QuantMind Training Config Generator
qusong0627/QuantMind
Turns a plain-language model training request into a validated QuantMind training config file that can be imported from the Model Training page.
Agent skill
Corl Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging the artifacts of a CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as…
MITAuto-check passed
Install Corl Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill corl-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-artifact-evaluation .claude/skills/corl-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 "corl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-artifact-evaluation into .claude/skills/corl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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/CoRL-Skills/skills/corl-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 corl-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-artifact-evaluation .agents/skills/corl-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 "corl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-artifact-evaluation into .agents/skills/corl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 corl-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-artifact-evaluation .cursor/skills/corl-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 "corl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-artifact-evaluation into .cursor/skills/corl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 CoRL-Skills/skills/corl-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 corl-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills corl-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/CoRL-Skills/skills/corl-artifact-evaluation .gemini/skills/corl-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 "corl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-artifact-evaluation into .gemini/skills/corl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 corl-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 corl-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/CoRL-Skills/skills/corl-artifact-evaluation .github/skills/corl-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 "corl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-artifact-evaluation into .github/skills/corl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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 corl-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 corl-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/CoRL-Skills/skills/corl-artifact-evaluation .opencode/skills/corl-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 "corl-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CoRL-Skills/skills/corl-artifact-evaluation into .opencode/skills/corl-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "corl-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
corl-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 745 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 CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as…
- Packaging the artifacts of a CoRL paper — code
- SKILL.md covers Artifact inventory for a…, The reuse test, Review-time packaging… and Post-acceptance: durable release, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Training configs
What it does
Corl Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging the artifacts of a CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as anonymous review-time evidence and as durable public releases after acceptance, in a venue with no formal artifact-badging track.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
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 CoRL paper — code
- Training configs
- Demonstration datasets
- Policy checkpoints
Example prompts
- “/corl-artifact-evaluation”
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
Links to these hosts (documentation or services it may open):
corl.org
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
Corl Artifact Evaluation loads about 1.7k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 745 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). 745 words, ~1,660 tokens.
Download SKILL.mdSave it as
.claude/skills/corl-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- corl-artifact-evaluation
- description
- Use when packaging the artifacts of a CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as anonymous review-time evidence and as durable public releases after acceptance, in a venue with no formal artifact-badging track.
CoRL Artifact Evaluation
CoRL has no separate artifact-evaluation track or badge system in the 2026 materials verified for this pack (a formal AE track remains 待核实 each cycle). Artifacts are instead judged twice, informally: by reviewers deciding whether to trust your tables, and by the community deciding — for years afterward — whether your method becomes a baseline. Package for both audiences from the start.
Artifact inventory for a robot-learning paper
| Artifact | Review-time form (anonymous) | Public form (post-acceptance) |
|---|---|---|
| Training code + configs | Scrubbed ZIP in the supplementary | GitHub repo, tagged release matching the paper |
| Evaluation harness | Same ZIP; scripts + fixed init-state lists | Same repo; the part reused most by others |
| Demonstration data | Small sample in ZIP; full set described | Archive with DOI + datasheet, license stated |
| Policy checkpoints | Optional if small; else described | Hosted weights keyed to each results table |
| Sim environments / tasks | Env definitions + versions in ZIP | Repo or upstream PR to the benchmark suite |
| Hardware recipe | Appendix spec (rig, control interface) | Project page: BOM-level detail, photos |
| Overview video | Supplementary upload (≤ 250 MB, 2026 cap) | Project page / video host — PMLR takes no video |
The reuse test
Design the release around one question: can a stranger reproduce your evaluation without emailing you? Concretely, a competent robot-learning grad student should be able to (1) install, (2) run evaluation with a released checkpoint, and (3) regenerate one paper table, in an afternoon, in simulation. Training reproduction and hardware reproduction are aspirational tiers above this floor — label the tiers honestly rather than implying all three.
Release tiers — declare one per artifact in the README:
T1 evaluate: released checkpoint + eval script reproduce Table N in sim
T2 retrain: configs + data (or data recipe) reproduce the training run
within the reported seed spread
T3 re-embody: rig documentation sufficient to attempt hardware replicationReview-time packaging (anonymous)
- One ZIP, one top-level README, a table mapping
paper table → command. - Determinize what you can: fixed evaluation seeds, fixed init-state lists,
pinned dependency versions (
corl-reproducibilityowns the manifest format). - Anonymize mechanically, then by eyeball: repo history stripped, paths cleaned, no W&B/HF org names, no grant numbers in license headers, and remember the URL rule — an anonymized page on a lab-named domain still leaks.
- Keep it runnable without a robot: reviewers do not have your hardware, so the artifact's demonstrable slice is sim evaluation plus logs/video of the hardware runs. Ship episode-level logs (CSV) for every hardware table.
Post-acceptance: durable release
- Timing. The 2026 camera-ready deadline (October 12) is when links get frozen into the PMLR record — stand up the public repo, dataset archive, and project page before finalizing the PDF so the printed URLs are real.
