Bio Fragment Analysis
GPTomics/bioSkills
Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and…
A skill your agent uses when strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the paper's metric, dataset versions and splits, front-end/DSP…
Install Icassp Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icassp-reproducibility --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/ICASSP-Skills/skills/icassp-reproducibility .claude/skills/icassp-reproducibility && 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 "icassp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-reproducibility into .claude/skills/icassp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-reproducibility", 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/ICASSP-Skills/skills/icassp-reproducibilityType 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 icassp-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icassp-reproducibility --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/ICASSP-Skills/skills/icassp-reproducibility .agents/skills/icassp-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "icassp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-reproducibility into .agents/skills/icassp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-reproducibility", 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 icassp-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icassp-reproducibility --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/ICASSP-Skills/skills/icassp-reproducibility .cursor/skills/icassp-reproducibility && 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 "icassp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-reproducibility into .cursor/skills/icassp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-reproducibility", 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 ICASSP-Skills/skills/icassp-reproducibility--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 icassp-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icassp-reproducibility --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/ICASSP-Skills/skills/icassp-reproducibility .gemini/skills/icassp-reproducibility && 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 "icassp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-reproducibility into .gemini/skills/icassp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-reproducibility", 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 icassp-reproducibilityInstalls 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 icassp-reproducibility -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/ICASSP-Skills/skills/icassp-reproducibility .github/skills/icassp-reproducibility && 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 "icassp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-reproducibility into .github/skills/icassp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-reproducibility", 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 icassp-reproducibility -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 icassp-reproducibility --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/ICASSP-Skills/skills/icassp-reproducibility .opencode/skills/icassp-reproducibility && 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 "icassp-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICASSP-Skills/skills/icassp-reproducibility into .opencode/skills/icassp-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icassp-reproducibility", 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-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 483 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the paper's metric, dataset versions and splits, front-end/DSP…
- Strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the papers metric
- SKILL.md covers The evidence spine, The scoring ruler is the thing…, Front-end determinism and Degrees of reproducibility, plus 3 more sections
- Calls python3 and pip
- Dataset versions and splits
What it does
Icassp Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the paper's metric, dataset versions and splits, front-end/DSP settings, seeds, and compute, and mapping each claim to a checkable location, since ICASSP has no reviewed appendix and the four pages plus a public release must carry it.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research. 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
- Strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the papers metric
- Dataset versions and splits
- Front-end/DSP settings
- Mapping each claim to a checkable location
Example prompts
- “/icassp-reproducibility”
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:
python3pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, 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
Icassp Reproducibility loads about 1.2k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 483 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). 483 words, ~1,153 tokens.
Download SKILL.mdSave it as
.claude/skills/icassp-reproducibility/SKILL.md (or your agent's skills folder).- name
- icassp-reproducibility
- description
- Use when strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the paper's metric, dataset versions and splits, front-end/DSP settings, seeds, and compute, and mapping each claim to a checkable location, since ICASSP has no reviewed appendix and the four pages plus a public release must carry it.
ICASSP Reproducibility
Use this before submission and again before camera-ready. ICASSP has no reviewed supplement, so reproducibility rests on what the four pages state plus whatever you release publicly (which, under single-blind review, may be public immediately). The recurring ICASSP failure is not a missing repository — it is a number whose measurement cannot be reconstructed.
The evidence spine
Map each claim — an algorithm result, a theoretical bound, or an empirical metric — to a checkable location in the paper or the released package:
- For an empirical result: dataset and version, split or trial list, front-end/DSP settings, model, the exact scorer and its configuration, seeds, number of runs, and reported spread.
- For an estimation/detection result: the signal and noise model, the estimator, and the reference bound (e.g., Cramér-Rao) the result is compared against.
- For a real-time or embedded claim: hardware, latency or real-time factor, and memory.
- Explain any data you cannot release honestly, and describe how a reader could reproduce from the licensed source.
The scoring ruler is the thing that decays
Across ICASSP's modalities, the same trap recurs: the metric name is stated but the ruler behind it is not, so the number is unreproducible.
| Modality | Metric | The ruler that must be pinned |
|---|---|---|
| Speech recognition | WER / CER | Text normalization, scoring tool, reference edition |
| Enhancement / separation | SI-SDR, PESQ, STOI | Reference alignment, permutation policy, mode/wideband setting |
| Speaker / biometrics | EER, minDCF | Trial list, score normalization, DCF operating point |
| Image / video restoration | PSNR, SSIM | Border handling, bit depth, color space, crop |
| Communications | BER / BLER | SNR definition, channel model, decoder settings |
| Estimation | RMSE / MSE | SNR range, trial count, and the bound compared to |
Ship the ruler, not just the model: a released checkpoint with no scorer configuration cannot reproduce the headline metric.
