evaluation into .agents/skills/colm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-artifact-evaluation", 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 colm-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-artifact-evaluation --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/COLM-Skills/skills/colm-artifact-evaluation .cursor/skills/colm-artifact-evaluation && 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 "colm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-artifact-evaluation into .cursor/skills/colm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-artifact-evaluation", 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 COLM-Skills/skills/colm-artifact-evaluation--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 colm-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-artifact-evaluation --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/COLM-Skills/skills/colm-artifact-evaluation .gemini/skills/colm-artifact-evaluation && 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 "colm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-artifact-evaluation into .gemini/skills/colm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-artifact-evaluation", 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 colm-artifact-evaluationInstalls 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 colm-artifact-evaluation -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/COLM-Skills/skills/colm-artifact-evaluation .github/skills/colm-artifact-evaluation && 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 "colm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-artifact-evaluation into .github/skills/colm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-artifact-evaluation", 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 colm-artifact-evaluation -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 colm-artifact-evaluation --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/COLM-Skills/skills/colm-artifact-evaluation .opencode/skills/colm-artifact-evaluation && 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 "colm-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-artifact-evaluation into .opencode/skills/colm-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-artifact-evaluation", 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
colm-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 871 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public…
- Works in 5 steps: De-anonymize the repository; restore… → Upload weights/data with cards and… → Add the paper's OpenReview URL to every… → …
- Packaging the artifacts of a COLM paper — model weights
- SKILL.md covers The COLM artifact taxonomy, One-command reproduction, Anonymous review packaging and Datasheets for anything you…, plus 4 more sections
- Calls make
What it does
Colm Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public post-acceptance release, navigating licenses, API terms-of-service limits, and the absence of a formal COLM artifact track.
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 Legal & Compliance, covering Policy and terms drafting. 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
- Packaging the artifacts of a COLM paper — model weights
- Evaluation sets
- Cached model outputs — for anonymous review and public post-acceptance release
- Navigating licenses
Example prompts
- “/colm-artifact-evaluation”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- De-anonymize the repository; restore real hub org names and W&B links.
- Upload weights/data with cards and licenses; tag the exact code release cited in
- Add the paper's OpenReview URL to every artifact so provenance points both ways.
- Freeze a colm2026 git tag — the paper cites a snapshot, not a moving main.
- Update the paper's availability statement to match what actually shipped
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
Shell commands in SKILL.md call:
make
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
Colm Artifact Evaluation loads about 1.8k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 871 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). 871 words, ~1,814 tokens.
Download SKILL.mdSave it as
.claude/skills/colm-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- colm-artifact-evaluation
- description
- Use when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public post-acceptance release, navigating licenses, API terms-of-service limits, and the absence of a formal COLM artifact track.
COLM Artifact Evaluation
COLM had no formal artifact-evaluation track verifiable for the 2026 cycle (checked 2026-07-08; 待核实 each edition). That absence does not lower the bar — it moves the audit into ordinary review, where artifact quality influences scores without a rubric to appeal to. Package as if a skeptical reviewer will spend ten minutes with your materials, because at this venue one usually will.
The COLM artifact taxonomy
LM papers produce artifact types with very different release mechanics; inventory yours before deciding anything:
| Artifact | Review-time form | Release-time form | Blocking question |
|---|---|---|---|
| Code (training/eval) | Anonymized repo or supplement ZIP | Public repo, tagged release | Does one command reproduce one table? |
| Prompts | Verbatim appendix + files in package | Same, public | Exact strings, incl. system prompts? |
| Fine-tuned weights | Usually described, not uploaded (size) | Model hub upload with model card | Does the base model's license permit derivative release? |
| Training/eval data you built | Anonymized sample + datasheet | Full release with license | Any personal data, scraped ToS conflicts, or annotator-privacy issues? |
| Cached model outputs | Sample in supplement | Full archive | Does the provider's ToS permit publishing outputs at this scale? |
| Human-eval materials | Instructions + interface in appendix | Same | IRB/consent status stated? |
The two questions authors most often skip are in the right-hand column: derivative weight licensing and output-publication ToS. Both can void a promised release after acceptance — resolve them before the paper commits to anything.
