Brave Search
badlogic/pi-skills
Web search and content extraction via Brave Search API. An agent skill from badlogic/pi-skills.
A skill your agent uses when deciding whether a project belongs at WSDM or a neighbor - tests for the web/social-data core and the practical-yet-principled bar, scope coverage from web search and…
Install Wsdm Topic Selection
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-topic-selection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-topic-selection .claude/skills/wsdm-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 "wsdm-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-topic-selection into .claude/skills/wsdm-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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/WSDM-Skills/skills/wsdm-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 wsdm-topic-selection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-topic-selection .agents/skills/wsdm-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 "wsdm-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-topic-selection into .agents/skills/wsdm-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-topic-selection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-topic-selection .cursor/skills/wsdm-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 "wsdm-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-topic-selection into .cursor/skills/wsdm-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 WSDM-Skills/skills/wsdm-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 wsdm-topic-selection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wsdm-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/WSDM-Skills/skills/wsdm-topic-selection .gemini/skills/wsdm-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 "wsdm-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-topic-selection into .gemini/skills/wsdm-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-topic-selection .github/skills/wsdm-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 "wsdm-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-topic-selection into .github/skills/wsdm-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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 wsdm-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 wsdm-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/WSDM-Skills/skills/wsdm-topic-selection .opencode/skills/wsdm-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 "wsdm-topic-selection" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WSDM-Skills/skills/wsdm-topic-selection into .opencode/skills/wsdm-topic-selection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wsdm-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
wsdm-topic-selection- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 758 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 WSDM or a neighbor - tests for the web/social-data core and the practical-yet-principled bar, scope coverage from web search and…
- Deciding whether a project belongs at WSDM
- SKILL.md covers The two-gate test, Scope coverage check (2026 CFP…, Routing table and Selectivity realism, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- A neighbor - tests for the web/social-data core and the practical-yet-principled bar
What it does
Wsdm Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at WSDM or a neighbor - tests for the web/social-data core and the practical-yet-principled bar, scope coverage from web search and recommendation to social networks and responsible web AI, routing against SIGIR, KDD, WWW, CIKM, RecSys, and ICWSM, and long-versus-short-track fit.
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 Productivity & Automation, covering Web search. 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 belongs at WSDM
- A neighbor - tests for the web/social-data core and the practical-yet-principled bar
- Scope coverage from web search and recommendation to social networks and responsible web AI
- Routing against SIGIR
Example prompts
- “/wsdm-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
Wsdm Topic Selection loads about 1.7k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 758 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). 758 words, ~1,694 tokens.
Download SKILL.mdSave it as
.claude/skills/wsdm-topic-selection/SKILL.md (or your agent's skills folder).- name
- wsdm-topic-selection
- description
- Use when deciding whether a project belongs at WSDM or a neighbor - tests for the web/social-data core and the practical-yet-principled bar, scope coverage from web search and recommendation to social networks and responsible web AI, routing against SIGIR, KDD, WWW, CIKM, RecSys, and ICWSM, and long-versus-short-track fit.
WSDM Topic Selection
Decide if a project is WSDM-shaped before anyone formats a page. WSDM (pronounced "wisdom") is deliberately narrow: search and data mining on the Web and the Social Web, run as a small, highly selective, traditionally single-track winter meeting jointly sponsored by four ACM SIGs (SIGIR, SIGKDD, SIGMOD, SIGWEB). The sponsorship list is the scope diagram - the venue lives at the intersection of retrieval, mining, data management, and the web itself.
The two-gate test
Gate 1 - the data gate. Is the primary object of study web or social-web data: queries and clicks, documents and links, user-item interactions, social graphs, ads, reviews, conversational sessions? A method paper whose experiments merely include a web dataset fails this gate; the web data must be what the contribution is about. Tabular-ML, vision, and generic NLP work fail here regardless of quality.
Gate 2 - the "practical yet principled" gate. The series describes its emphasis as practical yet principled approaches, and the PC enforces both adjectives:
- Practical: plausible at platform scale, aware of serving cost, evaluated on realistic interaction data.
