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
Interspeech Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging code, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready…
MITAuto-check passed
Install Interspeech Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill interspeech-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills interspeech-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/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation .claude/skills/interspeech-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 "interspeech-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation into .claude/skills/interspeech-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interspeech-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/INTERSPEECH-Skills/skills/interspeech-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 interspeech-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills interspeech-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/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation .agents/skills/interspeech-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 "interspeech-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation into .agents/skills/interspeech-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interspeech-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 interspeech-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills interspeech-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/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation .cursor/skills/interspeech-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 "interspeech-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation into .cursor/skills/interspeech-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interspeech-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 INTERSPEECH-Skills/skills/interspeech-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 interspeech-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills interspeech-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/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation .gemini/skills/interspeech-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 "interspeech-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation into .gemini/skills/interspeech-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interspeech-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 interspeech-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 interspeech-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/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation .github/skills/interspeech-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 "interspeech-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation into .github/skills/interspeech-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interspeech-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 interspeech-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 interspeech-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/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation .opencode/skills/interspeech-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 "interspeech-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/INTERSPEECH-Skills/skills/interspeech-artifact-evaluation into .opencode/skills/interspeech-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "interspeech-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
interspeech-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 680 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, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready…
- Audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review
- SKILL.md covers The speech-specific artifact set, Anonymous demo pages without…, Voice data is personal data and Minimum viable recipe, plus 4 more sections
- Calls git
- Public release at camera-ready
What it does
Interspeech Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready, checkpoint and recipe distribution, voice-data licensing and speaker-consent hygiene, and the ethics of releasing synthesis or cloning systems.
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
- Audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review
- Public release at camera-ready
- Checkpoint and recipe distribution
- Voice-data licensing and speaker-consent hygiene
Example prompts
- “/interspeech-artifact-evaluation”
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:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
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
Interspeech Artifact Evaluation loads about 1.6k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 680 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). 680 words, ~1,582 tokens.
Download SKILL.mdSave it as
.claude/skills/interspeech-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- interspeech-artifact-evaluation
- description
- Use when packaging code, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready, checkpoint and recipe distribution, voice-data licensing and speaker-consent hygiene, and the ethics of releasing synthesis or cloning systems.
INTERSPEECH Artifact Evaluation
Interspeech has no badge-granting artifact committee; the artifact culture is community-enforced instead. Reviewers listen to your samples, and post-publication readers judge the paper by whether the recipe reproduces. This skill treats artifacts in the two states an Interspeech cycle forces: anonymous during review, permanent after acceptance. Check the current author instructions for what may be attached versus linked — attachment rules vary by cycle.
The speech-specific artifact set
| Artifact | Review-time form | Post-acceptance form |
|---|---|---|
| Audio samples (TTS/VC/enhancement) | Anonymous static demo page or attached files | Permanent samples page linked in camera-ready |
| Code + training recipe | Anonymized repo (no usernames in history/CI) | Public repo, tagged at the paper's commit |
| Model checkpoints | Usually withheld (size, identity risk) | Hosted release with license and card |
| Data/corpus contribution | Described + license stated in paper | Archived with DOI, documented splits |
| Scoring/protocol scripts | In the anonymized repo | In the public repo — the piece most reused |
Anonymous demo pages without leaks
Audio demos are where Interspeech anonymity dies. Before the deadline, sweep:
- Hosting identity: GitHub Pages under a personal account, university subdomains, and cloud buckets named after the lab all deanonymize. Use a fresh neutral host.
- Page furniture: analytics IDs, favicon, footer credits, CNAME records.
- The audio itself: a distinctive proprietary voice can identify the lab as surely as a name — consider whether samples themselves are identifying.
- File metadata: WAV/MP3 tags, encoder strings, creation paths.
- Immutability: do not edit the page during review; a changing page suggests post-deadline work and can carry timestamps.
Voice data is personal data
Speech artifacts carry legal and ethical weight that generic ML artifacts do not:
- Corpus licenses bind derivatives. VoxCeleb-trained speaker models, LDC-derived lexicons, and YouTube-scraped audio each constrain what you may redistribute. State, in the paper, the license of every corpus used.
- Speaker consent governs sample release: a cloned or converted voice needs the source speaker's permission to be published, even "just for the demo page."
- Cloning/spoofing dual use: for synthesis and voice-conversion systems, say what you release (recipe? checkpoint? nothing beyond samples?) and why. The community norm — visible in anti-spoofing challenges like ASVspoof — is to weigh release against misuse and to say so explicitly.
- Do not ship raw audio of human subjects unless its license explicitly allows redistribution; ship feature extraction from the official corpus instead.
