Agent skill

Fse Artifact Evaluation

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

MITAuto-check passed

Install Fse Artifact Evaluation

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-artifact-evaluation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/FSE-Skills/skills/fse-artifact-evaluation .claude/skills/fse-artifact-evaluation && rm -rf skills-src

Use ~/.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/

Paste into Claude Code

Install the "fse-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation into .claude/skills/fse-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation

Type 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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-artifact-evaluation --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/FSE-Skills/skills/fse-artifact-evaluation .agents/skills/fse-artifact-evaluation && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "fse-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation into .agents/skills/fse-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-artifact-evaluation --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/FSE-Skills/skills/fse-artifact-evaluation .cursor/skills/fse-artifact-evaluation && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "fse-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation into .cursor/skills/fse-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path FSE-Skills/skills/fse-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-artifact-evaluation --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/FSE-Skills/skills/fse-artifact-evaluation .gemini/skills/fse-artifact-evaluation && rm -rf skills-src

Use ~/.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/

Paste into Gemini CLI

Install the "fse-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation into .gemini/skills/fse-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-artifact-evaluation

Installs 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).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/FSE-Skills/skills/fse-artifact-evaluation .github/skills/fse-artifact-evaluation && rm -rf skills-src

Use ~/.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/

Paste into GitHub Copilot

Install the "fse-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation into .github/skills/fse-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-artifact-evaluation --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/FSE-Skills/skills/fse-artifact-evaluation .opencode/skills/fse-artifact-evaluation && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "fse-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-artifact-evaluation into .opencode/skills/fse-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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: fse-artifact-evaluation
GitHub stars: 1.2k
Token cost: ~1.2k tokens
SKILL.md length: 467 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available
SKILL.md covers The ACM badges (verify the…, What SIGSOFT evaluators open…, Packaging plan and Anonymized review artifact vs.…, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Evaluated Functional and Reusable

What it does

Fse Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what SIGSOFT evaluators check first, DOI-issuing archives, evaluator-proof documentation, and the separate post-acceptance artifact deadline.

Its SKILL.md is about 1.2k 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 an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available
Evaluated Functional and Reusable
Results Reproduced)
Covering what SIGSOFT evaluators check first

Example prompts

“/fse-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

Fse Artifact Evaluation loads about 1.2k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 467 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~85
When it runs · the whole SKILL.md, loaded when a task matches: ~1.2k

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). 467 words, ~1,205 tokens.

Download SKILL.mdSave it as .claude/skills/fse-artifact-evaluation/SKILL.md (or your agent's skills folder).

name: fse-artifact-evaluation
description: Use when packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what SIGSOFT evaluators check first, DOI-issuing archives, evaluator-proof documentation, and the separate post-acceptance artifact deadline.

FSE Artifact Evaluation

Use this for the artifact track. FSE follows the ACM Artifact Review and Badging scheme, and the artifact evaluation is a separate, post-acceptance process with its own deadline. Two things to internalize: badges are earned by evaluators actually using your package, and the review artifact (anonymized, for the paper's reviewers) is not the same deliverable as the badge artifact (de-anonymized, permanently archived).

The ACM badges (verify the current set and names)

Badge	What it certifies	What earns it
Artifacts Available	The artifact is permanently, publicly retrievable	Deposit in a DOI-issuing archive (Zenodo, figshare, Software Heritage)
Artifacts Evaluated - Functional	The artifact runs and does what the paper says	A clean-machine install, a demo, and documented expected outputs
Artifacts Evaluated - Reusable	Others can build on it	The Functional bar plus careful docs, structure, and licensing
Results Reproduced	An evaluator reproduced the paper's key results	A turnkey path from the artifact to the headline numbers

Available is a low-cost, high-value badge (archive the package); Functional/Reusable/Reproduced require the evaluator's own run to succeed, so the failure mode is always "did not run on their machine," never "the idea was weak."

What SIGSOFT evaluators open first

Claim type	First thing inspected	Common failure caught
A tool/technique	The README and one install/run command	Undocumented dependencies; only-works-on-authors'-laptop
An empirical study	The scripts that turn data into the paper's tables	Numbers in the PDF that no script reproduces
A mined dataset	The extraction scripts + the extracted data	Query shipped, data missing; provenance unpinned
An LLM-based result	Cached prompts/outputs + model IDs	Requires live API keys; not reproducible

Assume an evaluator gives your package a bounded time budget on a clean machine. Design for the first ten minutes to succeed.

