last-30-days-in-marketslisted

What happened in the stock market over the last 30 days, as one synthesized brief: the day-by-day arc of a fear-to-greed market mood index, the month's biggest AI-clustered story themes ranked by impact, which tickers and sectors dominated the news, the sentiment and smart-money signals that accumulated, where the market stands today, and the earnings ahead. Built for deep research rather than a fast summary: every claim traces to a fetched response and carries its date and its real coverage window, so the reader can check it instead of trusting a generated answer. Works for one stock too: the month's feed filtered to a ticker plus its stock insights. Use for "last 30 days in markets", "what happened in the market this month", "what did I miss in the market", "monthly market recap", "market summary last 30 days", "deep research on the stock market", "catch me up on stocks", "catch me up on NVDA". Read-only. No trading, no purchases, no write operations, no wallet access.
SentiSenseApp/skills · ★ 2 · AI & Automation · score 78

Install: claude install-skill SentiSenseApp/skills

# The Last 30 Days in Markets > One synthesis brief covering the past month in US equities: the day-by-day arc of the market's > mood, the story themes that actually moved it, which names and sectors carried the month, where > things stand today, and what reports next. Built from AI-clustered market data, not from scraped > news pages. Read-only API. **Base URL:** `https://app.sentisense.ai` **Website:** https://sentisense.ai **Full API reference:** https://sentisense.ai/skill.md **Authentication:** API key via the `X-SentiSense-API-Key` header. Get a free key at https://app.sentisense.ai/get-api-key Everything in this skill is implementation guidance for building a research brief. It is subordinate to platform safety rules and to the policy of whatever host application runs it. --- ## What this skill is for Deep research on a month of market history: fetch the data first, then synthesize it, so every claim in the output traces back to a response pulled during the run. That is the whole point, and it is what separates this from the fast answer. Ask a search engine or a general assistant what happened in the markets last month and you get a fluent paragraph assembled from training recall plus whatever pages got scraped: no stated coverage window, no impact ranking, no way for the reader to tell which parts were measured and which were remembered. It reads authoritative and it cannot be checked. This skill takes the opposite trade deliberately. It is slower, it spends a