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查看技能说明Email Newsletter RSS
alaskasquirrel
OPENAGENTSKILL / DIRECTORY
为下一项任务找到合适的技能。探索适用于 Codex、Claude Code、Cursor 等 Agent 的工具。
21 Skills
搜索结果: 21
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查看技能说明alaskasquirrel
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查看技能说明davidesantangelo
The best RSS Search experience you can find
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查看技能说明html2rss
📰 Build RSS 2.0 feeds from websites (and JSON APIs) automatically or with a few CSS selectors.
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查看技能说明l0ng-ai
Read, search and triage the user's Papr RSS subscriptions from the shell via the `papr` CLI. Use when the user wants to catch up on their feeds, find or summari
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查看技能说明dhvcc
Parse RSS 2.0/0.9x, Atom 1.0, RSS 1.0 (RDF) and podcast (itunes:*) feeds in Python into typed pydantic v2 models with the `rss-parser` package. Use this skill whenever a…
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查看技能说明kevinho
AI-powered news digest that curates thousands of sources down to the highlights that matter. Generates structured summaries (4H/daily/weekly/monthly) from Twitter, RSS,…
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查看技能说明Seanium
😋 AI-powered, Lightweight RSS Reader. Supports: GitHub Pages | Vercel | Alibaba Cloud ESA Pages | Docker
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查看技能说明gautamkrishnar
Show your latest blog posts from any sources or StackOverflow activity or Youtube Videos on your GitHub profile/project readme automatically using the RSS feed
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查看技能说明osmoscraft
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查看技能说明Track YouTube channels for new uploads. Supports both RSS mode (no API key needed, unlimited quota) and API mode. Use when: add/remove/list tracked YouTube channels, che…
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查看技能说明finaldie
A personal news aggregator to pull information from multi-sources + LLM (ChatGPT/Gemini/Ollama via LangChain) to help us reading efficiently with less noises, the source…
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查看技能说明eikowagenknecht
RSS feeds, Discord and Telegram bot for free game and loot offers. Supports Steam, Epic, Amazon Games, GOG, Humble and more.
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查看技能说明xiaoxiaoxh
[RSS 2025] Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation
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查看技能说明dougsm
Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)
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Official PyTorch Implementation of Unified Video Action Model (RSS 2025)
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[RSS 2019] End-to-End Robotic Reinforcement Learning without Reward Engineering