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
Sigir Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources…
MITAuto-check passed
Install Sigir Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigir-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-artifact-evaluation .claude/skills/sigir-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 "sigir-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-artifact-evaluation into .claude/skills/sigir-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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/SIGIR-Skills/skills/sigir-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 sigir-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-artifact-evaluation .agents/skills/sigir-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 "sigir-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-artifact-evaluation into .agents/skills/sigir-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 sigir-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-artifact-evaluation .cursor/skills/sigir-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 "sigir-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-artifact-evaluation into .cursor/skills/sigir-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 SIGIR-Skills/skills/sigir-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 sigir-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills sigir-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/SIGIR-Skills/skills/sigir-artifact-evaluation .gemini/skills/sigir-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 "sigir-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-artifact-evaluation into .gemini/skills/sigir-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 sigir-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 sigir-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/SIGIR-Skills/skills/sigir-artifact-evaluation .github/skills/sigir-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 "sigir-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-artifact-evaluation into .github/skills/sigir-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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 sigir-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 sigir-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/SIGIR-Skills/skills/sigir-artifact-evaluation .opencode/skills/sigir-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 "sigir-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/SIGIR-Skills/skills/sigir-artifact-evaluation into .opencode/skills/sigir-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sigir-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
sigir-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 634 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources…
- Test collections
- SKILL.md covers Routing: artifact-in-paper vs…, The reviewer-runnable IR…, Judgments and collections as… and Anonymized review packaging…, plus 3 more sections
- Calls conda, bash and python
- Judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper
What it does
Sigir Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper, building reviewer-runnable IR repositories, run-file and qrels hygiene, licensing and datasheets, and single- vs double-anonymous handling.
Its SKILL.md is about 1.6k 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
- Test collections
- Judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper
- Building reviewer-runnable IR repositories
- Run-file and qrels hygiene
Example prompts
- “/sigir-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:
condabashpython
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
Sigir Artifact Evaluation loads about 1.6k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 634 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). 634 words, ~1,640 tokens.
Download SKILL.mdSave it as
.claude/skills/sigir-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- sigir-artifact-evaluation
- description
- Use when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper, building reviewer-runnable IR repositories, run-file and qrels hygiene, licensing and datasheets, and single- vs double-anonymous handling.
SIGIR Artifact Evaluation
At SIGIR, "artifact" means something more specific than in most ML venues: the community's unit of exchange is the run file + qrels + index recipe, inherited from the TREC evaluation tradition. A SIGIR artifact is convincing when a stranger can rebuild your ranking, score it with standard tooling, and get your table. This skill covers packaging that artifact — and the routing decision that comes first.
Routing: artifact-in-paper vs Resources paper
SIGIR 2026 explicitly forbids double-dipping: the same dataset cannot be both a Resources track submission and the contribution of another paper. Decide ownership:
| Situation | Route |
|---|---|
| Code/runs that back a method claim | Repository cited from the full/short paper |
| New corpus/judgments, and the resource itself is the contribution | Resources track (6 pages + refs, single-anonymous in 2026) |
| New resource used incidentally by a method paper | Method paper cites it; release separately; do not also submit it as a Resource paper in the same cycle |
| Reproduction study of published results | Reproducibility track (own track in 2026; budget 待核实) |
The anonymity asymmetry matters operationally: Resources reviewers may inspect the real, non-anonymized resource, while full-paper reviewers must see an anonymized mirror. Same artifact, two different packaging jobs.
The reviewer-runnable IR repository
Structure the repository around the evaluation chain, because that is how an IR reviewer will try to audit it:
repo/
README.md # 10-minute path: install -> retrieve -> evaluate -> Table 2
environment.yml # or Dockerfile; pin the retrieval toolkit version
data/DOWNLOAD.md # scripted fetch for public collections; never redistribute
indexing/build.sh # exact analyzer/tokenizer settings — silent nDCG movers
runs/ # TREC-format run files behind every table row
qrels/ # only if you created judgments; else pointers + checksums
eval/score.sh # ir_measures / trec_eval invocation with exact flags
eval/significance.py # the paired test that produced the paper's p-values
MANIFEST.md # table-of-paper -> script -> run file mappingNon-negotiables:
- Run files are the artifact. Ship the exact TREC-format runs behind every reported number; they let reviewers verify metrics without re-running GPUs.
- The index recipe is part of the method. Stemming, stopwords, max sequence length, and doc-splitting settings change scores; record them as code, not prose.
- Score with community tooling (
trec_eval,ir_measures,ranx, or the toolkit's own eval) so numbers are checkable in one command. - Checksums for derived data: qrels subsets, filtered corpora, sampled queries.
