skills.rest
futuresearchfuturesearchOfficial·3 Agent Skills Included

everyrow-sdk

Forecasting, dataset research, ranking, and semantic data cleaning

Generates probability, date, and numeric forecasts for questions about the future, with written rationales for every answer. Researches, ranks, classifies, deduplicates, and merges large datasets row by row without manual spreadsheet work. Connects directly to Claude, Cursor, Gemini, and Codex so forecasting and data operations run inside existing workflows.
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npx skills add futuresearch/everyrow-sdk --all -g -y

All Skills in This Repository (3)

Pure Emerald Level Indicators
📦 In Repo
futuresearchfuturesearch

deploy-mcp

Deploy and manage the everyrow MCP server across Kubernetes environments.

Official
Advanced
📦 In Repo
futuresearchfuturesearch

run-mcp-local

Run the everyrow HTTP MCP server locally with Docker Compose and Redis.

Official
Intermediate
📦 In Repo
futuresearchfuturesearch

everyrow-sdk

Orchestrate data researchers via the Everyrow SDK to forecast, score, classify, and merge datasets.

Official
Advanced

Frequently Asked Questions

FAQPage Schema
How to install everyrow-sdk?▼

Run `npx skills add futuresearch/everyrow-sdk --all -g -y` in your terminal to install all skills in this suite globally.

How to get probability forecasts for future events?▼

Pass a table of questions to the forecast operation and receive probabilities, date or numeric percentiles, and a rationale for each row. It supports binary, numeric, date, categorical, thresholded, and conditional questions.

Can it clean and merge messy datasets?▼

Yes. It deduplicates rows by semantic meaning, merges tables using intelligent entity matching, and classifies or ranks rows against your own criteria.

Does it work with Claude Code and Cursor?▼

Yes. It ships as an MCP server and Python SDK that runs in Claude Code, Claude Desktop, Cursor, Gemini CLI, and Codex.

Can I run web research on every row of a spreadsheet?▼

Yes. The agent_map operation runs a research agent on each row of your dataset, with low, medium, and high effort levels to control depth and cost.

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