last-30-days-in-markets

name	last-30-days-in-markets
description	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. 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". Read-only. No trading, no purchases, no write operations, no wallet access.
homepage	https://sentisense.ai
requires	{"env":["SENTISENSE_API_KEY"]}
primaryEnv	SENTISENSE_API_KEY
metadata	{"openclaw":{"requires":{"env":"[Truncated]"},"primaryEnv":"SENTISENSE_API_KEY","envVars":["[Truncated]"]}}

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.

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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 dozen or so API calls, and it will tell the reader when the data does not reach, which parts of the month are thin, and what it could not cover. In exchange the reader gets something auditable: dated events ranked by a real impact score, a numeric mood series they can plot, and an explicit coverage line. Use it when the answer matters enough to be checked, and reach for the fast summary when it does not.

The material it works from is unusual, and worth understanding before writing anything. This API returns no publisher headlines and no article text. It returns story clusters: groups of related coverage clustered and titled by SentiSense's own models, each carrying an impact score, an aggregate sentiment, and the tickers involved, alongside real numeric series for the market's mood.

That constraint is also the product. A recap built from clusters tells you which themes dominated a month and how much they mattered, which is what a person actually wants after three weeks away. A list of headlines is available anywhere.

So the standard throughout is simple: The rules below are what that standard means in practice.