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Found 75 Skills
Subscribe to AI and tech RSS feeds and persist normalized metadata into SQLite using mature Python tooling (feedparser + sqlite3). Use when adding feed URLs/OPML sources, running incremental sync with deduplication, and storing entry metadata without full-text extraction or summarization.
Fetch journal articles from Crossref published after a user-specified date and insert them into PostgreSQL `journals` with DOI deduplication. Use when incrementally ingesting journal metadata from `journals_issn` into `journals`.
Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
Operate notifications as one ECC-native workflow across GitHub, Linear, desktop alerts, hooks, and connected communication surfaces. Use when the real problem is alert routing, deduplication, escalation, or inbox collapse.
Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.
Integrate multiple plot point analysis results into a comprehensive report, and generate high-quality analysis through deduplication, classification, sorting, and summarization. Suitable for integrating multiple analysis sources and generating unified reports
Reviews, curates, and maintains the Forge library of agents, skills, and templates. Performs deduplication analysis, staleness detection, quality promotion, and orphan reference checking. Produces structured review reports with actionable recommendations for merging, archiving, or promoting library items. Use this skill when the user wants to review the library, clean up agents or skills, check what's available, find duplicates, trim unused items, see library statistics, or says "what's in my library?" Also triggers on scheduled review intervals or when the library grows beyond 20 items. Do NOT use for creating new agents (use Agent Creator), creating skills (use Skill Creator), or planning teams (use Mission Planner).
Implement Nostr client architecture including relay pool management, subscription lifecycle with EOSE/CLOSED handling, event deduplication, optimistic UI for publishing, and reconnection strategies. Use when building Nostr clients, managing WebSocket relay connections, handling subscription state machines, implementing event caches, or debugging relay communication issues like missed events or broken reconnections.
多模态产品图片相似度分析与分组。当用户提到产品图片相似度、视觉分组、查找外观相似的商品、基于图片去重、竞品同款检测、同款商品聚类、按外观分组、image similarity, product image comparison, visual clustering, same-style recognition, appearance deduplication, image grouping时触发此技能。即使用户未明确说"图片相似度"，只要其意图涉及商品主图对比、视觉聚类、识别视觉上相同或相似的商品，或根据外观、颜色、构图等视觉特征对商品列表进行后处理，也应触发此技能。
For users needing to conduct systematic literature reviews, literature reviews, related work, or literature research: AI automatically generates search terms, performs multi-source retrieval → deduplication → AI reads and scores each paper one by one (1–10 points for semantic relevance and sub-topic grouping) → selects papers based on high-score priority ratio → automatically generates word budget for the review (70% cited sections + 30% non-cited sections, average of three samplings) → free writing in the style of senior domain experts (fixed sections: abstract, introduction, sub-topics, discussion, future outlook, conclusion), with strict verification of main text word count and number of references, and mandatory export to PDF and Word. Supports multilingual translation and intelligent compilation (en/zh/ja/de/fr/es).
Next.js performance optimization and best practices. Use when writing Next.js code (App Router or Pages Router); implementing Server Components, Server Actions, or API routes; optimizing RSC serialization, data fetching, or server-side rendering; reviewing Next.js code for performance issues; fixing authentication in Server Actions; or implementing Suspense boundaries, parallel data fetching, or request deduplication.
Verify your own completed code changes using the repo's existing infrastructure and an independent evaluator context. Use after implementing a change when you need to run unit or integration tests, check build or lint gates, prove the real surface works with evidence, and challenge the changed code for clarity, deduplication, and maintainability. If the repo is not verifiable yet, hand off to `agent-readiness`; if you are reviewing someone else's code, use `review`.