# The audit a reviewer (or you, pre-submission) should be able to run
conda env create -f environment.yml && conda activate repro
bash indexing/build.sh && bash eval/score.sh runs/ours.trec
python eval/significance.py runs/ours.trec runs/bm25.trec # matches §5?Judgments and collections as artifacts
If you built topics, judgments, or a corpus:
- Document the annotation protocol: assessor pool, guidelines, pay, agreement statistics (e.g., Cohen's or Krippendorff's), and adjudication.
- State pooling: which systems contributed to the judged pool and to what depth — unpooled dense-retrieval evaluation is a known validity trap reviewers probe.
- License explicitly (CC variants for data; note source-document terms separately) and include a datasheet: provenance, intended use, known biases, PII handling.
- For web/log-derived data, describe the privacy pipeline; "anonymized internally" without method is treated as unusable by careful reviewers.
Show full SKILL.md (251 more words)Show less
Anonymized review packaging (full/short papers)
- Mirror the repo to an anonymous host; scrub commit history (fresh export, not a redacted clone), usernames in paths, and institution-specific cluster scripts.
- Keep the mirror small and runnable: reviewers grant minutes, not hours. The 10-minute README path decides whether the artifact helps or is ignored.
- Model checkpoints too large to host anonymously: ship the training script plus the exact seed/config, and say so plainly in the README.
Packaging failures reviewers actually hit
Observed failure modes, in descending frequency:
- The README's first command fails (missing
environment.ymlpin, absolute paths, CUDA assumptions) — the reviewer stops there and the artifact scores as absent. - Run files present but not mapped to tables — without a MANIFEST, a reviewer
cannot tell
runs/final3.trecfromruns/final3_fixed.trec. - Redistributed data the license forbids (qrels, corpus slices) — a policy problem that outlasts the review.
- Evaluation script computes a nonstandard metric variant silently (e.g., a different gain function for nDCG) — the "numbers don't match" review comment.
- Anonymization done by deletion: the repo compiles but the interesting config was "removed for anonymity," which reads as hiding.
Post-acceptance hardening
- Replace the mirror with the public repository before camera-ready; mint an archival DOI (Zenodo or institutional) for the frozen state the paper describes.
- Register resources where the community looks:
ir_datasetsintegration, a Hugging Face dataset card, or TREC-adjacent registries as fits the artifact. - Badging: ACM defines artifact badges, but whether SIGIR applies them this cycle was not verifiable (待核实) — treat badges as optional polish, run-file hygiene as core.
Output format
[Artifact route] in-paper repo / Resources paper / Reproducibility track / release-only
[Runnable path] install->index->retrieve->score minutes: <n> (goal <=10 read + run start)
[Run-file coverage] tables backed by shipped runs: <k>/<n>
[Index recipe] scripted y/n; analyzer settings recorded y/n
[Judgment docs] protocol/agreement/pooling/license: complete / gaps <list>
[Anonymity mode] double-anonymous mirror / single-anonymous real repo
[Post-acceptance] DOI plan, ir_datasets/HF registration plan© 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 SIGIR-Skills/skills/sigir-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Sigir 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 |
|---|---|---|---|---|---|---|
| Sigir Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| 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 | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT | |
| Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | Automated safety check: Pass | MIT | |
| Fast Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT |
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Questions about Sigir Artifact Evaluation
What does Sigir Artifact Evaluation do?
A skill your agent uses when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources…. Sigir Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, run files, test collections, or judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper, building reviewer-runnable IR repositories, run-file and qrels hygiene, licensing and datasheets, and single- vs double-anonymous handling.
When should I use Sigir Artifact Evaluation?
Sigir Artifact Evaluation fits situations like: test collections; judgments for a SIGIR submission — deciding between an artifact inside a full/short paper and a standalone Resources track paper; building reviewer-runnable IR repositories; run-file and qrels hygiene.
How do I install Sigir Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigir-artifact-evaluation -a claude-code`. Or copy the skill folder (SIGIR-Skills/skills/sigir-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/sigir-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Sigir Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill sigir-artifact-evaluation -a codex`. Or copy the skill folder (SIGIR-Skills/skills/sigir-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/sigir-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Sigir 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 sigir-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/sigir-artifact-evaluation, .gemini/skills/sigir-artifact-evaluation, .github/skills/sigir-artifact-evaluation and .opencode/skills/sigir-artifact-evaluation in your project.
What does Sigir Artifact Evaluation need to run?
Going by SKILL.md and its folder, Sigir Artifact Evaluation needs the command-line tools its instructions call (conda, bash and python). Our summary lists: Python 3.
Does Sigir 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 Sigir 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 Sigir Artifact Evaluation use?
Sigir 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 Sigir Artifact Evaluation use?
About 1.6k 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 Sigir Artifact Evaluation?
Skills that share tags, products or a category with Sigir Artifact Evaluation: Arize Evaluator (github/awesome-copilot, 40k stars), Artifacts Builder (nexu-io/open-design, 100k stars), Ccs Artifact Evaluation (brycewang-stanford/Awesome-Journal-Skills, 1.2k stars) and Mobisys 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 Sigir 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.