- Principled: a nameable mechanism, bias-aware evaluation, and an explanation of why it works - not a leaderboard delta.
Projects strong on one adjective route elsewhere: principled-only theory toward theory-friendly venues, practical-only system reports toward industry tracks or applied venues.
Scope coverage check (2026 CFP areas)
The 2026 call organized scope roughly as: web search (including query analysis, evaluation, user behavior and log analysis, and explicitly "Search with Foundation Models"); web mining and content analysis (including recommender systems, crawling/indexing); social networks (link prediction, community detection, computational social science, influence, trust); fairness, accountability, and explainability for ranking, recommendation, and ads; and conversational search and assistants. If the project needs a paragraph of throat-clearing to sound like one of these, note that as a fit warning.
Routing table
| Signal in your project | Better first target | Why |
|---|---|---|
| Core IR theory, test-collection evaluation, no web-mining angle | SIGIR | Deeper IR-methods bench |
| General-purpose mining/ML method, web data incidental | KDD | Scope is data mining at large |
| Web systems, standards, platform measurement, web economics | TheWebConf (WWW) | Broader web-as-artifact scope |
| Solid applied IR/DB/mining result, breadth over selectivity | CIKM | Larger, broader program |
| Recommender-systems contribution with user-centric evaluation | RecSys | Dedicated community and review lens |
| Social-media phenomena, computational social science first | ICWSM | Social-science evaluation standards |
| Learning theory / new architecture, evaluation on static benchmarks | NeurIPS/ICML/ICLR | Method-first review culture |
| Web search/mining/rec with logs, bias-awareness, deployment realism | WSDM | This is the lane |
Tie-breakers when two venues survive: (1) whose recent proceedings contain the
papers you must cite - submit to the ongoing conversation; (2) whose review
process suits the work - WSDM's no-rebuttal, one-in-six regime punishes papers
that need explaining; (3) calendar position (wsdm-workflow maps the chain).
Show full SKILL.md (314 more words)Show less
Selectivity realism
Anchor numbers: WSDM 2025 accepted just over 100 of more than 600 submissions (~16-17%). Single-track capacity keeps the program small by design. Honest self-assessment questions before committing the August slot:
1. Name the WSDM lineage this joins (see wsdm-related-work). [____]
2. State the behavioral fact the paper exploits or corrects. [____]
3. State the mechanism in one clause, no "framework" words. [____]
4. Which quadrant of evidence is weakest (wsdm-experiments)? [____]
5. Would the industry half of the PC call the setting realistic? [____]
6. Is there a reason to *choose* this paper, not just no flaw? [____]Blank boxes at question 1-3 usually mean the project is a neighbor-venue paper wearing WSDM formatting. A weak answer at 6 with strong answers elsewhere suggests the short-paper track (since 2026) over the long track.
Routing vignettes (fictional)
- A contrastive-learning objective evaluated on MovieLens and two vision benchmarks. Fails Gate 1 - web data is a test case, not the object. Route to the ML flagships; return to WSDM only if a version emerges whose claims are about interaction data specifically.
- A measurement study of coordinated inauthentic amplification on a microblog platform, with a detection heuristic. Passes Gate 1; Gate 2 depends on the mechanism. If detection is principled and evaluated for ranking/mining impact, WSDM fits; if the contribution is the social phenomenon itself, ICWSM's review culture will value it higher.
- An LLM-based relevance judge replacing crowd labels, validated against human judgments on public query sets. Passes both gates via the evaluation lineage; equally at home at SIGIR - apply tie-breaker (1): wherever the papers it must cite appeared last two years.
- A vector-database sharding scheme with a web-corpus benchmark. The contribution is data management; the web corpus is incidental. VLDB-family first, WSDM only with a retrieval-behavior angle.
