Agent skill

Icassp Artifact Evaluation

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

A skill your agent uses when packaging ICASSP code, data, audio or image samples, model checkpoints, scoring scripts, seeds, and logs, even though ICASSP has no formal artifact badge.

MITAuto-check passed

Install Icassp Artifact Evaluation

skills CLI

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

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

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills icassp-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/ICASSP-Skills/skills/icassp-artifact-evaluation .claude/skills/icassp-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 "icassp-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-artifact-evaluation into .claude/skills/icassp-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-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/ICASSP-Skills/skills/icassp-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 icassp-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 icassp-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/ICASSP-Skills/skills/icassp-artifact-evaluation .agents/skills/icassp-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 "icassp-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-artifact-evaluation into .agents/skills/icassp-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-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 icassp-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 icassp-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/ICASSP-Skills/skills/icassp-artifact-evaluation .cursor/skills/icassp-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 "icassp-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-artifact-evaluation into .cursor/skills/icassp-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-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 ICASSP-Skills/skills/icassp-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 icassp-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 icassp-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/ICASSP-Skills/skills/icassp-artifact-evaluation .gemini/skills/icassp-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 "icassp-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-artifact-evaluation into .gemini/skills/icassp-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-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 icassp-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 icassp-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/ICASSP-Skills/skills/icassp-artifact-evaluation .github/skills/icassp-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 "icassp-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-artifact-evaluation into .github/skills/icassp-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-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 icassp-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 icassp-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/ICASSP-Skills/skills/icassp-artifact-evaluation .opencode/skills/icassp-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 "icassp-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-artifact-evaluation into .opencode/skills/icassp-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-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: icassp-artifact-evaluation
GitHub stars: 1.2k
Token cost: ~1.1k tokens
SKILL.md length: 466 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when packaging ICASSP code, data, audio or image samples, model checkpoints, scoring scripts, seeds, and logs, even though ICASSP has no formal artifact badge.

Packaging ICASSP code
SKILL.md covers Artifact plan, What ICASSP evidence reviewers…, Worked vignette: packaging a… and Turnkey scoring stub, plus 2 more sections
Calls python3
Model checkpoints

What it does

Icassp Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging ICASSP code, data, audio or image samples, model checkpoints, scoring scripts, seeds, and logs, even though ICASSP has no formal artifact badge. Covers what signal-processing reviewers actually open, how single-blind review lets artifacts be public from the start, and how to make a task's measurement reproducible turnkey.

Its SKILL.md is about 1.1k 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 ICASSP code
Model checkpoints
Scoring scripts
Even though ICASSP has no formal artifact badge

Example prompts

“/icassp-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:
- python3
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

Icassp Artifact Evaluation loads about 1.1k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 466 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~92
When it runs · the whole SKILL.md, loaded when a task matches: ~1.1k

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). 466 words, ~1,132 tokens.

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

name: icassp-artifact-evaluation
description: Use when packaging ICASSP code, data, audio or image samples, model checkpoints, scoring scripts, seeds, and logs, even though ICASSP has no formal artifact badge. Covers what signal-processing reviewers actually open, how single-blind review lets artifacts be public from the start, and how to make a task's measurement reproducible turnkey.

ICASSP Artifact Evaluation

Use this for evidence packaging around ICASSP. There is no formal artifact-evaluation committee or badge at ICASSP as of the 2026 cycle; artifacts are voluntary and their value is in review credibility and post-publication impact. Because review is single-blind, a repository can carry your name from day one — no anonymous mirror is required.

Artifact plan

Decide what evidence supports the four-page claim: training and evaluation code, the exact dataset splits or trial lists, the scoring script, model checkpoints, seeds, logs, and a handful of qualitative samples (audio, spectrogram, image, or signal plot).
Keep the decision-critical numbers reproducible from the released package; a reviewer who cannot regenerate the headline metric discounts it.
Ship a minimal reproduction map: environment, dependencies, hardware, commands, expected outputs, runtime, seeds, and any known nondeterminism (GPU kernels, thread counts).
For restricted corpora you cannot redistribute, provide enough provenance and preprocessing to reproduce from the licensed source without violating the data-use terms.
Because links can be public, put the repository URL in the paper and test it from a logged-out browser before submission.

