Monitor CI
nrwl/nx
Monitor Nx Cloud CI pipeline and handle self-healing fixes. An agent skill from nrwl/nx.
A skill your agent uses when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to…
Install Kdd Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-topic-selection --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/KDD-Skills/skills/kdd-topic-selection .claude/skills/kdd-topic-selection && 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 "kdd-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-topic-selection into .claude/skills/kdd-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-topic-selection", 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/KDD-Skills/skills/kdd-topic-selectionType 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 kdd-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-topic-selection --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/KDD-Skills/skills/kdd-topic-selection .agents/skills/kdd-topic-selection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "kdd-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-topic-selection into .agents/skills/kdd-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-topic-selection", 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 kdd-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-topic-selection --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/KDD-Skills/skills/kdd-topic-selection .cursor/skills/kdd-topic-selection && 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 "kdd-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-topic-selection into .cursor/skills/kdd-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-topic-selection", 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 KDD-Skills/skills/kdd-topic-selection--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 kdd-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills kdd-topic-selection --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/KDD-Skills/skills/kdd-topic-selection .gemini/skills/kdd-topic-selection && 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 "kdd-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-topic-selection into .gemini/skills/kdd-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-topic-selection", 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 kdd-topic-selectionInstalls 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 kdd-topic-selection -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/KDD-Skills/skills/kdd-topic-selection .github/skills/kdd-topic-selection && 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 "kdd-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-topic-selection into .github/skills/kdd-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-topic-selection", 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 kdd-topic-selection -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 kdd-topic-selection --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/KDD-Skills/skills/kdd-topic-selection .opencode/skills/kdd-topic-selection && 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 "kdd-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/KDD-Skills/skills/kdd-topic-selection into .opencode/skills/kdd-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kdd-topic-selection", 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
kdd-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 764 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 belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to…
- Works in 2 steps: is it KDD-shaped? → the track fork
- Whether it routes to ICDM
- SKILL.md covers Stage 1: is it KDD-shaped?, Stage 2: the track fork, Decision vignette and Neighbor-venue routing, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Kdd Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship. Covers the deployment-evidence fork, data-regime framing, and SIGKDD fit signals before writing.
Its SKILL.md is about 1.7k 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 DevOps & Cloud. 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
- Whether it routes to ICDM
Example prompts
- “/kdd-topic-selection”
Workflow steps
2 steps, taken from the step headings in SKILL.md.
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 Topic Selection loads about 1.7k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 764 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). 764 words, ~1,668 tokens.
Download SKILL.mdSave it as
.claude/skills/kdd-topic-selection/SKILL.md (or your agent's skills folder).- name
- kdd-topic-selection
- description
- Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship. Covers the deployment-evidence fork, data-regime framing, and SIGKDD fit signals before writing.
KDD Topic Selection
Use this before any writing. KDD routing is a two-stage decision — first whether the work is KDD-shaped at all, then which track — and the second stage is the one teams get wrong, because the Research and ADS tracks want different papers written about the same system. A submission may enter exactly one of them per cycle.
Stage 1: is it KDD-shaped?
KDD rewards contributions where the data regime is the protagonist: scale, drift, heterogeneity, noise, sparsity, graph structure, streams, or the friction of real deployment. Signals:
- The method's value claim references a property of data (works at 10^9 edges, under drift, with 0.1% labels) rather than only a property of models.
- There is a mining/discovery/prediction task with measurable output — not purely a learning-theory statement or a pure systems benchmark.
- Someone outside the authors' subfield could use the result on their data.
Anti-signals: novelty lives entirely in architecture or loss design (ML flagships); the contribution is query processing or storage (VLDB/SIGMOD); the core is retrieval ranking on web corpora (SIGIR/WSDM/WWW may fit better, though overlap is real).
Stage 2: the track fork
The decisive question: what is the strongest evidence you can honestly produce?
| Your strongest honest evidence | Track | The bar you must clear |
|---|---|---|
| New mechanism + ablations + scale curves on (mostly) public data | Research | Delta over the mining lineage, mechanism isolated |
| A system running in production with measurable post-launch outcomes | ADS | Quantified post-launch performance — its absence is a desk reject (2026 CFP); lessons learned expected |
| A dataset/benchmark others will build on, with licenses and baselines | Datasets & Benchmarks | Documentation, provenance, maintenance story |
| Mining/ML advancing a natural-science question | AI for Sciences | Scientific validity alongside method quality |
| A provocative direction without full evidence | Blue Sky Ideas | Argument quality; check the track's own CFP |
Two frequent mis-forks:
- Deployed system submitted to Research because the team fears the ADS bar: it then competes on mechanism novelty it may not have, while its actual strength (deployment evidence) earns no credit.
- Research prototype submitted to ADS with projected or offline-only numbers: desk-rejected under the post-launch quantification rule. "We could deploy it" is a Research-track sentence.
- The blocked-from-deployment exception in the ADS CFP is for documented external blockers (regulation, partner constraints), not for "we ran out of time."
