icassp-artifact-evaluation
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-artifact-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 reposito
- Artifact plan
- What ICASSP evidence reviewers open first
- Worked vignette: packaging a separation result
- Turnkey scoring stub
- Calibration anchors
- Output format
One command should reproduce the headline metric from released outputs. python3 score.py \
What does the icassp-artifact-evaluation skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-artifact-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.