Arize Evaluator
github/awesome-copilot
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and…
Agent skill
Micro Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when preparing a MICRO artifact for post-acceptance evaluation — packaging simulators, configs, traces, and scripts so evaluators can regenerate the paper's figures…
MITAuto-check passed
Install Micro Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill micro-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-artifact-evaluation .claude/skills/micro-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 "micro-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-artifact-evaluation into .claude/skills/micro-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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/MICRO-Skills/skills/micro-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 micro-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-artifact-evaluation .agents/skills/micro-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 "micro-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-artifact-evaluation into .agents/skills/micro-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 micro-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-artifact-evaluation .cursor/skills/micro-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 "micro-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-artifact-evaluation into .cursor/skills/micro-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 MICRO-Skills/skills/micro-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 micro-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills micro-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/MICRO-Skills/skills/micro-artifact-evaluation .gemini/skills/micro-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 "micro-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-artifact-evaluation into .gemini/skills/micro-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 micro-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 micro-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/MICRO-Skills/skills/micro-artifact-evaluation .github/skills/micro-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 "micro-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-artifact-evaluation into .github/skills/micro-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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 micro-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 micro-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/MICRO-Skills/skills/micro-artifact-evaluation .opencode/skills/micro-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 "micro-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/MICRO-Skills/skills/micro-artifact-evaluation into .opencode/skills/micro-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "micro-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
micro-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 697 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when preparing a MICRO artifact for post-acceptance evaluation — packaging simulators, configs, traces, and scripts so evaluators can regenerate the paper's figures…
- Preparing a MICRO artifact for post-acceptance evaluation — packaging simulators
- SKILL.md covers Badge targets and what each…, The time-budget problem is the…, Package inventory and Licensed and unshippable inputs, plus 4 more sections
- Calls make
- Scripts so evaluators can regenerate the papers figures
What it does
Micro Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing a MICRO artifact for post-acceptance evaluation — packaging simulators, configs, traces, and scripts so evaluators can regenerate the paper's figures, targeting the ACM Available/Functional/Reproducible badges, handling licensed workloads and long simulations, and earning the optional artifact appendix.
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
- Preparing a MICRO artifact for post-acceptance evaluation — packaging simulators
- Scripts so evaluators can regenerate the papers figures
- Targeting the ACM Available/Functional/Reproducible badges
- Handling licensed workloads and long simulations
Example prompts
- “/micro-artifact-evaluation”
Requirements
- Python 3
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
Micro Artifact Evaluation loads about 1.7k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 697 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). 697 words, ~1,710 tokens.
Download SKILL.mdSave it as
.claude/skills/micro-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- micro-artifact-evaluation
- description
- Use when preparing a MICRO artifact for post-acceptance evaluation — packaging simulators, configs, traces, and scripts so evaluators can regenerate the paper's figures, targeting the ACM Available/Functional/Reproducible badges, handling licensed workloads and long simulations, and earning the optional artifact appendix.
MICRO Artifact Evaluation
MICRO runs artifact evaluation after acceptance: authors of accepted papers are
invited to submit artifacts, an Artifact Evaluation Committee reviews them, and
successful papers carry ACM badges in the proceedings. Anchors here are the MICRO
2025 AE pages (micro58/submit/artifacts.php, verified 2026-07-08); the 2026
edition's AE page and calendar are 待核实 — expect the same architecture, verify
the dates.
Badge targets and what each demands
| Badge (ACM) | Bar | Microarchitecture-specific gotcha |
|---|---|---|
| Artifact Available | Deposited on a public archival repository — Zenodo, FigShare, Dryad named in 2025 | A GitHub repo alone fails; it is not archival. Deposit a tagged snapshot, keep GitHub as the living mirror |
| Artifact Functional | Documented, complete, exercisable | Simulator builds on a clean machine — your decade of dotfiles is not in the package |
| Results Reproducible | Evaluators regenerate the paper's main claims | Simulation time is the enemy: full SPEC runs take days-weeks; provide a subset path |
Papers passing AE also earned, in 2025, the right to a two-page artifact appendix in the camera-ready, free of charge — reproduction instructions published with the paper.
The time-budget problem is the design problem
A MICRO artifact's hardest constraint is that cycle-level simulation is slow and evaluators have finite weeks. Design three nested entry points:
make smoke # ~30 min: one workload region, one config — proves the plumbing
make key # ~1 day: the headline figure (Fig. 3) on a representative subset
make full # ~1 week: every figure, every sweep — for the determined evaluatorDocument expected runtime and disk for each. State which paper claims each tier
supports. An evaluator who finishes make key in a day and sees Fig. 3 appear is
an evaluator who awards the badge.
Package inventory
- Top-level README: paper title, badge targets, the three entry points, a
figure-to-command map ("Fig. 5 ⇐
make fig5, ~6 h"), hardware/software requirements (the 2025 process asked for these to route artifacts to suitable evaluators). - Environment capture: container image or pinned build script for the simulator toolchain; host assumptions stated (cores, RAM — parallel sims are memory-hungry).
- Everything
micro-reproducibilitypinned: simulator commit + patches, config files for baseline/mechanism/ablations, manifests, plotting scripts. - Raw stats for the paper's figures, so evaluators can verify plots even before re-simulating — this alone often satisfies partial reproduction.