- Durability ladder. Lab web servers die with funding cycles: put datasets and static artifacts in DOI-granting archives, code in a tagged repository release, and treat the project page as a pointer hub rather than the sole home.
- Checkpoint provenance. Key each released checkpoint to its table and seed
("
ckpt_t3_s2= Table 3, seed 2"); publishing only a best-seed checkpoint while the paper reports seed means invites a mismatch report you'll answer publicly — CoRL reviews of accepted papers are public, and so is the follow-up scrutiny. - License deliberately. Code (permissive vs copyleft), data (usage terms, consent constraints for human video), and weights (increasingly their own license class) are three separate decisions; "no license" means "nobody may legally build on this."
Show full SKILL.md (240 more words)Show less
Benchmarks and datasets as first-class artifacts
If the artifact is the contribution (a task suite, a large demo corpus), the bar rises from reuse to stewardship:
- Version the benchmark explicitly (v1.0 at camera-ready) and changelog any post-publication fix; silent edits corrupt every downstream comparison.
- Publish the evaluation protocol as executable code, not prose — episode counts, init distributions, success criteria — so future papers cite numbers produced by your harness rather than reimplementations.
- Provide a datasheet: collection method, operator demographics if teleop, filtering, known biases, consent/licensing status of any human footage.
Failure patterns that surface after publication
Because CoRL reviews of accepted papers are public and the community reuses artifacts aggressively, release defects surface fast and visibly:
- The vanished environment: the eval harness imports a benchmark fork whose branch was deleted; vendor the environment code or pin an archived copy.
- The config drift: the repo's default config differs from the paper's runs; ship the exact configs used, named per table, and make defaults match.
- The GPU-only afternoon: an evaluation that silently requires 8×A100 to run at all; document minimum hardware and offer a reduced smoke-test target.
- The consent surprise: teleop or human-video data released without the consent scope to permit it; resolve data-rights questions before the paper promises release, not after.
Release checklist
[ ] Tier (T1/T2/T3) declared per artifact; T1 actually tested by a
teammate on a clean machine
[ ] README: install steps, table→command map, expected tolerances
[ ] Checkpoints keyed to tables and seeds; selection rule stated
[ ] Dataset archived with DOI, datasheet, and license
[ ] Hardware logs + uncut eval video published for hardware tables
[ ] Licenses chosen for code, data, and weights separately
[ ] All URLs live before camera-ready freeze (2026: Oct 12 AoE)
[ ] Anonymous variant retired only after decisions are outRe-check the live cycle for any new artifact policy, badge program, or required availability statement at https://www.corl.org/contributions/instruction-for-authors — venue policy here is young and moves quickly, like the field itself.
© 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 CoRL-Skills/skills/corl-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Corl 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 |
|---|---|---|---|---|---|---|
| Corl Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| QuantMind Training Config Generatorqusong0627/QuantMind | 1.7k | — | ~1.5k | Automated safety check: Pass | AGPL-3.0 | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Artifacts Buildernexu-io/open-design | 100k | — | ~347 | Automated safety check: Pass | Apache-2.0 | |
| Ito Trainingaffaan-m/ECC | 276k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT |
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Questions about Corl Artifact Evaluation
What does Corl Artifact Evaluation do?
A skill your agent uses when packaging the artifacts of a CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as…. Corl Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging the artifacts of a CoRL paper — code, training configs, demonstration datasets, policy checkpoints, simulation environments, and benchmark definitions — as anonymous review-time evidence and as durable public releases after acceptance, in a venue with no formal artifact-badging track.
When should I use Corl Artifact Evaluation?
Corl Artifact Evaluation fits situations like: packaging the artifacts of a CoRL paper — code; training configs; demonstration datasets; policy checkpoints.
How do I install Corl Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill corl-artifact-evaluation -a claude-code`. Or copy the skill folder (CoRL-Skills/skills/corl-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/corl-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Corl Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill corl-artifact-evaluation -a codex`. Or copy the skill folder (CoRL-Skills/skills/corl-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/corl-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Corl 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 corl-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/corl-artifact-evaluation, .gemini/skills/corl-artifact-evaluation, .github/skills/corl-artifact-evaluation and .opencode/skills/corl-artifact-evaluation in your project.
What does Corl Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Corl Artifact Evaluation is instructions for the agent only.
Does Corl Artifact Evaluation access the network?
SKILL.md names 1 domain. As links in the text: corl.org. This is read from the text; nothing was executed.
Is Corl 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 Corl Artifact Evaluation use?
Corl 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 Corl Artifact Evaluation use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 Corl Artifact Evaluation?
Skills that share tags, products or a category with Corl Artifact Evaluation: QuantMind Training Config Generator (qusong0627/QuantMind, 1.7k stars), Arize Evaluator (github/awesome-copilot, 40k stars), Artifacts Builder (nexu-io/open-design, 100k stars) and Ito Training (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Corl Artifact Evaluation?
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.