A skill your agent uses when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps…

MITAuto-check passedResearch & Science

Install Kdd Related Work

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-related-work -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-related-work --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/KDD-Skills/skills/kdd-related-work .claude/skills/kdd-related-work && 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
kdd-related-work
GitHub stars
1.2k
Token cost
~1.6k tokens
SKILL.md length
738 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps…

  • Works in 5 steps: Nearest KDD ancestor identified, cited,… → Every benchmarked baseline is also… → All venue attributions of "classic"… → …
  • Positioning a KDD submission against the data-mining lineage (prior KDD volumes
  • SKILL.md covers The lineage obligation, Venue map for positioning, Misattribution traps and Novelty sentence pattern, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Kdd Related Work is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps, cross-cycle resubmission overlap, concurrent arXiv work, and the mechanism-contrast style of novelty argument that ACM SIGKDD reviewers expect.

Its SKILL.md is about 1.6k 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 Literature review, Positioning and messaging and Academic paper search. It works with arXiv. 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

  • Positioning a KDD submission against the data-mining lineage (prior KDD volumes
  • WWW) and the ML flagships
  • Handling venue misattribution traps
  • Cross-cycle resubmission overlap

Example prompts

  • “/kdd-related-work”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Nearest KDD ancestor identified, cited, and mechanically contrasted.
  2. Every benchmarked baseline is also positioned in prose (benchmarking without
  3. All venue attributions of "classic" papers spot-checked against ACM DL/DBLP.
  4. Two-lane coverage: at least the mining lane and one adjacent lane (ML, systems, or
  5. Overlap declarations drafted: resubmission id, arXiv status, sibling submissions.

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

Kdd Related Work loads about 1.6k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 738 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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). 738 words, ~1,646 tokens.

Download SKILL.mdSave it as .claude/skills/kdd-related-work/SKILL.md (or your agent's skills folder).
name
kdd-related-work
description
Use when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps, cross-cycle resubmission overlap, concurrent arXiv work, and the mechanism-contrast style of novelty argument that ACM SIGKDD reviewers expect.

Use this to audit positioning before the writing freeze. KDD sits inside a dense family of venues that share topics but not registers, and its reviewers know the mining lineage personally — misplacing a classic paper's venue or missing the direct KDD predecessor of your own method are both instantly visible errors here.

The lineage obligation

A KDD submission is expected to know its own conference's history on the topic. Before writing, build the ancestry chain: which prior KDD papers created the problem's vocabulary, and what does this paper change structurally? The DeepWalk (KDD 2014) → node2vec (KDD 2016) → metapath2vec (KDD 2017) chain in resources/exemplars/library.md is the canonical shape — each successor names the structural property of the data its predecessor mishandled. A related-work section that surveys families ("embedding methods can be divided into...") instead of stating deltas is journal register, not KDD register.

Venue map for positioning

Literature laneVenuesWhat KDD reviewers check
Own lineagePrior KDD volumes (ACM DL)Is the nearest KDD ancestor cited and contrasted mechanically?
Mining siblingsICDM, SDM, PKDD/ECML, CIKMAre same-shaped methods here distinguished, not ignored?
Web/search/recWWW, WSDM, SIGIR, RecSysFor graph/behavior/recsys papers: the applied twin literature
ML flagshipsNeurIPS, ICML, ICLRIs a general-ML method quietly identical to yours?
Systems/DBVLDB, SIGMODFor scalability claims: does a database solution already exist?
Applied domainDomain journals, industry reportsFor ADS: is the deployment problem's own literature acknowledged?

Misattribution traps

Getting a citation's venue wrong is disproportionately damaging at KDD because reviewers wrote or reviewed the misplaced papers. Verified traps (see the exemplars library for DOIs): Isolation Forest is ICDM, not KDD; LightGBM is NeurIPS, not KDD despite shadowing XGBoost everywhere; Wide & Deep is a RecSys workshop paper. Check every "seminal KDD paper" claim against the ACM DL record before it ships.

Novelty sentence pattern

The strongest KDD positioning names its neighbors and the mechanism-level difference in one breath:

text
Template:
  Unlike <nearest KDD ancestor>, which <mechanism> and therefore <failure in
  our regime>, and unlike <ML-flagship neighbor>, which <mechanism> but
  <cost/assumption>, our method <new mechanism>, which is what enables
  <the regime-specific capability the paper demonstrates>.

Instantiated:
  Unlike fixed-decay sketches (KDD'xx), whose single decay rate forces a
  forgetting-vs-staleness trade under drift, and unlike window-retrained
  detectors (ICML'yy) whose state grows with window length, StreamHive
  selects decay rates online, which is what allows bounded memory and
  drift tracking simultaneously.

If the sentence cannot be instantiated, the gap is a research problem, not a writing problem — surface it before the deadline, not in rebuttal.

