Twitter/X Trend Monitor & Thread Publisher
Polls the X API v2 trending topics endpoint every 15 minutes and compares against a keyword watchlist stored in Redis. When a trend matches, fetches top tweets by engagement and drafts a summarizing thread via OpenAI Chat Completions. Publishes the thread via X API v2 with reply chaining, respecting OAuth 2.0 PKCE rate limits.
npx skills add agentskillexchange/skills --skill twitter-x-trend-monitor-thread-publisher
Twitter/X Trend Monitor & Thread Publisher is built around Redis in-memory datastore. The underlying ecosystem is represented by redis/redis (73,523+ GitHub stars). It gives an agent a more technical and reliable way to work with the tool than a thin one-line wrapper, using stable interfaces like keys, hashes, TTLs, streams, pub/sub, sorted sets, locks and preserving the operational context that matters for real tasks.
What this skill actually does
In practice, the skill gives an agent a stable interface to redis so it can inspect state, run the right operation, and produce a result that fits into a larger engineering or operations pipeline. The original use case is clear: Polls the X API v2 trending topics endpoint every 15 minutes and compares against a keyword watchlist stored in Redis. When a trend matches, fetches top tweets by engagement and drafts a summarizing thread via OpenAI Chat Completions. Publishes the thread via X API v2 with reply chaining, respecting OAuth 2.0 PKCE rate limits. The implementation typically relies on keys, hashes, TTLs, streams, pub/sub, sorted sets, locks, with configuration passed through environment variables, connection strings, service tokens, or workspace config depending on the upstream platform.
- Accesses keys, hashes, TTLs, streams, pub/sub, sorted sets, locks instead of scraping a UI, which makes runs easier to audit and retry.
- Supports structured inputs and outputs so another tool, agent, or CI step can consume the result.
- Can be wired into cron jobs, webhook handlers, MCP transports, or local CLI workflows depending on the skill format.
- Fits into broader integration points such as caching, queues, rate limiting, and fast shared state.
Key integration points include caching, queues, rate limiting, and fast shared state. In a real environment that usually means passing credentials through env vars or app config, respecting rate limits and permission scopes, and returning structured artifacts that can be attached to tickets, pull requests, dashboards, or follow-up automations.