FactIQ

FactIQ - a real-time finance and economy database for AI Agents

Turn your agent into a finance and economy analyst. This plugin for Claude (web, desktop, and Cowork), ChatGPT (web and desktop), Claude Code, and Codex gives the agent direct access to FactIQ's warehouse of official statistics — SEC filings, US, China, India, Korea, IMF, World Bank, and more — plus live market data, earnings-call transcripts, executive media appearances, central-bank speeches, minutes, and statements, and satellite-derived data (fire detections, air-quality activity signals, rainfall, nighttime lights, shipping and port activity, reservoir levels). The agent discovers series, runs read-only SQL, computes derived metrics, and returns a sourced answer, chart JSON, or report JSON.

No codebase or hosted database is required — only a free FactIQ account.

Want to contribute? The highest-leverage addition is a domain playbook that teaches the agent a whole class of questions — see Contributing.

Install

FactIQ supports four client paths. The website guides are canonical for app setup and plan requirements, which can change faster than this repository:

Claude (web, desktop, and Cowork)

FactIQ is listed in the official Claude directory. Open claude.ai/directory/factiq while signed in to Claude, click Connect, complete the FactIQ sign-in, and start a new chat. You can also find it under Customize → Plugins by searching for FactIQ. For the best experience, use Cowork. The plugin requires a paid Claude plan.

To install from this repository instead, open Customize → Plugins, add defog-ai/factiq-plugin as a personal marketplace, install FactIQ, and complete the browser sign-in. Organizations that approve plugins centrally can mirror this repository as a private marketplace. See the Claude guide for the current steps and organization-policy notes.

ChatGPT (web and desktop)

FactIQ is listed in the official ChatGPT plugin directory. Open the FactIQ listing while signed in to ChatGPT, click Install plugin, complete the FactIQ sign-in, and start a new chat. Type @FactIQ to select it explicitly. This works on ChatGPT Plus, Pro, Business, Enterprise, and Edu plans, on the web and in the desktop app. On Business, Enterprise, and Edu, a workspace admin may need to allow FactIQ first. The install also covers Codex inside the ChatGPT app; Codex CLI uses the separate setup below.

To install from this repository instead, open Plugins in the ChatGPT desktop app, add defog-ai/factiq-plugin as a marketplace, and install FactIQ from the Personal tab. This path works only in the desktop app, on a Pro, Business, Enterprise, or Edu plan. See the ChatGPT guide for the current plan matrix and troubleshooting steps.

Claude Code

/plugin marketplace add defog-ai/factiq-plugin/plugin install factiq@factiq/reload-plugins

Run /reload-plugins after installing so Claude Code picks up the new skill and MCP server in the current session (otherwise they only appear the next time you start Claude Code).

This adds the FactIQ skill (Claude invokes it automatically for economic/financial data questions) and the bundled FactIQ MCP server. Claude Code namespaces skills installed from plugins, so its manual invocation is:

Finally, authenticate the MCP server:

1. Run /mcp.
2. Select factiq from the list of servers.
3. Choose Authenticate (or Connect) to open the browser sign-in.
4. Complete the FactIQ login (email, Google, or passkey) and return to Claude Code — the FactIQ tools are now authorized.

Codex

codex plugin marketplace add defog-ai/factiq-plugincodex plugin add factiq@factiq

Then authorize the MCP server:

codex mcp login factiq

Complete the browser sign-in (the same FactIQ login: email, Google, or passkey). Start a new Codex thread after installation; the skill auto-invokes for economic/financial data questions.

codex plugin marketplace upgrade factiqcodex plugin add factiq@factiq

[mcp_servers.factiq]url = "https://api.factiq.com/mcp"

claude mcp add --transport http factiq https://api.factiq.com/mcp

Without a browser: scripts, CI, and headless agents

Every FactIQ tool also works without the browser sign-in. The guide at factiq.com/docs/api explains headless access for scripts, CI, Claude Code, and Codex.

What the plugin sends and runs

• One remote MCP server. The plugin adds https://api.factiq.com/mcp (see .mcp.json), which you sign in to with OAuth. Each tool call sends its arguments (search terms, SQL statements, series IDs, tickers, regions) to that server and returns FactIQ data. FactIQ records connector tool inputs and outputs for reliability and support, as described in the privacy policy.
• One tool that sends a message. send_feedback sends a short report, written by the agent, to the FactIQ team when a FactIQ result looks wrong. It changes no data. Every other tool only reads data.
• Local scripts with no network access. The skill can run the Python scripts in scripts/ (hs_codes.py, trade_sql.py, series_math.py). They read only their arguments, files you pass them, and the bundled files in scripts/data/, and they print their result. They make no network calls.
• Local outputs only. Charts and reports are files written in your own workspace. The plugin does not publish them anywhere.
• The plugin has no hooks and runs nothing when it is installed.

Try it

Once installed and authenticated, ask a question:

/factiq:factiq How has India's trade deficit with China evolved since 2020?

The agent finds the relevant series, runs the SQL, and replies with a sourced answer, chart JSON, or full report, depending on what you ask for. You don't need the slash command: any economic or financial data question in any supported client auto-invokes the skill.

For an earnings question such as "What did Micron management say on its latest call?", the skill checks live transcript coverage first, pins the exact fiscal quarter, and then retrieves bounded management-claim and Q&A-pressure rows for that same call. It keeps spoken remarks separate from formal guidance and SEC filed actuals; the tool does not return a raw transcript.

