Agent SkillsNeverSight/learn-skills.dev › artifact-evaluation-prep

artifact-evaluation-prep

GitHub

为会议提供可复现的研究制品包,包含安装说明、复现命令、环境检查及数据打包,确保审稿人能验证论文主张。

data/skills-md/a-green-hand-jack/ml-research-skills/artifact-evaluation-prep/SKILL.md NeverSight/learn-skills.dev

Trigger Scenarios

准备会议评审用的可复现代码和数据包 生成面向审稿人的安装与复现指令 处理研究论文的制品评估材料

Install

npx skills add NeverSight/learn-skills.dev --skill artifact-evaluation-prep -g -y
More Options

Non-standard path

npx skills add https://github.com/NeverSight/learn-skills.dev/tree/main/data/skills-md/a-green-hand-jack/ml-research-skills/artifact-evaluation-prep -g -y

Use without installing

npx skills use NeverSight/learn-skills.dev@artifact-evaluation-prep

指定 Agent (Claude Code)

npx skills add NeverSight/learn-skills.dev --skill artifact-evaluation-prep -a claude-code -g -y

安装 repo 全部 skill

npx skills add NeverSight/learn-skills.dev --all -g -y

预览 repo 内 skill

npx skills add NeverSight/learn-skills.dev --list

SKILL.md

Frontmatter
{
    "name": "artifact-evaluation-prep",
    "description": "Prepare a research artifact package for conference artifact evaluation, reproducibility review, badges, supplementary material, or post-acceptance artifact release. Use this skill whenever the user needs install instructions, reviewer-facing reproduction commands, Docker or environment checks, data\/checkpoint packaging, hardware\/runtime estimates, anonymized or public artifact metadata, artifact evaluation forms, or a claim-to-artifact reproducibility audit for ML\/AI venues.",
    "allowed-tools": "Read, Write, Edit, Bash, Glob, WebSearch, WebFetch",
    "argument-hint": "[project-dir] [--venue <venue>] [--mode audit|package|instructions|smoke-test]"
}

Artifact Evaluation Prep

Prepare a paper's code, data, checkpoints, scripts, and instructions so an external artifact reviewer can reproduce the paper-facing claims with minimal ambiguity.

Use this skill when:

  • a venue requires or offers artifact evaluation, reproducibility badges, or artifact appendices
  • the user needs reviewer-facing install, quickstart, demo, or reproduction instructions
  • a camera-ready or accepted paper needs an artifact package handoff
  • code, data, checkpoints, models, Docker images, or external services must be packaged
  • runtime, hardware, random seeds, expected outputs, or troubleshooting notes need to be made explicit
  • claims in the paper need to be mapped to runnable scripts or released artifacts

Do not use this skill as a general code-release skill. Use release-code for public repository hygiene, licensing, CITATION files, tags, and GitHub releases. Use this skill for reviewer-facing artifact execution and claim reproduction.

Pair this skill with:

  • camera-ready-finalizer to recover accepted-paper obligations and final claim/evidence state
  • release-code to prepare public repository hygiene after artifact obligations are clear
  • reproducibility-audit when environment, data, or execution drift needs a broader audit
  • run-experiment for generating or testing reproduction commands
  • figure-results-review when artifact outputs must match paper figures or tables
  • citation-audit when artifact metadata cites datasets, code, or prior artifacts
  • research-project-memory when artifact status, blockers, and reviewer-facing instructions should persist

Skill Directory Layout

<installed-skill-dir>/
├── SKILL.md
└── references/
    ├── artifact-audit.md
    ├── memory-writeback.md
    ├── package-manifest.md
    ├── report-template.md
    └── reviewer-instructions.md

Progressive Loading

  • Always read references/artifact-audit.md, references/package-manifest.md, and references/reviewer-instructions.md.
  • Read references/report-template.md before writing a saved artifact evaluation report.
  • Read references/memory-writeback.md when the project has memory/, component .agent/ folders, or the user asks for persistent state.
  • If venue rules matter, verify current official artifact evaluation instructions before asserting deadlines, badge names, anonymity rules, upload fields, page limits, or required formats.