Decision vignette
A team built a fraud-detection model, ran it in shadow mode for two months, and has a
new graph-sampling trick inside it. Three honest papers hide here: (a) the sampling
trick with ablations and public-graph scale curves → Research; (b) after full
launch and a measured A/B window → ADS; (c) the labeled transaction graph, if
releasable → Datasets & Benchmarks. The wrong move is one paper claiming all
three, thinly. Pick the paper whose evidence exists this cycle — the dual-cycle
calendar (kdd-workflow) makes "ADS next cycle, after the measurement window" a
concrete plan rather than a consolation.
Show full SKILL.md (296 more words)Show less
Neighbor-venue routing
Route AWAY from KDD when:
- contribution = new learning objective/architecture, data regime incidental
-> NeurIPS / ICML / ICLR
- contribution = mining method, but community/deadline fit favors a sibling
-> ICDM or SDM (same shape, different scale of venue)
- contribution = web/user-behavior/retrieval centric
-> WWW / WSDM / SIGIR / RecSys
- contribution = data management, indexing, query execution
-> VLDB / SIGMOD
- contribution = domain science outcome, method standard
-> domain journal, or KDD's AI-for-Sciences track if the mining is realIf the paper misses at KDD, the reviews tell you which axis failed
(kdd-review-process); a mechanism-novelty objection with solid deployment evidence
often means the right venue was the ADS track all along — a track switch, not a
venue switch.
Fit-signal reference
| Signal in the project | KDD reading |
|---|---|
| Method's win grows with data size or graph density | Core Research fit — scale is the venue's home turf |
| Production system + measurable online outcomes | ADS fit, and only ADS once post-launch numbers exist |
| Contribution is a labeled corpus or benchmark harness | Datasets & Benchmarks, not a Research paper padded with baselines |
| Novel loss/architecture, benchmarks incidental | ML flagship shape; expect "delta over lineage?" pushback at KDD |
| Discovery claim in a natural-science domain | AI for Sciences, or a domain venue if the mining is off-the-shelf |
| Query/index/storage performance is the headline | VLDB/SIGMOD shape |
| The paper's tables would not change if the data were 100x smaller | The scale story is decorative — fix that before routing anywhere |
Timing interacts with routing
Because KDD runs two cycles, track choice is also schedule choice: a Research paper
can target the nearest cycle, while the ADS version of the same project may need to
wait for its measurement window to close (kdd-workflow). When co-authors disagree
on track, the tiebreaker question is: which paper's gate evidence exists today?
Projected evidence loses to existing evidence at this venue every time — the ADS
desk-check makes that literal.
Commit checklist before writing
- One sentence naming the data regime and why it defeats existing methods.
- Track chosen from the evidence table above, and the track's specific bar quoted from the current CFP (track CFPs are separate documents — read the right one).
- The nearest KDD-lineage ancestor identified (
kdd-related-work). - The cycle chosen with evidence lead times honored (
kdd-workflow).
Output format
[KDD-shaped] yes / no -> <redirect venue>
[Track] Research / ADS / Datasets+Benchmarks / AI4Science / BlueSky
[Evidence basis] <the strongest honest evidence available this cycle>
[Desk-check exposure] ADS post-launch quantification: satisfied / fails / N-A
[Regime sentence] <one sentence>
[Next action] <write / measure / wait a cycle / re-route>© 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-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Kdd 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Kdd Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Monitor CInrwl/nx | 29k | 6 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Terraform and OpenTofu Guideagentscope-ai/QwenPaw | 35k | 6 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 9 repos | ~4.3k | Automated safety check: Pass | None | |
| Openclaw Live Updateropenclaw/openclaw | 392k | — | ~3.7k | Automated safety check: Pass | MIT | |
| Analyze GitHub Action Logswithastro/astro | 63k | 1 repos | ~1.3k | Automated safety check: Pass | Custom licence |
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Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
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Questions about Kdd Topic Selection
What does Kdd Topic Selection do?
A skill your agent uses when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to…. Kdd Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship.
When should I use Kdd Topic Selection?
Kdd Topic Selection fits situations like: whether it routes to ICDM.
How do I install Kdd Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-topic-selection -a claude-code`. Or copy the skill folder (KDD-Skills/skills/kdd-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/kdd-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Kdd Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-topic-selection -a codex`. Or copy the skill folder (KDD-Skills/skills/kdd-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/kdd-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Kdd 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 kdd-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/kdd-topic-selection, .gemini/skills/kdd-topic-selection, .github/skills/kdd-topic-selection and .opencode/skills/kdd-topic-selection in your project.
What does Kdd Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Kdd Topic Selection is instructions for the agent only.
Does Kdd 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 Kdd 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 Kdd Topic Selection use?
Kdd 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 Kdd Topic Selection use?
About 1.7k tokens (SKILL.md is roughly 6.7k 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 Topic Selection?
Skills that share tags, products or a category with Kdd Topic Selection: Monitor CI (nrwl/nx, 29k stars), Terraform and OpenTofu Guide (agentscope-ai/QwenPaw, 35k stars), Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars) and Openclaw Live Updater (openclaw/openclaw, 392k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Kdd Topic Selection?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.