Agent skill

Icassp Topic Selection

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

A skill your agent uses when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech…

MITAuto-check passed

Install Icassp Topic Selection

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icassp-topic-selection -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icassp-topic-selection --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-topic-selection .claude/skills/icassp-topic-selection && 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/

Facts

Skill name
icassp-topic-selection
GitHub stars
1.2k
Token cost
~1.3k tokens
SKILL.md length
541 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech…

  • Deciding whether a project fits ICASSP
  • SKILL.md covers Find the signal-processing…, The ICASSP-vs-Interspeech…, Fit signal table (full ICASSP… and Re-route table, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • The IEEE Signal Processing Society flagship spanning all signal processing

What it does

Icassp Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech routing decision for speech work, plus routing to ICIP, EUSIPCO, WASPAA, SPS journals, or ML venues by identifying the signal-processing primitive of the contribution.

Its SKILL.md is about 1.3k 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

  • Deciding whether a project fits ICASSP
  • The IEEE Signal Processing Society flagship spanning all signal processing
  • Specifically for the ICASSP-versus-Interspeech routing decision for speech work
  • Plus routing to ICIP

Example prompts

  • “/icassp-topic-selection”

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

    No scripts in the folder and no shell commands in SKILL.md.

    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 Topic Selection loads about 1.3k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 541 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
~1.3k

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). 541 words, ~1,250 tokens.

Download SKILL.mdSave it as .claude/skills/icassp-topic-selection/SKILL.md (or your agent's skills folder).
name
icassp-topic-selection
description
Use when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech routing decision for speech work, plus routing to ICIP, EUSIPCO, WASPAA, SPS journals, or ML venues by identifying the signal-processing primitive of the contribution.

ICASSP Topic Selection

Use this before writing. ICASSP is the IEEE Signal Processing Society flagship, and its defining feature is breadth: speech and audio, image and video, communications and radar, sensor arrays, estimation and detection theory, and machine learning for signals all review under one roof. Speech is one track among many, not the identity of the venue. A project fits when its contribution changes a signal-processing primitive and can be proved in four pages.

Find the signal-processing primitive

Name what the contribution actually is:

  • A new estimator, transform, filter, or detector; a new representation or embedding; a new objective or inference procedure; a resource (corpus/benchmark); or a model-based-deep-learning result that changes a block of the signal chain.
  • If no signal-processing primitive exists — the paper is generic ML with no signal model — ICASSP is likely the wrong venue (see the re-route table).

The ICASSP-vs-Interspeech routing decision (read this for any speech paper)

Speech papers can go to either venue, and picking wrong costs a cycle. They differ on mechanics as much as on scope:

DimensionICASSP (IEEE SPS)Interspeech (ISCA)
ScopeAll signal processing; speech is one trackSpeech and spoken language only
CultureSignal-processing methods, math-forwardSpeech-science + engineering, spoken-language focus
Format4+1 (4 content pages + reference page)4+1 (4 content + reference page), different template
AnonymitySingle-blind — author list includedDouble-anonymous with a pre-deadline anonymity period
DeadlineSeptember (autumn)Late February (winter)
PortalCMS (cmsworkshops.com)Microsoft CMT
ProceedingsIEEE XploreISCA Archive, open access

Decision rule: route to ICASSP when the contribution is a signal-processing method whose speech application is one instance (a new front-end, estimator, separation objective, or array technique), or when the September calendar and single-blind culture fit. Route to Interspeech when the contribution is fundamentally about spoken language — phonetics, prosody, dialogue, speech science, or a speech-specific model where the linguistic content is the point — or when the February calendar and double-blind norms fit. When both fit, let deadline and reviewer community decide.

Show full SKILL.md (216 more words)Show less

Fit signal table (full ICASSP breadth)

Signal in the projectICASSP reading
New estimator/filter/detector with a signal model + evaluationCore fit — the house genre
Speech/audio method framed as a signal-processing mechanismCore fit (vs Interspeech if it is spoken-language-centric)
Image/video restoration, coding, or analysisCore fit (compare ICIP for image-specific work)
Communications, radar, array, or sensor signal processingCore fit
Generic deep net with strong benchmarks but no signal insightBetter at NeurIPS/ICML/ICLR/AAAI
A finished, journal-length result with extensive theoryAn SPS journal (TSP/TASLP/SPL)

Re-route table

  • Generic ML, no signal model → NeurIPS, ICML, ICLR, AAAI.
  • Spoken-language-centric speech → Interspeech (or SLT/ASRU for those subfields).
  • Image-specific → ICIP; European/audio-workshop → EUSIPCO, WASPAA, DCASE.
  • Journal-length, finished → IEEE TSP, TASLP, SPL, TIP, or a sibling SPS journal; consider the OJSP-ICASSP track to present at ICASSP while publishing open-access in a journal.

Sharpening moves before committing

  • State the primitive in one sentence; if you cannot, the ICASSP framing does not yet exist.
  • Confirm the result fits four pages with a task-matched metric and a standard baseline; a claim that needs journal-length exposition should go to an SPS journal or the OJSP track.
  • For speech work, run the ICASSP-vs-Interspeech table explicitly rather than defaulting to whichever deadline is closer.
  • Subject-area emphasis drifts by cycle; scan the current EDICS list before final routing.

Output format

text
[Fit] strong ICASSP / possible ICASSP / better elsewhere
[Primitive] estimator / transform / representation / objective / resource / model-based-DL / none
[If speech] ICASSP vs Interspeech -> <decision + reason (scope/calendar/blinding)>
[Best venue] ICASSP / Interspeech / ICIP / EUSIPCO / WASPAA / SPS journal / ML venue
[Contribution sentence] <one signal-processing claim>
[Next action] <framing, experiment, or venue switch>

© 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-topic-selection of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

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Signal Channel for NanoClawnanocoai/nanoclaw31k—~3.6kAutomated safety check: NotesMIT

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Questions about Icassp Topic Selection

What does Icassp Topic Selection do?

A skill your agent uses when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech…. Icassp Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project fits ICASSP, the IEEE Signal Processing Society flagship spanning all signal processing, and specifically for the ICASSP-versus-Interspeech routing decision for speech work, plus routing to ICIP, EUSIPCO, WASPAA, SPS journals, or ML venues by identifying the signal-processing primitive of the contribution.

When should I use Icassp Topic Selection?

Icassp Topic Selection fits situations like: deciding whether a project fits ICASSP; the IEEE Signal Processing Society flagship spanning all signal processing; specifically for the ICASSP-versus-Interspeech routing decision for speech work; plus routing to ICIP.

How do I install Icassp Topic Selection in Claude Code?

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

How do I install Icassp Topic Selection in Codex?

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

Can I use Icassp Topic Selection 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-topic-selection -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-topic-selection, .gemini/skills/icassp-topic-selection, .github/skills/icassp-topic-selection and .opencode/skills/icassp-topic-selection in your project.

What does Icassp Topic Selection need to run?

SKILL.md names no scripts, command-line tools or credentials: Icassp Topic Selection is instructions for the agent only.

Does Icassp Topic Selection 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 Topic Selection 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 Topic Selection use?

Icassp Topic Selection 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 Topic Selection use?

About 1.3k tokens (SKILL.md is roughly 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 Topic Selection?

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Who maintains Icassp Topic Selection?

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.