Show full SKILL.md (184 more words)Show less

Packaging plan

text

[Container]   ship a Dockerfile or a pinned environment (requirements/lockfile); avoid
              "install these 40 things by hand"
[README]      one-screen orientation: what it is, how to install, how to run the demo, how to
              reproduce each claim, expected runtime and outputs
[Mapping]     an explicit table: paper claim -> script -> expected result
[Data]        the extracted dataset itself (or documented access), not just the query
[Provenance]  repo SHAs, extraction dates, model IDs/dates, seeds
[License]     an OSI-approved license so the artifact can be badged Reusable
[Archive]     deposit in a DOI-issuing repository for the Available badge

Anonymized review artifact vs. badge artifact

At submission: the artifact is anonymized for the paper's reviewers — no owner strings, cluster paths, lab names, or identity-revealing links, and no live repository that discloses authors.
After acceptance: replace anonymized placeholders with the public, licensed, DOI-issuing archive; this is the version the artifact evaluators badge and the camera-ready cites.

Worked vignette: packaging a detection tool + study

A paper contributes a defect-detection tool and an empirical evaluation. To target Reusable and Reproduced: ship a Docker image with the tool pre-built; a run_demo.sh that detects on a small bundled project in under a minute; a reproduce/ directory whose scripts regenerate each table from logged results; a claim-to-script mapping table in the README; the extracted evaluation dataset with pinned SHAs; and an MIT/Apache license. State honestly which results are turnkey and which need the full (slow) dataset run.

Calibration

The artifact deadline is after acceptance and independent of the camera-ready; do not conflate them.
Badge names, the exact set offered, and whether evaluation is single- or double-anonymous vary by cycle — confirm on the current artifact-track call.

Output format

text

[Target badges] Available / Functional / Reusable / Reproduced
[Artifact role] anonymized review artifact / public badge artifact
[Contents] <tool/data/scripts/provenance/license>
[Ten-minute test] does install + demo succeed on a clean machine? yes/no
[Claim mapping] <claim -> script -> expected result present? yes/no>
[Fixes before upload] <ordered list>

Files

Just SKILL.md in FSE-Skills/skills/fse-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Fse 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.

Fse Artifact Evaluation compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Fse Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.2k	Automated safety check: Pass	MIT	13 days ago
Arize Evaluatorgithub/awesome-copilot	40k	1 repos	~8.1k	Automated safety check: Notes	MIT	yesterday
Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~969	Automated safety check: Pass	MIT	13 days ago
Sigcomm Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.1k	Automated safety check: Pass	MIT	13 days ago
Sigmetrics Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.4k	Automated safety check: Pass	MIT	13 days ago
Socc Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.5k	Automated safety check: Pass	MIT	13 days ago

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Questions about Fse Artifact Evaluation

What does Fse Artifact Evaluation do?

A skill your agent uses when packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what…. Fse Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available, Evaluated Functional and Reusable, Results Reproduced), covering what SIGSOFT evaluators check first, DOI-issuing archives, evaluator-proof documentation, and the separate post-acceptance artifact deadline.

When should I use Fse Artifact Evaluation?

Fse Artifact Evaluation fits situations like: packaging an ESEC/FSE artifact for the ACM Artifact Review and Badging scheme (Artifacts Available; evaluated Functional and Reusable; results Reproduced); covering what SIGSOFT evaluators check first.

How do I install Fse Artifact Evaluation in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a claude-code`. Or copy the skill folder (FSE-Skills/skills/fse-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fse-artifact-evaluation in your project. Claude Code loads it when a task matches its description.

How do I install Fse Artifact Evaluation in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-artifact-evaluation -a codex`. Or copy the skill folder (FSE-Skills/skills/fse-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fse-artifact-evaluation in your project. Codex loads it when a task matches its description.

Can I use Fse 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 fse-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/fse-artifact-evaluation, .gemini/skills/fse-artifact-evaluation, .github/skills/fse-artifact-evaluation and .opencode/skills/fse-artifact-evaluation in your project.

What does Fse Artifact Evaluation need to run?

SKILL.md names no scripts, command-line tools or credentials: Fse Artifact Evaluation is instructions for the agent only. Our summary lists: Docker.

Does Fse 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 Fse 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 Fse Artifact Evaluation use?

Fse 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 Fse Artifact Evaluation use?

About 1.2k tokens (SKILL.md is roughly 4.8k 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 Fse Artifact Evaluation?

Skills that share tags, products or a category with Fse Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), Sigcomm Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Sigmetrics Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Fse 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.

Install Fse Artifact Evaluation

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

FSE Artifact Evaluation

The ACM badges (verify the current set and names)

What SIGSOFT evaluators open first

Packaging plan

Anonymized review artifact vs. badge artifact

Worked vignette: packaging a detection tool + study

Calibration

Output format

Files

Compare with similar skills

Similar skills

More from brycewang-stanford/Awesome-Journal-Skills

Questions about Fse Artifact Evaluation