autosearch:recent-signal-fusion

Name: autosearch:recent-signal-fusion
Availability: InStock
Author: Mario

Fuse recent signals from multiple channels into a time-weighted candidate list.

40|6|Updated Mar 26, 2026

Search Tags:#synthesis #ranking #clustering #time-window #cross-channel #recency #signal-fusion

Ready to run

npx skills add https://github.com/0xmariowu/Autosearch --skill autosearch-recent-signal-fusion

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: autosearch:recent-signal-fusion
Source: https://github.com/0xmariowu/Autosearch/tree/main/autosearch/skills/meta/recent-signal-fusion
Command: npx skills add https://github.com/0xmariowu/Autosearch --skill autosearch-recent-signal-fusion

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Unifies fragmented, time-sensitive signals from diverse sources into a single, time-weighted recency bundle to support rapid awareness and decision-making.

Core Features & Use Cases

Cross-channel recency fusion: aggregates signals from Reddit, X, Hacker News, Weibo, YouTube, GitHub activity, and other channels into one ranked set.
Semantic clustering & ranking: clusters near-duplicates across platforms and scores top clusters by recency, platform reliability, and engagement signals.
Use Case: generates daily or 24-hour digests to inform a runtime AI synthesis workflow.

Quick Start

Provide a 24-hour recency bundle for a topic by aggregating signals across configured channels and returning the top clusters.

Frequently Asked Questions about autosearch:recent-signal-fusion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I consolidate recent signals from multiple platforms into a single ranked list?▼

Cross-channel recency fusion aggregates fragmented signals from platforms like Reddit, X, Hacker News, Weibo, YouTube, and GitHub into a single time-weighted recency bundle. It applies semantic clustering to group near-duplicates and scores them by recency, platform reliability, and engagement.

What is the best way to generate a 24-hour trend digest across social media and code repositories?▼

Generating a 24-hour trend digest requires fusing recent activity from platforms like Reddit, X, Hacker News, Weibo, YouTube, and GitHub into a time-weighted recency bundle. The output provides top semantic clusters, time bounds, and platform coverage to support rapid decision-making.

Can I cluster near-duplicate discussions across Reddit, X, and Hacker News?▼

Yes, semantic clustering groups near-duplicate signals across platforms like Reddit, X, and Hacker News into unified clusters. These clusters are then scored by recency, platform reliability, and engagement signals to produce a ranked candidate list.

Does cross-platform signal fusion require specific dependencies or components?▼

No, cross-channel recency fusion operates with no listed dependencies or components. You simply provide signals from your configured channels like Weibo and YouTube, and it outputs a structured bundle with top clusters, time bounds, and platform coverage.

When do I need time-weighted signal fusion for trend monitoring?▼

You need time-weighted signal fusion when unifying fragmented, time-sensitive signals from diverse sources into a single recency bundle for rapid awareness. It is applied to trend monitoring, 24-hour watches, and weekly digests across platforms like Reddit, X, and GitHub.

How are cross-platform signals scored and ranked in a recency bundle?▼

Cross-platform signals are scored by recency, platform reliability, and engagement signals within a time-weighted recency bundle. Semantic clustering groups near-duplicates across platforms, and the resulting clusters are ranked to output a structured bundle with top clusters and time bounds.