GitHub - defog-ai/factiq-plugin: Real-time economy+finance database for AI agents

Turn your agent into a finance and economy analyst. This plugin for 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 and earnings-call intelligence. The agent discovers series, runs read-only SQL on FactIQ's database, computes derived metrics, and publishes the result as a shareable FactIQ chart or report, a terminal preview, or a bespoke local HTML visualization.

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

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, MCP server, and command in the current session (otherwise they only appear the next time you start Claude Code).

This adds the skill (Claude invokes it automatically for economic/financial data questions), the bundled FactIQ MCP server, and the /factiq:ask command:

Command	Purpose
`/factiq:ask <question>`	Run a full analysis and get a shareable chart, terminal chart, or report

Finally, authenticate the MCP server:

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

Enable auto-updates

So you always get the latest skill, MCP tools, and commands without reinstalling, turn on auto-updates for the marketplace:

Run /plugin.
Select Marketplaces.
Select factiq.
Toggle auto-updates on.

Claude Code will then refresh the plugin automatically whenever this marketplace changes.

Codex

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

Then authorize the MCP server:

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.

Update an existing Codex install

To update after this marketplace changes, refresh the configured marketplace name (factiq), then reinstall the plugin:

codex plugin marketplace upgrade factiq
codex plugin add factiq@factiq

Alternative: install as a standalone MCP server (no plugin)

Add the MCP server directly to your Codex config (~/.codex/config.toml):

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

Then codex mcp login factiq. The skill won't auto-invoke without the plugin, but the MCP tools are available for manual use.

For Claude Code without the plugin:

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

Then authorize with /mcp.

Try it

Once installed and authenticated, ask a question:

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

The agent finds the relevant series, runs the SQL, and replies with a shareable factiq.com chart link — or a terminal chart or full report, depending on what you ask for. You don't need the slash command: any economic or financial data question in a normal Claude Code or Codex conversation auto-invokes the skill.

How it works

Your coding agent is the analyst: it decomposes the question, finds the data, does the math, authors the output, and publishes it — all through tool calls to the FactIQ MCP server (bundled in .mcp.json), which Claude Code and Codex talk to natively over a single OAuth connection.

┌─────────────────────────────┐
│  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_earnings
│  publish    share_chart, share_report
└──────────────┬──────────────┘
               │
┌──────────────▼──────────────┐
│  FactIQ data warehouse      │      ~20 official sources, one schema
│  + factiq.com share pages   │      published charts/reports render here
└─────────────────────────────┘

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:

Table	What it holds
`series`	The catalog — one row per series: id, title, description, dataset, frequency, units, seasonality, geography, time coverage
`data_points`	The values — `(series_id, time, value)`, indexed for fast retrieval
`dimensions`	Faceted metadata — `(series_id, dimension_type, dimension_code, dimension_name)`, e.g. `partner`, `flow`, `commodity`, `hs_level` for trade data

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

Region	Schemas
United States	SEC filings data, BLS (employment, CPI, JOLTS, OEWS), Census (trade incl. HS-level, retail, housing), BEA (GDP, income), EIA (energy), USDA ERS, BTS (transportation), earnings-call intelligence
China	NBS macro indicators, GACC customs (HS-level trade)
India	MOSPI (CPI, WPI, IIP, GDP), RBI (banking, rates, forex), DGCI&S trade (HS-level), city traffic
South Korea	KCS customs (HS-level trade)
Global	IMF, World Bank, Singapore SingStat, live market data (quotes, fundamentals, FX, commodities)

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 both Claude Code and Codex from the skills/ directory
references/data/ — the data layer: SQL idioms (sql-guide.md) and the dataset schema overview (schemas.md)
references/output/ — publishing formats: ChartSpec (chart-spec.md), report JSON (report-spec.md), and the bespoke-viz guide (viz-guide.md)
references/report-patterns/ — domain playbooks (monetary policy, bilateral trade, bilateral economic policy, fiscal-policy revenue, business formation). 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
commands/ask.md — the /factiq:ask slash command (Claude Code)
scripts/term_chart.py — stdlib-only renderer that prints ANSI/ASCII terminal previews from FactIQ ChartSpec JSON and share_report report objects. It supports bar, simple line, and table fallback renderers
scripts/build_viz.py — local-only tool that assembles fetched data into a self-contained HTML viz and screenshots it headless for iteration; usage in references/output/viz-guide.md
assets/viz-shell.html — starting-point shell for bespoke visualizations

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 Code plugin + marketplace manifests
.codex-plugin/ — Codex plugin manifest
.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:

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.
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.
Ready SQL templates — tested queries against the three-table schema for the key computations (latest-month YoY, YTD comparisons, top-N drivers).
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

Terminal renderers — new chart types or better ASCII/ANSI output in scripts/term_chart.py (keep it stdlib-only).
Viz recipes — reusable patterns for build_viz.py and references/output/viz-guide.md.
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 the questions it targets end-to-end through the skill and checking the published output; a PR description that shows a before/after share link is the most convincing review material.

Security

No secrets belong in this repo, and the plugin holds none — all access goes through the MCP server's OAuth flow, so the coding agent holds the token and nothing is written here. All SQL runs read-only against FactIQ's data warehouse.

License

MIT