ClawHub

AI Trending Radar

Dev Tools

This skill should be used when the user wants to discover, collect, or summarize the latest trending and viral open-source AI or LLM projects. Triggers include: 收集最新爆火AI开源项目, 最近有哪些热门大模型, 给我找前沿AI开源项目, what are the trending AI repos, latest open-source LLM projects, or any variation asking for hot trending new AI model releases and repositories.

Install

openclaw skills install ai-trending-models

AI Trending Models Skill

Purpose

Systematically collect and summarize the latest, most viral open-source AI / large-language-model (LLM) projects from authoritative sources, and present a structured, actionable intelligence report to the user.

Trigger Conditions

Load this skill whenever the user asks to:

  • Find / collect / scout the latest trending AI open-source projects
  • Summarize recent hot LLM / multimodal / agent frameworks
  • Track what's blowing up on GitHub, HuggingFace, arXiv, or AI news outlets

Workflow

Step 1 — Run the automated fetcher (preferred)

Execute scripts/fetch_trending.py to pull live data from multiple sources:

python3 scripts/fetch_trending.py

The script outputs a structured JSON file (trending_report.json) with raw data. Read and interpret that file for the final report.

If the script cannot run (network issues, missing deps), fall back to Step 2.

Step 2 — Manual web research fallback

Query each source listed in references/sources.md using web_fetch or web_search. Collect at minimum:

  • GitHub Trending (past 7 days, filter: AI / ML / LLM)
  • HuggingFace Models — trending tab
  • arXiv cs.AI / cs.CL — last 7 days, sorted by submission count
  • Papers With Code — trending methods
  • Twitter / X — #OpenSourceAI, #LLM hashtags top posts

Step 3 — Deduplicate & rank

Rank projects by composite signal:

  1. GitHub stars velocity (stars gained / days since release)
  2. Cross-source mention frequency (appears in GitHub + HuggingFace + arXiv = higher rank)
  3. Recency (prefer projects released or updated within 30 days)
  4. Community buzz (forks, issues, PR activity, social mentions)

Step 4 — Produce the report

Output a clean Markdown report following the template in references/report_template.md.

Key sections:

  • 执行摘要 / Executive Summary — 3-sentence overview of what's hot right now
  • TOP 10 爆火项目 — ranked table with: rank, project name, org/author, stars ⭐, stars delta Δ, category, one-line description, link
  • 按方向分类 — group projects by: LLM底座 | 多模态 | Agent/工具链 | 推理加速 | 数据/微调 | 其他
  • 值得关注的论文 — top 5 arXiv papers linked to open-source code
  • 趋势洞察 — bullet-point analysis of what the data signals for the industry

Output Standards

  • Language: match the user's language (default Chinese 中文)
  • Format: Markdown with emoji for visual clarity
  • Stars count: use K notation (e.g. 12.4K)
  • Always include direct URLs to GitHub repos / HuggingFace model pages
  • Date-stamp the report header with the collection date
  • If data is older than 3 days, note it clearly

Quality Rules

  • Minimum 10 projects in the main table; aim for 15–20
  • No duplicates across GitHub / HuggingFace entries for the same project
  • Verify each project is genuinely open-source (has an open license)
  • Flag projects that are "demo-only" or have no released weights
  • Prioritize projects with working code over paper-only releases
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