One-command reproduction
The credibility core of the package is a single entry point per headline result:
# Makefile at package root — one target per main-text table/figure
table2: ## headline comparison, ~40 GPU-min or ~$8 API spend
python run_eval.py --config eval/run-042.yaml --out results/table2.csv
figure3: ## scaling curve from cached outputs (no model access needed)
python plots/scaling.py --cache outputs/cache.jsonl --out figs/figure3.pdf
verify: ## regenerate all numbers from cached outputs only
python verify_from_cache.py --tolerance 0.1The verify-from-cache target is the LM-specific trick: reviewers without GPUs or
API budgets can still confirm that your published numbers follow from your recorded
model responses. It converts "trust me" into "check me" at zero compute cost, and it
keeps working after API models drift or deprecate (colm-reproducibility).
Anonymous review packaging
- Build from a clean export and grep for identity leaks — the commands and channel
list live in
colm-supplementary; org-scoped model-hub IDs are the leak class unique to LM work. - If weights must be inspectable at review time, an anonymized hub account or a size-reduced distilled checkpoint are the workable options; a link to your lab's account is a double-blind violation under COLM's no-identifying-links rule.
- Include a
MANIFEST.md: inventory, license per item, compute needed per target, and what is deliberately absent with the reason ("training corpus omitted: contains licensed text; filtering scripts included instead").
Datasheets for anything you release
For each dataset or evaluation set: how items were created (author population, LLM-generated fraction — disclosable under the 2026 LLM policy), collection dates (this doubles as contamination documentation for future users), license and source licenses, known biases and coverage gaps, and a contamination canary if you want future training runs to be detectable. For model releases: intended use, evaluation scope, and known failure modes. These documents are cheap at packaging time and impossible to reconstruct honestly later.
Post-acceptance release sequence
- De-anonymize the repository; restore real hub org names and W&B links.
- Upload weights/data with cards and licenses; tag the exact code release cited in the camera-ready.
- Add the paper's OpenReview URL to every artifact so provenance points both ways.
- Freeze a
colm2026git tag — the paper cites a snapshot, not a movingmain. - Update the paper's availability statement to match what actually shipped
(
colm-camera-readyowns the deadline: August 7, 2026 this cycle).
Show full SKILL.md (327 more words)Show less
The ten-minute reviewer walkthrough
Dry-run the package as the busiest plausible reviewer before every upload. The sequence they follow is predictable, so optimize for it in order:
- Minute 1-2: open
MANIFEST.md. If there is no manifest, they grep for a README and form their opinion from whatever half-stale file they find. The manifest is the cheapest score you will ever buy. - Minute 3-4: look for the entry point.
make table2or an equivalent single command. If the first thing they see is a 400-line setup guide with cluster assumptions, the walkthrough ends here. - Minute 5-7: run
verifyfrom cache. No GPUs, no keys, under a minute of compute — this is the step that actually gets executed in practice, which is why the cached-outputs archive earns its place in the package. - Minute 8-9: spot-check a prompt file against the paper's appendix. Any mismatch between packaged prompts and printed prompts contaminates trust in both.
- Minute 10: skim the code for identity leaks and hardcoded secrets — partly
ethics-duty, partly curiosity. This is where an org-scoped
from_pretrainedstring ends your anonymity.
Sizing note: keep the review package lean — cached outputs can be sampled down to
what verify needs, with the full archive promised for release. No supplementary
size cap was verifiable for COLM 2026 (待核实), but a multi-gigabyte upload fails
socially even where it succeeds technically.
Longevity: the two-year test
A COLM artifact's real audience arrives later — the group in two years trying to
compare against you. Two cheap investments serve them: freeze an environment
manifest (exact package versions; a container digest if you can), and write the
MANIFEST.md assuming every external URL in it will eventually rot — name
artifacts by content hash where possible so mirrors stay verifiable. The venue
publishes on OpenReview, where your artifact links are permanently attached to the
paper's public page; links that die quietly are the failure mode, so prefer
archival hosts for anything you would want cited.