Show full SKILL.md (197 more words)Show less
Front-end determinism
Signal papers decay silently through the front end. Pin the sample rate, framing, window function, FFT size, feature type, and any resampling. A change from a 25 ms to a 20 ms window, or a resampler swap, moves every downstream number without touching the model — and reviewers who reproduce will notice.
Degrees of reproducibility
- Turnkey — one command regenerates each reported metric from released outputs and seeds.
- Scripted — scripts exist but need documented manual steps or licensed-data access.
- Descriptive — prose detailed enough that a competent engineer could rebuild the pipeline.
For ICASSP, make the scoring path turnkey even when full training stays scripted; reviewers rerun scorers, not trainings. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.
Reproducibility stub
# Pin the environment and the ruler; regenerate the headline number.
pip install -r requirements.txt # exact versions, including the DSP/feature lib
python3 run_eval.py --config configs/main.yaml --seed 1
python3 run_eval.py --config configs/main.yaml --seed 2
python3 run_eval.py --config configs/main.yaml --seed 3
python3 aggregate.py --runs runs/ --report mean_std # matches Table 1 mean ± spreadVignette: a keyword-spotting paper
A submission reports detection accuracy for a small-footprint keyword spotter. Its reproducibility spine: the corpus version and split, the feature front-end (sample rate, mel bins, window), the decision threshold and how it was set, seeds and run count, the on-device latency, and the exact scorer for the false-alarm/false-reject operating point — plus one honest sentence on the condition it was not evaluated under (e.g., far-field noise).
Output format
[Claim inventory] <claim -> checkable location>
[Scoring ruler] pinned / partial / missing
[Front-end] sample rate / framing / features pinned?
[Randomness] seeds + run count + reported spread
[Reproducibility level] turnkey / scripted / descriptive
[Fixes] <what must appear in the 4 pages vs the released package>© 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 ICASSP-Skills/skills/icassp-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Icassp Reproducibility 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 |
|---|---|---|---|---|---|---|
| Icassp Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Bio Fragment AnalysisGPTomics/bioSkills | 1.2k | 1 repos | ~4.3k | Automated safety check: Pass | MIT | |
| Figuresbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~2.1k | Automated safety check: Pass | Custom licence | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 |
Similar skills
Figures
brycewang-stanford/Auto-Empirical-Research-Skills
Design and format publication-quality figures: chart choice, color, scales, legends, captions, reproducibility.
Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
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Related
Categories
Questions about Icassp Reproducibility
What does Icassp Reproducibility do?
A skill your agent uses when strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the paper's metric, dataset versions and splits, front-end/DSP…. Icassp Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the paper's metric, dataset versions and splits, front-end/DSP settings, seeds, and compute, and mapping each claim to a checkable location, since ICASSP has no reviewed appendix and the four pages plus a public release must carry it.
When should I use Icassp Reproducibility?
Icassp Reproducibility fits situations like: strengthening ICASSP reproducibility across signal-processing modalities — pinning the scoring ruler for the papers metric; dataset versions and splits; front-end/DSP settings; mapping each claim to a checkable location.
How do I install Icassp Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-reproducibility -a claude-code`. Or copy the skill folder (ICASSP-Skills/skills/icassp-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icassp-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Icassp Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-reproducibility -a codex`. Or copy the skill folder (ICASSP-Skills/skills/icassp-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icassp-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Icassp Reproducibility 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-reproducibility -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-reproducibility, .gemini/skills/icassp-reproducibility, .github/skills/icassp-reproducibility and .opencode/skills/icassp-reproducibility in your project.
What does Icassp Reproducibility need to run?
Going by SKILL.md and its folder, Icassp Reproducibility needs the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.
Does Icassp Reproducibility access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Icassp Reproducibility 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 Reproducibility use?
Icassp Reproducibility 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 Reproducibility use?
About 1.2k tokens (SKILL.md is roughly 4.6k 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 Icassp Reproducibility?
Skills that share tags, products or a category with Icassp Reproducibility: Bio Fragment Analysis (GPTomics/bioSkills, 1.2k stars), Figures (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars) and CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Icassp Reproducibility?
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.