For a media question such as "How has Jensen Huang discussed export controls outside earnings calls?", the skill checks company-level structured coverage, runs deterministic lexical retrieval with relevance ordering, and uses explicit newest ordering only for a timeline. Results are public-safe paraphrases with timestamped YouTube links, not quotations; the workflow verifies the linked source when exact wording or tone is material. A dedicated playbook also keeps media-vs-earnings comparisons aligned by company, person, topic, and date. The same tool covers documents published by central banks and monetary authorities (the Federal Reserve, the European Central Bank, the Bank of England, the Bank of Japan, the Reserve Bank of India, and others): a question such as "What has the Bank of Japan said about the inflation outlook?" filters by institution or country and reports the meeting or speech date that each document carries.

How it works

Your coding agent is the analyst: it finds the data, does the math, and authors a local output. Data access uses the FactIQ MCP server bundled in .mcp.json over one OAuth connection.

┌─────────────────────────────┐│  Claude / ChatGPT /         ││  Claude Code / Codex        ││  + factiq skill (SKILL.md)  │      the agent orchestrates everything└──────────────┬──────────────┘               │  MCP over HTTP (one OAuth connection)┌──────────────▼──────────────┐│  FactIQ MCP server          ││                             ││  discover   search_datasets, describe_dataset, search_series,│             get_data_catalog│  fetch      run_sql (read-only), get_series, get_market_data,│             search_company_filings, search_earnings_transcripts,│             search_media_appearances, search_news└──────────────┬──────────────┘               │┌──────────────▼──────────────┐│  FactIQ data warehouse      │      official and derived data sources└─────────────────────────────┘

The reason a single skill can query BLS unemployment, Chinese customs flows, RBI monetary data, and World Bank indicators with the same SQL idioms: every data source in the backend is normalized into the same three core tables, identical in every schema:

Our ingestion pipelines do the hard work of flattening each source's bespoke format — BLS flat files, BEA APIs, customs records, RBI releases — into this shape, so the agent learns the model once and it works everywhere. Discovery, pivoting, and filtering follow the same patterns across all ~20 schemas; the recipes live in references/data/sql-guide.md.

What's in the warehouse

references/data/schemas.md has the static overview; the get_data_catalog tool returns the live, authoritative version.

Repo map

Where the behavior lives — the files contributors will touch:

• skills/factiq/SKILL.md — the skill definition and single source of truth for the workflow. Auto-discovered by supported plugin clients from the skills/ directory
• references/data/ — the data layer: SQL idioms (sql-guide.md) and the dataset schema overview (schemas.md)
• references/output/ — local output formats: ChartSpec (chart-spec.md) and report JSON (report-spec.md)
• references/report-patterns/ — domain playbooks (monetary policy, bilateral trade, bilateral economic policy, fiscal-policy revenue, business formation, earnings intelligence, media-appearance intelligence). report-patterns/README.md is the single entry point SKILL.md references: it teaches the dialectical method (thesis → antithesis → synthesis) all reports follow and routes each domain to its playbook, so adding a playbook doesn't touch SKILL.md

Plugin plumbing — you shouldn't need to touch these:

• .mcp.json — declares the bundled FactIQ MCP server (Streamable HTTP, OAuth). Read by both Claude Code and Codex plugin loaders
• .claude-plugin/ — Claude and Claude Code plugin + marketplace manifests
• .codex-plugin/ — ChatGPT and Codex plugin manifest
• .codex-plugin/plugin.json uses the registered FactIQ app ID as its plugin name for ChatGPT app association
• .agents/plugins/marketplace.json — Codex marketplace entry for codex plugin marketplace add defog-ai/factiq-plugin

Contributing

Contributions are welcome — this plugin is meant to grow with its community. Open an issue or a pull request.

Bespoke skills (domain playbooks) — the highest-leverage contribution

The most valuable thing you can add is a domain playbook: a reference file that teaches the agent how to answer a whole class of questions well. A playbook is a domain's dialectic written down in advance — the headline reading a question invites, the contradictions a competent skeptic would raise against it, and the SQL to fetch both (see the method in references/report-patterns/README.md). The existing ones live in references/report-patterns/ and are the pattern to follow:

• monetary-policy.md — central bank policy stance, administered rates, OMO, balance-sheet context
• bilateral-trade.md — country-pair trade trends, product drivers, mirror-statistics caveats
• fiscal-policy-revenue.md — government receipts, tax composition, distributional detail

A good playbook contains:

1. A trigger — which question shapes it covers ("latest trend in trade between A and B", "explain the Fed's stance"), added as a row to the routing table in references/report-patterns/README.md so the agent reads the playbook before fetching. SKILL.md points at that router, so it doesn't need to change.
2. Required coverage — the domain's canonical antitheses: the counter-checks a complete answer must fetch (mirror statistics, real vs nominal, composition, the counterparty's ledger), so the agent doesn't stop at the first obvious chart.
3. Ready SQL templates — tested queries against the three-table schema for the key computations (latest-month YoY, YTD comparisons, top-N drivers).
4. Caveats and guardrails — unit normalization, base-year changes, national-vs-subnational traps, data gaps to disclose explicitly.

Ideas we'd love to see: labor-market health, inflation decomposition, energy markets, housing, sovereign debt, sector earnings analysis, country macro-risk snapshots.

Other welcome contributions

• SQL idioms and pitfalls — additions to references/data/sql-guide.md from real usage.
• Docs and fixes — anything that makes the agent's first attempt land.

Test a playbook by running its questions end to end and checking the saved chart or report output. Include exact before-and-after examples in the PR.

Security

No secrets belong in this repo, and the plugin holds none — interactive access goes through the MCP server's OAuth flow, so the coding agent holds the token and nothing is written here. Headless access is set up outside this repo, as described at factiq.com/docs/api. All SQL runs read-only against FactIQ's data warehouse.

License

MIT