A skill your agent uses when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing…

MITAuto-check passed

Install Icdm Topic Selection

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icdm-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/ICDM-Skills/skills/icdm-topic-selection .claude/skills/icdm-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
icdm-topic-selection
GitHub stars
1.2k
Token cost
~1.2k tokens
SKILL.md length
579 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 is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing…

  • Deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit
  • SKILL.md covers Fit test, Track fork within ICDM, Fit signal table and The routing calendar from…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Choosing among its Research

What it does

Icdm Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing ICDM with KDD, SDM, CIKM, WSDM, WWW, ICDE, or the ML flagships by contribution type, sponsor community, and the data-mining routing calendar seen from ICDM's June deadline.

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.

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 is a strong ICDM (IEEE International Conference on Data Mining) fit
  • Choosing among its Research
  • Blue Sky tracks
  • Comparing ICDM with KDD

Example prompts

  • “/icdm-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

Icdm Topic Selection loads about 1.2k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 579 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~93
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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). 579 words, ~1,243 tokens.

Download SKILL.mdSave it as .claude/skills/icdm-topic-selection/SKILL.md (or your agent's skills folder).
name
icdm-topic-selection
description
Use when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing ICDM with KDD, SDM, CIKM, WSDM, WWW, ICDE, or the ML flagships by contribution type, sponsor community, and the data-mining routing calendar seen from ICDM's June deadline.

ICDM Topic Selection

Use this before writing. Two decisions happen here: is the work ICDM-shaped at all, and if so, which track. ICDM is the IEEE-sponsored data-mining flagship; it rewards a named data-mining mechanism on a defined mining task with strong baselines and a scalability or discovery-validity story — not pure learning theory, and not a broad deep-learning systems result.

Fit test

  • Prefer ICDM when the contribution is a data-mining method: pattern discovery, graph mining, anomaly detection, temporal/streaming mining, clustering, or scalable analytics, with an algorithmic idea and defensible empirical evidence.
  • Route to SDM (SIAM) if the contribution is primarily mathematical/statistical rigor in a mining method — SDM's community weights theory and analysis more heavily.
  • Route to KDD if the work is large-scale applied discovery or a deployed data-science system aimed at the biggest data-mining audience and its two-cycle calendar.
  • Route to CIKM for information/knowledge management, IR, and database-adjacent work; to WSDM for web-and-social search and mining; to WWW/TheWebConf for web-native contributions; to ICDE/SIGMOD/VLDB for database-systems results.
  • Route to an ML flagship (NeurIPS/ICML/ICLR) if the contribution is a general learning method with thin data-mining specificity.

Track fork within ICDM

Track2026 reviewBest for
ResearchTriple-blindA novel mining algorithm/mechanism with baselines and scale evidence
AppliedSingle-blind (new in 2026)A deployed/industrial system with measured real-world impact
Blue SkyCCC-sponsoredA visionary, forward-looking position with a research agenda

If a project is a deployed system whose contribution is the deployment and its measured outcomes, the Applied Track fits and spares you the triple-blind anonymization burden. If the contribution is the algorithm and the deployment is illustrative, stay on Research.

Fit signal table

Signal in the projectICDM reading
Named mining mechanism + baselines + scaling curveCore fit — the house genre
Anomaly/graph/pattern/stream mining with a discovery-validity argumentCore fit
Deployed system with quantified impact, deployment is the pointApplied Track
Pure statistical/theoretical mining analysisBetter served at SDM
Broad deep-learning method, little mining specificityRoute to an ML flagship
Database-systems or query contributionRoute to ICDE/SIGMOD/VLDB
Show full SKILL.md (247 more words)Show less

The routing calendar from ICDM's seat

ICDM's deadline sits in June, conference in November. That position matters when choosing where a finished project goes next: a paper not ready for ICDM's June can often target CIKM (spring deadline, autumn conference) the same year, WSDM (late-summer deadline, following spring), SDM (autumn deadline, following spring), or KDD's next cycle. Choose by community and format fit, not prestige — the same result reads differently to each pool.

Vignette: where a streaming anomaly detector goes

A project delivers a one-pass anomaly detector for edge streams with a memory bound and experiments on injected anomalies. ICDM reading: strong Research Track fit — a named mining mechanism, a scaling argument, and a discovery-validity claim. Strip the mechanism and keep only "we deployed it and fraud dropped," and it becomes an Applied Track paper (or a KDD applied submission). Grow it into a pure asymptotic analysis of the sketch with no system, and SDM becomes the better community.

Sharpening moves before committing

  • Name the mining task and the data regime in one sentence; if you cannot, the ICDM framing does not exist yet.
  • Name the single mechanism the contribution rests on, and the baseline it beats for a stated reason, not just on a leaderboard.
  • Confirm the whole argument — body, references, appendix — can fit ICDM's 10-page all-inclusive cap; a result needing 14 pages is a journal or SDM paper.
  • Topic emphasis and track lineup drift between editions; scan the current calls before final routing.

Output format

text
[Fit] strong ICDM / possible ICDM / better elsewhere
[Track] Research / Applied / Blue Sky
[Best venue] ICDM / KDD / SDM / CIKM / WSDM / WWW / ICDE / ML-flagship / journal
[Contribution sentence] <one sentence naming task + mechanism>
[Top rejection risk] <novelty / baselines / scale / discovery-validity / fit>
[Next action] <experiment, framing, track switch, 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 ICDM-Skills/skills/icdm-topic-selection of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Icdm Topic Selection 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.

Icdm Topic Selection compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Icdm Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.2kAutomated safety check: PassMIT
TopicsZimoLiao/scholaraio577—~294Automated safety check: PassMIT
Topic Modelingbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~3.7kAutomated safety check: PassCustom licence
Bestblogs Topicginobefun/BestBlogs4k—~670Automated safety check: PassNone
Zsxq Topicitwanger/toBeBetterJavaer18k—~564Automated safety check: PassNone
The Strong Nomohitagw15856/pm-claude-skills1.4k—~987Automated safety check: PassMIT

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Questions about Icdm Topic Selection

What does Icdm Topic Selection do?

A skill your agent uses when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing…. Icdm Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit, choosing among its Research, Applied, and Blue Sky tracks, and comparing ICDM with KDD, SDM, CIKM, WSDM, WWW, ICDE, or the ML flagships by contribution type, sponsor community, and the data-mining routing calendar seen from ICDM's June deadline.

When should I use Icdm Topic Selection?

Icdm Topic Selection fits situations like: deciding whether a project is a strong ICDM (IEEE International Conference on Data Mining) fit; choosing among its Research; blue Sky tracks; comparing ICDM with KDD.

How do I install Icdm Topic Selection in Claude Code?

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

How do I install Icdm Topic Selection in Codex?

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

Can I use Icdm 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 icdm-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/icdm-topic-selection, .gemini/skills/icdm-topic-selection, .github/skills/icdm-topic-selection and .opencode/skills/icdm-topic-selection in your project.

What does Icdm Topic Selection need to run?

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

Does Icdm 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 Icdm 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 Icdm Topic Selection use?

Icdm 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 Icdm Topic Selection use?

About 1.2k 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 Icdm Topic Selection?

Skills that share tags, products or a category with Icdm Topic Selection: Topics (ZimoLiao/scholaraio, 577 stars), Topic Modeling (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), Bestblogs Topic (ginobefun/BestBlogs, 4k stars) and Zsxq Topic (itwanger/toBeBetterJavaer, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Icdm 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.