Overlap declarations specific to KDD

  • Cross-cycle resubmission is a formal mechanism, not an overlap problem: declare the previous OpenReview forum id and prepend the change summary (kdd-supplementary). Silently resubmitting a prior cycle's rejected paper as "new" risks the AC discovering the old forum anyway.
  • Research vs ADS: the same underlying system may legitimately produce a methods paper and a deployment paper over time, but the same paper may not go to both tracks in one cycle, and substantial text overlap between two live submissions is a dual-submission problem.
  • arXiv and workshop versions: follow the current CFP's anonymity and prior-publication wording (待核实 per cycle); cite concurrent arXiv work neutrally, state the technical difference, and avoid priority claims reviewers cannot check.
  • Ethics of citation at this venue includes baseline fairness: if you cite a method you also benchmark against, the tuning symmetry disclosure in kdd-reproducibility is part of honest positioning.
Show full SKILL.md (249 more words)Show less

Vignette: positioning an LLM-era mining paper

A 2026-cycle submission uses an LLM to label graph nodes for semi-supervised fraud detection. Its positioning problem is triangular: the graph-mining lane (prior KDD fraud and GNN work) will ask what changed structurally; the LLM lane (recent ML flagship work on LLM annotation) will ask why this is not just prompt engineering; and the practitioner lane will ask about labeling cost at scale. The section that works allocates a paragraph per lane and ends each with a delta sentence:

  • vs. KDD fraud lineage: same task, but label scarcity is attacked at the labeling step rather than the propagation step — cite the nearest KDD ancestor and say what it could not do at 0.1% labels.
  • vs. LLM-annotation work: those pipelines assume i.i.d. text; the contribution here is consistency-checking LLM labels against graph structure — a mechanism, not a prompt.
  • vs. practice: per-node labeling cost quantified against human annotation budgets, which is the sentence the industry reviewer needs.

Triangulated positioning like this also pre-writes the rebuttal: each reviewer lens already has its paragraph (kdd-author-response).

Pre-freeze audit

  1. Nearest KDD ancestor identified, cited, and mechanically contrasted.
  2. Every benchmarked baseline is also positioned in prose (benchmarking without discussion reads as strawmanning).
  3. All venue attributions of "classic" papers spot-checked against ACM DL/DBLP.
  4. Two-lane coverage: at least the mining lane and one adjacent lane (ML, systems, or domain) are represented, matching where the claims live.
  5. Overlap declarations drafted: resubmission id, arXiv status, sibling submissions.

Output format

text
[Positioning] delta-stated / survey-style (rewrite) / lineage-missing
[Nearest ancestors] <KDD predecessor>, <ML neighbor>, <systems neighbor>
[Novelty sentence] <instantiated template or BLOCKED: gap unclear>
[Misattribution check] <papers verified against ACM DL / findings>
[Overlap declarations] <resubmission forum id / arXiv / sibling tracks>

© 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 KDD-Skills/skills/kdd-related-work of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Kdd Related Work 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.

Kdd Related Work compared with similar skills
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Ijcai Related Workfranklee16/academic-research-skills2231 repos~427Automated safety check: PassNone
Literature Reviewneflibata-feng/MyArxiv-Agent12620 repos~5.9kAutomated safety check: NotesMIT
Systematic Literature Review Builderbytedance/deer-flow84k2 repos~4.3kAutomated safety check: PassMIT
Paper Research on arXivXiaomiMiMo/MiMo-Code14k—~1.5kAutomated safety check: PassMIT
Literature Review AgentAr9av/PaperOrchestra6791 repos~5.2kAutomated safety check: PassCustom licence

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Works with

Questions about Kdd Related Work

What does Kdd Related Work do?

A skill your agent uses when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps…. Kdd Related Work is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when positioning a KDD submission against the data-mining lineage (prior KDD volumes, ICDM, SDM, WSDM, CIKM, WWW) and the ML flagships, handling venue misattribution traps, cross-cycle resubmission overlap, concurrent arXiv work, and the mechanism-contrast style of novelty argument that ACM SIGKDD reviewers expect.

When should I use Kdd Related Work?

Kdd Related Work fits situations like: positioning a KDD submission against the data-mining lineage (prior KDD volumes; WWW) and the ML flagships; handling venue misattribution traps; cross-cycle resubmission overlap.

How do I install Kdd Related Work in Claude Code?

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

How do I install Kdd Related Work in Codex?

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

Can I use Kdd Related Work 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 kdd-related-work -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kdd-related-work, .gemini/skills/kdd-related-work, .github/skills/kdd-related-work and .opencode/skills/kdd-related-work in your project.

What does Kdd Related Work need to run?

SKILL.md names no scripts, command-line tools or credentials: Kdd Related Work is instructions for the agent only.

Does Kdd Related Work 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 Kdd Related Work 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 Kdd Related Work use?

Kdd Related Work 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 Kdd Related Work use?

About 1.6k tokens (SKILL.md is roughly 6.6k 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 Kdd Related Work?

Skills that share tags, products or a category with Kdd Related Work: Ijcai Related Work (franklee16/academic-research-skills, 223 stars), Literature Review (neflibata-feng/MyArxiv-Agent, 126 stars), Systematic Literature Review Builder (bytedance/deer-flow, 84k stars) and Paper Research on arXiv (XiaomiMiMo/MiMo-Code, 14k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kdd Related Work?

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.