Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
Agent skill
Percom Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced, IEEE DataPort or Zenodo…
Install Percom Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills percom-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/PerCom-Skills/skills/percom-artifact-evaluation .claude/skills/percom-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 "percom-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation into .claude/skills/percom-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "percom-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/PerCom-Skills/skills/percom-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 percom-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills percom-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/PerCom-Skills/skills/percom-artifact-evaluation .agents/skills/percom-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 "percom-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation into .agents/skills/percom-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "percom-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 percom-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills percom-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/PerCom-Skills/skills/percom-artifact-evaluation .cursor/skills/percom-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 "percom-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation into .cursor/skills/percom-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "percom-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 PerCom-Skills/skills/percom-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 percom-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills percom-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/PerCom-Skills/skills/percom-artifact-evaluation .gemini/skills/percom-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 "percom-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation into .gemini/skills/percom-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "percom-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 percom-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 percom-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/PerCom-Skills/skills/percom-artifact-evaluation .github/skills/percom-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 "percom-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation into .github/skills/percom-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "percom-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 percom-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 percom-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/PerCom-Skills/skills/percom-artifact-evaluation .opencode/skills/percom-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 "percom-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/PerCom-Skills/skills/percom-artifact-evaluation into .opencode/skills/percom-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "percom-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
percom-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 500 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced, IEEE DataPort or Zenodo…
- Packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced
- SKILL.md covers What "reproducible" means for…, What a ubicomp evaluator opens…, Packaging plan and De-identification is the…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Zenodo deposit)
What it does
Percom Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced, IEEE DataPort or Zenodo deposit), covering what a ubicomp evaluator checks first for human-subjects sensing data, cross-subject reproduction, de-identification, and honest degrees of reproducibility.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. 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 an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced
- Zenodo deposit)
- Covering what a ubicomp evaluator checks first for human-subjects sensing data
- Cross-subject reproduction
Example prompts
- “/percom-artifact-evaluation”
Requirements
- Docker
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
Percom Artifact Evaluation loads about 1.3k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 500 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). 500 words, ~1,338 tokens.
Download SKILL.mdSave it as
.claude/skills/percom-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- percom-artifact-evaluation
- description
- Use when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced, IEEE DataPort or Zenodo deposit), covering what a ubicomp evaluator checks first for human-subjects sensing data, cross-subject reproduction, de-identification, and honest degrees of reproducibility.
PerCom Artifact Evaluation
Use this for reproducibility packaging. First, a cycle caveat: unlike SIGSOFT venues, PerCom has not historically run a mandatory formal artifact-evaluation track with a fixed badge set, and whether a given edition offers a reproducibility/badging track (e.g., IEEE Open Research Objects / Results Reproduced) is 待核实 — confirm on the current call. Regardless of whether a badge is offered, a well-packaged, de-identified sensing dataset and reproducible pipeline is a scored strength in the double-blind review and a lasting community contribution.
What "reproducible" means for a ubicomp sensing paper
Two deliverables, kept distinct:
- The anonymized review package (at submission): dataset link and code scrubbed of owner, testbed, and lab identity, for the double-blind reviewers.
- The public deposit (after acceptance): a de-identified dataset and code in a DOI-issuing archive under an open license — the version others cite and reuse, and the version any badge program evaluates.
What a ubicomp evaluator opens first
| Claim type | First thing inspected | Common failure caught |
|---|---|---|
| An activity/context recognizer | The script that regenerates cross-subject (LOSO) results | Only a pooled-accuracy script; no leave-one-subject-out path |
| A sensing dataset | The data itself + a datasheet (subjects, sensors, labels) | Link present, data missing; no de-identification described |
| A deployed system | A demo on bundled sample data | Only-runs-on-authors'-testbed; hardware not documented |
| A model result | Trained weights + inference on sample input | Requires the full raw dataset or private compute to run |
Assume an evaluator gives your package a bounded time budget on a clean machine with no access to your sensors or subjects. Design for the first ten minutes — a demo on bundled, de-identified sample data — to succeed.