Licensed and unshippable inputs
- SPEC CPU binaries/inputs cannot be redistributed. Ship the trace-generation recipe (tool version, SimPoint parameters, flags) plus, where licensing allows, the derived traces; otherwise document how an evaluator with a SPEC license regenerates them, and rest the Reproducible claim on shippable workloads.
- Proprietary RTL or PDK data (synthesis libraries are NDA'd): report the flow and constraints, mark the RTL numbers as not independently reproducible, and scope the badge request to the simulation results. Honest scoping beats a failed evaluation.
- FPGA/silicon dependencies: offer remote access if institutionally possible, or pre-recorded measurement logs with the analysis scripts.
Show full SKILL.md (275 more words)Show less
Common failure modes, ranked by frequency of pain
| Failure | Root cause | Prevention |
|---|---|---|
| Build breaks on evaluator machine | Undeclared host dependency (library, kernel version, python package) | Container or lockfile; test in a fresh VM |
| Numbers regenerate but differ slightly | Simulator nondeterminism or a config drifted after paper freeze | Determinism check + tagged commit at submission (micro-reproducibility) |
| Evaluator cannot find which command makes which figure | README written for the authors, not strangers | Figure-to-command map, tested by a labmate who did not build the artifact |
| Full run exceeds the AE window | No tiering | smoke/key/full entry points with honest runtime labels |
| Licensed inputs block everything | SPEC traces shipped nowhere, no fallback | Shippable-workload subset carrying the Reproducible claim |
A dry run fixes four of the five: hand the package to someone outside the project with only the README, and watch — silently — where they stall.
Working with evaluators
AE is collaborative, not adversarial: expect questions, fix breakage fast, and
keep a changelog of package updates during the window. Budget author-time in
August–September (the window overlaps the September 11 camera-ready in the 2026
calendar — plan both, see micro-camera-ready). The final deposit that carries
the Available badge must be the fixed package, re-uploaded to the archival
repository with a new version DOI.
Minimal artifact layout that evaluators navigate well
artifact/
├── README.md # badges sought, 3 entry points, figure-to-command map
├── Dockerfile # or install.sh with pinned versions
├── Makefile # smoke / key / full targets
├── configs/ # baseline.json, mechanism.json, ablations/
├── manifests/ # one YAML per experiment batch (micro-reproducibility)
├── stats-paper/ # the raw stats behind every published figure
├── plots/ # scripts: stats -> exact paper figures
├── traces/RECIPE.md # how to regenerate licensed traces (nothing licensed inside)
└── LIMITS.md # known fidelity gaps; claims stay inside themTwo conventions matter more than the tree itself: the README's first screen must
answer "what do I run first and how long will it take," and nothing an evaluator
needs may require reading source code to discover. If the paper linked an
anonymized repo at submission (micro-supplementary), this layout should already
exist — AE prep is then a de-anonymization pass plus the archival deposit.
Output format
[Badge plan] Available: deposit target · Functional: yes/no · Reproducible: scoped to <claims>
[Entry points] smoke/key/full runtimes measured on clean machine: <times>
[Figure map] figures with regenerate-commands: N/M
[Unshippables] licensed inputs + documented workaround each
[Environment] container/build-script tested from scratch: yes / no
[Calendar] AE dates vs camera-ready overlap plan (2026 dates 待核实)© 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 MICRO-Skills/skills/micro-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Micro 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 |
|---|---|---|---|---|---|---|
| Micro Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Ndss Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Sosp Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Hpca Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~940 | Automated safety check: Pass | MIT |
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Questions about Micro Artifact Evaluation
What does Micro Artifact Evaluation do?
A skill your agent uses when preparing a MICRO artifact for post-acceptance evaluation — packaging simulators, configs, traces, and scripts so evaluators can regenerate the paper's figures…. Micro Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when preparing a MICRO artifact for post-acceptance evaluation — packaging simulators, configs, traces, and scripts so evaluators can regenerate the paper's figures, targeting the ACM Available/Functional/Reproducible badges, handling licensed workloads and long simulations, and earning the optional artifact appendix.
When should I use Micro Artifact Evaluation?
Micro Artifact Evaluation fits situations like: preparing a MICRO artifact for post-acceptance evaluation — packaging simulators; scripts so evaluators can regenerate the papers figures; targeting the ACM Available/Functional/Reproducible badges; handling licensed workloads and long simulations.
How do I install Micro Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill micro-artifact-evaluation -a claude-code`. Or copy the skill folder (MICRO-Skills/skills/micro-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/micro-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Micro Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill micro-artifact-evaluation -a codex`. Or copy the skill folder (MICRO-Skills/skills/micro-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/micro-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Micro 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 micro-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/micro-artifact-evaluation, .gemini/skills/micro-artifact-evaluation, .github/skills/micro-artifact-evaluation and .opencode/skills/micro-artifact-evaluation in your project.
What does Micro Artifact Evaluation need to run?
Going by SKILL.md and its folder, Micro Artifact Evaluation needs the command-line tools its instructions call (make). Our summary lists: Python 3.
Does Micro 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 Micro 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 Micro Artifact Evaluation use?
Micro 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 Micro Artifact Evaluation use?
About 1.7k tokens (SKILL.md is roughly 6.8k 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 Micro Artifact Evaluation?
Skills that share tags, products or a category with Micro Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars), Ndss Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Sosp 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 Micro 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.