Output format
[Inventory] code ▢ prompts ▢ weights ▢ data ▢ cached-outputs ▢ human-eval ▢
[Legal gates] base-model license: <ok/blocks release> output-ToS: <ok/limits>
[One-command check] targets exist for: <tables/figures> verify-from-cache: ▢
[Anonymity] package clean / leaks: <items>
[Manifest + datasheets] present / missing: <which>
[Release plan] <ordered steps with dates>© 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 COLM-Skills/skills/colm-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Colm Artifact Evaluation 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 |
|---|---|---|---|---|---|---|
| Colm Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Master Agreement Generatoraffaan-m/ECC | 276k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Pii Contract Analyzegregmos/PII-Shield | 149 | — | ~8.9k | Automated safety check: Notes | MIT | |
| Privacy Eukimlawtech/korean-privacy-terms | 586 | — | ~968 | Automated safety check: Pass | Apache-2.0 | |
| Agents In The Teamborghei/Claude-Skills | 886 | — | ~4.2k | Automated safety check: Pass | MIT | |
| Terms Of Service Generatorzubair-trabzada/ai-legal-claude | 1.8k | — | ~2.9k | Automated safety check: Pass | None |
Similar skills
Pii Contract Analyze
gregmos/PII-Shield
Universal legal document processor with PII anonymization. An agent skill from gregmos/PII-Shield.
Privacy Eu
kimlawtech/korean-privacy-terms
EU 사용자 대상 서비스용 Privacy Notice·Terms of Service·Consent Modal·Cookie Banner 자동 생성.
Agents In The Team
borghei/Claude-Skills
Run delivery when AI coding and ops agents take tickets. An agent skill from borghei/Claude-Skills.
Terms Of Service Generator
zubair-trabzada/ai-legal-claude
Generates complete, GDPR/CCPA-compliant Terms of Service for a website or SaaS product, with plain English summaries for each section
Tos Clause Scanner
zebbern/claude-code-guide
Audit Terms of Service, user agreements, and privacy policies for consumer risks, producing a structured report that flags unfair clauses, data traps, and liability issues.
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Categories
Questions about Colm Artifact Evaluation
What does Colm Artifact Evaluation do?
A skill your agent uses when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public…. Colm Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging the artifacts of a COLM paper — model weights, training data, prompts, evaluation sets, and cached model outputs — for anonymous review and public post-acceptance release, navigating licenses, API terms-of-service limits, and the absence of a formal COLM artifact track.
When should I use Colm Artifact Evaluation?
Colm Artifact Evaluation fits situations like: packaging the artifacts of a COLM paper — model weights; evaluation sets; cached model outputs — for anonymous review and public post-acceptance release; navigating licenses.
How do I install Colm Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-artifact-evaluation -a claude-code`. Or copy the skill folder (COLM-Skills/skills/colm-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/colm-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Colm Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-artifact-evaluation -a codex`. Or copy the skill folder (COLM-Skills/skills/colm-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/colm-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Colm Artifact Evaluation 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 colm-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/colm-artifact-evaluation, .gemini/skills/colm-artifact-evaluation, .github/skills/colm-artifact-evaluation and .opencode/skills/colm-artifact-evaluation in your project.
What does Colm Artifact Evaluation need to run?
Going by SKILL.md and its folder, Colm Artifact Evaluation needs the command-line tools its instructions call (make). Our summary lists: Python 3.
Does Colm Artifact Evaluation 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 Colm Artifact Evaluation 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 Colm Artifact Evaluation use?
Colm Artifact Evaluation 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 Colm Artifact Evaluation use?
About 1.8k tokens (SKILL.md is roughly 7.3k 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 Colm Artifact Evaluation?
Skills that share tags, products or a category with Colm Artifact Evaluation: Master Agreement Generator (affaan-m/ECC, 276k stars), Pii Contract Analyze (gregmos/PII-Shield, 149 stars), Privacy Eu (kimlawtech/korean-privacy-terms, 586 stars) and Agents In The Team (borghei/Claude-Skills, 886 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Colm Artifact Evaluation?
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.