Core Principles

  • Artifact evaluation is a reviewer workflow, not just a code dump.
  • The artifact must reproduce the paper's important claims at an acceptable cost, or clearly document what it cannot reproduce.
  • Prefer one reliable quickstart and one complete reproduction path over many fragile commands.
  • Every command should state expected runtime, hardware, input, output, and success criteria.
  • Package only redistributable data, checkpoints, and dependencies; document restricted assets precisely.
  • Keep anonymity, licensing, and external-service assumptions explicit.
  • Treat smoke tests as required. An untested instruction file is not an artifact package.

Step 1 - Recover Evaluation Context

Collect:

  • venue and artifact evaluation track, if known
  • official artifact instructions, badge criteria, anonymity policy, and upload mechanism
  • accepted or submitted paper, appendix, supplementary material, and checklist
  • code repository, commit hash, branches, and worktrees
  • datasets, checkpoints, pretrained models, generated outputs, and external dependencies
  • hardware expectations: CPU/GPU type, memory, disk, runtime, network access
  • paper claims, figures, tables, and experiments that the artifact should support
  • constraints: private data, license limits, large files, cloud dependencies, nondeterminism, or reviewer time budget

If no venue is specified, produce a venue-agnostic artifact package but mark venue-specific fields as unresolved.

Step 2 - Map Claims to Artifact Paths

For each paper-facing claim or result, record:

  • claim or result ID
  • paper location
  • script, notebook, config, or command that supports it
  • input data or checkpoint
  • expected output file, metric, table, or figure
  • approximate runtime and hardware
  • deterministic tolerance or expected variance
  • reviewer priority: quickstart, core, optional, or not reproducible in package

Do not imply full reproducibility if only a smoke test or cached output is provided.

Step 3 - Build the Artifact Manifest

Read references/package-manifest.md.

Create or update a manifest that lists:

  • repository URL or archive path
  • exact commit, tag, or checksum
  • directory layout
  • environment files and Docker images
  • data and checkpoint locations
  • reproduction scripts and configs
  • expected generated outputs
  • license and citation metadata
  • known limitations and unsupported claims

Prefer small, stable names such as ARTIFACT.md, REPRODUCE.md, or docs/artifact_evaluation.md unless the venue requires a specific filename.

Step 4 - Write Reviewer Instructions

Read references/reviewer-instructions.md.

Provide:

  • setup commands
  • quick smoke test under a short runtime budget
  • core reproduction commands for main paper claims
  • expected outputs and how to compare them with the paper
  • troubleshooting for common failures
  • hardware, storage, network, and time requirements
  • contact policy or anonymous support channel if allowed
  • limitations and optional extended runs

Instructions should be copy-pasteable and should not require the reviewer to infer hidden paths or environment variables.

Step 5 - Smoke Test the Artifact

When allowed by the user and environment, run at least:

  • environment creation or dependency resolution
  • import or CLI sanity check
  • quickstart command
  • one representative data/checkpoint load
  • one expected-output comparison

If commands are too expensive, record the exact reason and create a minimal substitute test.

Step 6 - Handle Packaging Risks

Audit:

  • anonymization vs public release state
  • licenses for code, data, pretrained weights, and third-party assets
  • large-file strategy and checksums
  • private paths, credentials, API keys, and machine-specific assumptions
  • random seeds and nondeterminism
  • version pinning and dependency conflicts
  • reviewer time budget and failure recovery

Route public release issues to release-code; route environment drift to reproducibility-audit if available.

Step 7 - Write the Artifact Evaluation Report

Read references/report-template.md.

If saving to a project and no path is given, use:

docs/submission/artifact_evaluation_prep_YYYY-MM-DD.md

The report must include:

  • readiness decision
  • blocking issues
  • claim-to-artifact map
  • package manifest summary
  • smoke-test status
  • reviewer instruction status
  • risks, limitations, and reviewer-facing caveats
  • handoff to release, camera-ready, or memory

Step 8 - Write Back to Project Memory

Read references/memory-writeback.md when memory exists.

Update artifact status, reproduction commands, blockers, claim support, release actions, and final handoff notes without copying full command logs into memory.

Final Sanity Check

Before finalizing:

  • every important paper claim is either reproducible, smoke-tested, cached with explanation, or explicitly out of scope
  • quickstart instructions have expected outputs and runtime
  • hardware, data, checkpoints, licenses, and anonymity state are clear
  • package paths and links are stable
  • reviewer-facing failure modes are documented
  • public-release and camera-ready obligations are routed
  • project memory records artifact readiness and open blockers

Version History

  • e0220ca Current 2026-07-05 21:34

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