Minimum viable recipe
A speech result is reproducible when a stranger can regenerate the number, not just run the model. Package for the number:
release/
├── README.md # env, corpus versions + licenses, one command per table row
├── recipe/ # data prep → training → decoding, in run order
├── conf/ # exact configs/hyperparameters used in the paper
├── scoring/ # the WER/EER/MOS-analysis scripts and text-norm rules
├── RESULTS.md # expected outputs with tolerances (seeds, CI width)
└── LICENSE # code license + data-license pointersThe scoring/ directory matters most at this venue: WER moves with text
normalization and EER with trial lists, so publish the measurement, not only the
model (see interspeech-reproducibility).
Show full SKILL.md (254 more words)Show less
Timing across the cycle
- Before submission: anonymous demo page frozen; repo anonymized if linked.
- During review: touch nothing linked from the paper.
- At camera-ready (19 June in the 2026 cycle): flip links to permanent public homes; tag the repo; add the paper's ISCA Archive DOI to the README.
- Conference week: QR to samples on the poster; the demo page is your talk's safety net when room audio fails.
Leak patterns seen in the wild
Concrete ways speech artifacts have deanonymized their authors — sweep for each:
- A demo page hosted at
<university>.github.io/<lab-project>/linked straight from the anonymous PDF. - Git history in a "fresh" anonymized repo carrying the original committer
emails (
git log --format='%ae' | sort -ubefore sharing). - WAV files whose RIFF metadata names the workstation user; MP3 ID3 tags naming the recording engineer.
- A samples page reusing the lab's distinctive in-house TTS voice from previous published demos — recognizable by ear.
- Cloud-bucket URLs containing the grant acronym.
- Analytics or font-loader scripts on the demo page tying it to the lab's other properties.
Checkpoint release decision aid
| Situation | Community-normal release |
|---|---|
| ASR/SSL encoder on licensed-but-public corpora | Recipe + checkpoint |
| Speaker-verification model on VoxCeleb-family data | Recipe + checkpoint, license noted |
| TTS/VC on a consenting or synthetic voice | Recipe + checkpoint + samples |
| TTS/VC cloning a real speaker without release rights | Recipe + samples only; no checkpoint |
| Anything trained on scraped, unlicensed audio | Describe honestly; release nothing derived |
Output format
[Artifact inventory] samples / code / checkpoints / data / scoring — state of each
[Anonymity sweep] host, page, metadata, voice-identifiability findings
[License chain] corpus licenses → what may be redistributed
[Consent/dual-use] speaker permission status; release decision + rationale
[Release plan] what flips public at camera-ready, where it lives permanently
[Gaps] <ordered fixes>Attachment size limits, link policies, and any new artifact requirements are
cycle-volatile — verify on the current author pages (2026 sources logged in
resources/official-source-map.md, checked 2026-07-08).
© 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 INTERSPEECH-Skills/skills/interspeech-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Interspeech 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 |
|---|---|---|---|---|---|---|
| Interspeech 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 Interspeech Artifact Evaluation
What does Interspeech Artifact Evaluation do?
A skill your agent uses when packaging code, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready…. Interspeech Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging code, models, and audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review, public release at camera-ready, checkpoint and recipe distribution, voice-data licensing and speaker-consent hygiene, and the ethics of releasing synthesis or cloning systems.
When should I use Interspeech Artifact Evaluation?
Interspeech Artifact Evaluation fits situations like: audio artifacts for an INTERSPEECH paper — anonymous demo pages and sample audio during double-blind review; public release at camera-ready; checkpoint and recipe distribution; voice-data licensing and speaker-consent hygiene.
How do I install Interspeech Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill interspeech-artifact-evaluation -a claude-code`. Or copy the skill folder (INTERSPEECH-Skills/skills/interspeech-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/interspeech-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Interspeech Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill interspeech-artifact-evaluation -a codex`. Or copy the skill folder (INTERSPEECH-Skills/skills/interspeech-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/interspeech-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Interspeech 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 interspeech-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/interspeech-artifact-evaluation, .gemini/skills/interspeech-artifact-evaluation, .github/skills/interspeech-artifact-evaluation and .opencode/skills/interspeech-artifact-evaluation in your project.
What does Interspeech Artifact Evaluation need to run?
Going by SKILL.md and its folder, Interspeech Artifact Evaluation needs the command-line tools its instructions call (git).
Does Interspeech Artifact Evaluation access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Interspeech 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 Interspeech Artifact Evaluation use?
Interspeech 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 Interspeech Artifact Evaluation use?
About 1.6k tokens (SKILL.md is roughly 6.3k 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 Interspeech Artifact Evaluation?
Skills that share tags, products or a category with Interspeech 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 Interspeech 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.