Skill: leon0401/ai-trending-models (version 1.0.0)

--- name: ai-trending-models description: "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." --- # 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: ```bash 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)