What ICASSP evidence reviewers open first

Claim type	First artifact inspected	Common failure caught
Recognition/detection accuracy	Scoring script + dataset split	WER/error computed with an undocumented text-normalization or split
Enhancement / separation gain	Reference-aligned SI-SDR/PESQ/STOI scorer	Metric computed with mismatched reference or windowing
Speaker / biometric verification	Trial list + EER/minDCF scorer	Non-standard trials that inflate the score
Image/signal restoration	PSNR/SSIM script + test set	Metric on a different crop, bit depth, or borders
Real-time claim	Latency/RTF measurement harness	Feasibility asserted, never measured

Signal-processing reviewers will rerun a scorer on a provided output far more readily than they will retrain a model, so make the measurement path turnkey before polishing training code.

Show full SKILL.md (181 more words)Show less

Worked vignette: packaging a separation result

A hypothetical submission reports source-separation gain on a two-speaker mixture set.

Ship the mixture generation as one parameterized script (seed, SNR range, corpus source), not as constants buried in a notebook, so a reviewer can regenerate the exact test mixtures.
Include the reference-aligned SI-SDR scorer with its settings; separation numbers are meaningless if the alignment and permutation handling differ.
Emit result tables directly from logged outputs so the paper's numbers and the artifact's numbers cannot drift apart.
Provide five listenable example mixtures with their separated outputs; a reviewer often judges perceptual quality from these before reading the metric table.

Turnkey scoring stub

bash

# One command should reproduce the headline metric from released outputs.
python3 score.py \
  --hyp outputs/dev.hyp \
  --ref data/dev.ref \
  --metric si-sdr \
  --config configs/scoring.yaml   # window, alignment, permutation policy pinned here
# Expected: dev SI-SDR = <value in Table 1> (mean over 3 seeds)

Calibration anchors

Assume only the README and one entry script get opened; design the package so the top-level README reproduces the main number in one command.
Do not confuse a released toolkit with an artifact: ICASSP reviewers want the exact recipe that produced this paper's numbers, not a general library dump.
Formats and sizes for any uploaded supplementary media are cycle-specific; verify against the current paper kit rather than past years.

Output format

text

[Artifact role] public release / demo samples / scoring package
[Contents] code / data-split / scorer / checkpoints / seeds / samples
[Reproduction level] turnkey / scripted / descriptive / weak
[Measurement risks] metric ruler / split / alignment / normalization
[Fixes before release] <ordered list>

Files

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

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Icassp 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.

Icassp Artifact Evaluation compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Icassp Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.1k	Automated safety check: Pass	MIT	13 days ago
Arize Evaluatorgithub/awesome-copilot	40k	1 repos	~8.1k	Automated safety check: Notes	MIT	2 days ago
Artifacts Buildernexu-io/open-design	100k	—	~347	Automated safety check: Pass	Apache-2.0	today
Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~969	Automated safety check: Pass	MIT	13 days ago
Mobisys Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1k	Automated safety check: Pass	MIT	13 days ago
Fast Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills	1.2k	—	~1.6k	Automated safety check: Pass	MIT	13 days ago

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Questions about Icassp Artifact Evaluation

What does Icassp Artifact Evaluation do?

A skill your agent uses when packaging ICASSP code, data, audio or image samples, model checkpoints, scoring scripts, seeds, and logs, even though ICASSP has no formal artifact badge. Icassp Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging ICASSP code, data, audio or image samples, model checkpoints, scoring scripts, seeds, and logs, even though ICASSP has no formal artifact badge.

When should I use Icassp Artifact Evaluation?

Icassp Artifact Evaluation fits situations like: packaging ICASSP code; model checkpoints; scoring scripts; even though ICASSP has no formal artifact badge.

How do I install Icassp Artifact Evaluation in Claude Code?

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

How do I install Icassp Artifact Evaluation in Codex?

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

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

What does Icassp Artifact Evaluation need to run?

Going by SKILL.md and its folder, Icassp Artifact Evaluation needs the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Icassp 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 Icassp 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 Icassp Artifact Evaluation use?

Icassp 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 Icassp Artifact Evaluation use?

About 1.1k tokens (SKILL.md is roughly 4.5k 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 Icassp Artifact Evaluation?

Skills that share tags, products or a category with Icassp 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 Icassp 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 Icassp 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

ICASSP Artifact Evaluation

Artifact plan

What ICASSP evidence reviewers open first

Worked vignette: packaging a separation result

Turnkey scoring stub

Calibration anchors

Output format

Files

Compare with similar skills

Similar skills

More from brycewang-stanford/Awesome-Journal-Skills

Questions about Icassp Artifact Evaluation