Packaging plan
[Container] ship a Dockerfile or a pinned environment (requirements/lockfile); avoid
"install these 40 things by hand"
[README] one-screen orientation: what it is, install, run the demo, reproduce each claim,
expected runtime and outputs
[Datasheet] a dataset datasheet: subjects (count, relevant demographics), sensors (device,
firmware, sampling rate, placement), labels + protocol, and known biases
[Mapping] an explicit table: paper claim -> script -> expected result (with the LOSO split)
[Data] the de-identified dataset itself (or documented restricted-access), not just a query
[Ethics] IRB/consent status and the de-identification performed before release
[License] an open, DOI-issuing deposit (IEEE DataPort, Zenodo) so others can reuse and citeShow full SKILL.md (235 more words)Show less
De-identification is the ubicomp-specific bar
Human-subjects sensing data leaks identity in ways code does not: raw audio/video, GPS traces, timestamps that pinpoint a home, and even accelerometer gait can re-identify. Before any release:
- Remove or transform direct identifiers and re-identifying traces; document exactly what you did.
- Confirm your consent and IRB approval permit public release of the de-identified data — some approvals do not, and then the honest move is restricted access with a documented request path.
- Never ship a dataset that a subject did not consent to have published.
Worked vignette: a wearable-HAR dataset + recognizer
To make a HAR paper reproducible: ship a Docker image with the recognizer pre-built; a
run_demo.sh that classifies on a small bundled, de-identified sample in under a minute; a
reproduce/ directory whose scripts regenerate the leave-one-subject-out F1 table (not just a
pooled number) from logged features; a datasheet listing subjects, sensor placement, and sampling
rate; the de-identified dataset with a documented consent/IRB basis; and an open license with a
DOI. State honestly which results are turnkey and which need the full (slow) training run.
Calibration
- Whether a badge/reproducibility track runs, and which badges, is 待核实 per cycle — confirm on the current call rather than assuming a SIGSOFT-style scheme.
- The DOI-issuing deposit and datasheet are portable value even when no badge is offered.
- Anonymize the review package; de-identify (and confirm consent for) the public dataset — these are different obligations.
Output format
[Track status] formal reproducibility/badge track this cycle? yes/no/待核实
[Artifact role] anonymized review package / public de-identified deposit
[Contents] <recognizer/dataset/datasheet/scripts/ethics/license>
[Ten-minute test] does install + demo on bundled sample data succeed on a clean machine? yes/no
[Cross-subject reproduction] does a script regenerate the LOSO result? yes/no
[De-identification] documented + consent/IRB permits release? yes/no
[Fixes before deposit] <ordered list>© 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 PerCom-Skills/skills/percom-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Percom 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 |
|---|---|---|---|---|---|---|
| Percom Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
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Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
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Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Percom Artifact Evaluation
What does Percom Artifact Evaluation do?
A skill your agent uses when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced, IEEE DataPort or Zenodo…. Percom Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced, IEEE DataPort or Zenodo deposit), covering what a ubicomp evaluator checks first for human-subjects sensing data, cross-subject reproduction, de-identification, and honest degrees of reproducibility.
When should I use Percom Artifact Evaluation?
Percom Artifact Evaluation fits situations like: packaging an IEEE PerCom sensing artifact and dataset for reproducibility and any badging (IEEE Open Research Objects / Results Reproduced; zenodo deposit); covering what a ubicomp evaluator checks first for human-subjects sensing data; cross-subject reproduction.
How do I install Percom Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-artifact-evaluation -a claude-code`. Or copy the skill folder (PerCom-Skills/skills/percom-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/percom-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Percom Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill percom-artifact-evaluation -a codex`. Or copy the skill folder (PerCom-Skills/skills/percom-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/percom-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Percom 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 percom-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/percom-artifact-evaluation, .gemini/skills/percom-artifact-evaluation, .github/skills/percom-artifact-evaluation and .opencode/skills/percom-artifact-evaluation in your project.
What does Percom Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Percom Artifact Evaluation is instructions for the agent only. Our summary lists: Docker.
Does Percom 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 Percom 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 Percom Artifact Evaluation use?
Percom 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 Percom Artifact Evaluation use?
About 1.3k tokens (SKILL.md is roughly 5.4k 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 Percom Artifact Evaluation?
Skills that share tags, products or a category with Percom Artifact Evaluation: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Percom 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.