Foundation-model-era fit
LLM work is in scope only through the web lens - the 2026 CFP names "Search with Foundation Models" and the 2026 Industry Day theme was LLMs and agentic AI in industrial settings. The test: does the contribution concern how foundation models interact with web-scale search, recommendation, or user behavior (retrieval-augmentation for search, LLM-based ranking or judgment, agentic browsing, synthetic-content effects on ranking ecosystems)? A prompt technique evaluated on static QA benchmarks fails Gate 1 regardless of the word "search" in its title.
Output format
[Gate 1] web/social data is the object of study: pass / fail (reason)
[Gate 2] practical + principled both present: pass / weak adjective named
[Scope area] 2026-CFP area matched: <area or none>
[Routing] WSDM / <neighbor> with tie-breaker rationale
[Track] long / short / demo / cup / defer a year
[Confidence] choose-this-paper reason in one sentence© 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 WSDM-Skills/skills/wsdm-topic-selection of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wsdm 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 |
|---|---|---|---|---|---|---|
| Wsdm Topic Selection this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Brave Searchbadlogic/pi-skills | 2.6k | 5 repos | ~592 | Automated safety check: Pass | MIT | |
| Enterprise AI Scenario MapMetaInFLow/Enterprise-ai-scenario-map-skill | 632 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Web Searchjjyaoao/HelloAgents | 3.2k | 1 repos | ~5.6k | Automated safety check: Pass | MIT | |
| Ddg SearchTheSyart/claude-agent-examples | 405 | 1 repos | ~493 | Automated safety check: Pass | None | |
| Local Web SearchuluckyXH/OpenMOSS | 1.3k | — | ~392 | Automated safety check: Notes | MIT |
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Related
Categories
Questions about Wsdm Topic Selection
What does Wsdm Topic Selection do?
A skill your agent uses when deciding whether a project belongs at WSDM or a neighbor - tests for the web/social-data core and the practical-yet-principled bar, scope coverage from web search and…. Wsdm Topic Selection is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when deciding whether a project belongs at WSDM or a neighbor - tests for the web/social-data core and the practical-yet-principled bar, scope coverage from web search and recommendation to social networks and responsible web AI, routing against SIGIR, KDD, WWW, CIKM, RecSys, and ICWSM, and long-versus-short-track fit.
When should I use Wsdm Topic Selection?
Wsdm Topic Selection fits situations like: deciding whether a project belongs at WSDM; A neighbor - tests for the web/social-data core and the practical-yet-principled bar; scope coverage from web search and recommendation to social networks and responsible web AI; routing against SIGIR.
How do I install Wsdm Topic Selection in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-topic-selection -a claude-code`. Or copy the skill folder (WSDM-Skills/skills/wsdm-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wsdm-topic-selection in your project. Claude Code loads it when a task matches its description.
How do I install Wsdm Topic Selection in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wsdm-topic-selection -a codex`. Or copy the skill folder (WSDM-Skills/skills/wsdm-topic-selection in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wsdm-topic-selection in your project. Codex loads it when a task matches its description.
Can I use Wsdm 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 wsdm-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/wsdm-topic-selection, .gemini/skills/wsdm-topic-selection, .github/skills/wsdm-topic-selection and .opencode/skills/wsdm-topic-selection in your project.
What does Wsdm Topic Selection need to run?
SKILL.md names no scripts, command-line tools or credentials: Wsdm Topic Selection is instructions for the agent only.
Does Wsdm 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 Wsdm 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 Wsdm Topic Selection use?
Wsdm 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 Wsdm Topic Selection use?
About 1.7k tokens (SKILL.md is roughly 6.8k 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 Wsdm Topic Selection?
Skills that share tags, products or a category with Wsdm Topic Selection: Brave Search (badlogic/pi-skills, 2.6k stars), Enterprise AI Scenario Map (MetaInFLow/Enterprise-ai-scenario-map-skill, 632 stars), Web Search (jjyaoao/HelloAgents, 3.2k stars) and Ddg Search (TheSyart/claude-agent-examples, 405 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Wsdm 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.