This is a mature, actively maintained AI agent skill called 'last30days' that enables agents (Claude Code, Codex, Grok, Gemini CLI, MCP hosts) to research any topic by aggregating content from 20+ social and news platforms (Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, arXiv, Techmeme, Digg, and more) over a rolling 30-day window. The system is primarily a CLI engine (Python) with a companion Go MCP server for Claude Desktop. It employs a sophisticated pipeline: query planning by intent, parallel retrieval from multiple backends (keyless APIs, paid scrapers, CLI tools, LLM-based search), source-specific normalization, engagement-weighted relevance scoring, cross-source clustering, and deterministic fusion (weighted RRF) with per-author caps and diversity floors. Output is rendered as structured JSON, compact text, or self-contained HTML with optional publishing and Atom feeds. The codebase emphasizes security (credential hygiene, no browser cookie reads in unattended mode), determinism (fixture-based evaluation harness with grounding/recency/coherence scores), and graceful degradation (per-source fallback chains, never-raise error envelopes). While classified as an application (it's a skill/plugin for agent hosts), it exhibits strong data engineering traits: source adapters, normalization pipelines, scoring models, and an offline evaluation framework.
This repository is a curated directory/registry of 5,300+ community-built skills for OpenClaw, an AI agent platform. It is not a software application — it contains no runtime code, no API, and no executable logic. Instead, it is a structured catalog of markdown files organized into 30+ categories (DevOps, Search & Research, Coding Agents, etc.), where each entry links to a skill hosted on ClawHub (OpenClaw's public skills registry). The repo functions as a discovery and navigation layer: users browse categories to find skills, and contributors submit new skills via pull requests validated by a CI workflow. The repository also documents filtering criteria (excluding spam, duplicates, low-quality, crypto, and malicious entries), security considerations (VirusTotal scanning partnership), and contribution guidelines. While it shares some characteristics with a data_research repo (structured metadata, no application code), it is best classified as a curated content registry — the 'application' label applies only in the sense that it is not a data pipeline or research project. Key patterns include a consistent category taxonomy, a PR validation workflow, and documented quality gates. The main risks are link rot (5,300+ external links with no automated checking), quality variance across entries, and the manual effort required to maintain a list at this scale.