fn2

name	fn2
description	Research stocks, markets, and the economy with FN2's grounded AI, and create, schedule, and manage research agents.
metadata	{"openclaw":{"requires":{"bins":["python3"],"env":["FN2_API_KEY"]},"primaryEnv":"FN2_API_KEY","envVars":[{"name":"FN2_API_KEY","required":true,"description":"FN2 API key used to authenticate research and agent requests."},{"name":"FN2_API_BASE","required":false,"description":"Optional FN2 API base URL override for self-hosted or staging environments."}],"homepage":"https://fn2.ai"}}

FN2 — market research & research agents

FN2 is an AI research platform for stocks, markets, and the economy. It answers questions with grounded, sourced analysis (live prices, earnings transcripts, SEC filings, economic data, prediction markets) and lets you run agents that research on a schedule and report back.

This skill calls FN2 through a small bundled CLI at python3 {baseDir}/scripts/fn2.py (Python 3 standard library only — nothing to install). Run it with the exec tool.

When to use this skill

Reach for FN2 whenever the user asks about:

• A stock or ticker — price action, "how did NVDA do this week and why", fundamentals
• Earnings, guidance, or what management said on a call
• The market or macro picture — the S&P/Nasdaq, the Fed, inflation, rates, jobs
• Comparing companies, screening, or "what's moving and why"
• Setting up recurring research — a daily brief, a weekly recap, an earnings-day monitor — that runs automatically

For one-off questions, use research. For anything recurring or that should keep running on its own, create an agent.

Setup (once)

The CLI authenticates with the FN2_API_KEY environment variable.

If the user isn't connected to FN2 yet (no key set), the CLI prints a sign-up link — surface it to them as the next step. Don't try to work around a missing key; getting one is the onboarding:

You'll need a free FN2 account to use this. Create one and grab an API key here (it takes a minute): https://fn2.ai/api-keys?ref=openclaw Then run: export FN2_API_KEY=fn2_...

The ?ref=openclaw link takes them straight to key creation. Once they've exported the key, retry their request.

How to use it

Run the bundled CLI with exec. Add --json to any command when you want machine-readable output to parse.

Research (the most common use)

python3 {baseDir}/scripts/fn2.py research "How did NVDA do this week, and what drove it?"
python3 {baseDir}/scripts/fn2.py research "What's the macro backdrop into the next Fed meeting?"
python3 {baseDir}/scripts/fn2.py research "Summarize Apple's latest earnings call" --model z-ai/glm-5.2

A research call can take 30–120 seconds because FN2 pulls live data and reads sources. The answer comes back as Markdown.

Agents — schedule recurring research

# Run once, right now:
python3 {baseDir}/scripts/fn2.py agents create --prompt "Deep dive on AMD vs NVDA in AI accelerators"

# Every weekday morning:
python3 {baseDir}/scripts/fn2.py agents create --name "Macro Brief" \
  --prompt "Morning macro brief: overnight moves, key data, what to watch" \
  --every weekdays --timezone America/New_York

# A specific cron schedule (Mondays at 9am):
python3 {baseDir}/scripts/fn2.py agents create --name "Weekly Tech Recap" \
  --prompt "Recap the week in big-cap tech and call out next week's catalysts" \
  --cron "0 9 * * 1" --timezone America/New_York

Manage agents and read their results

python3 {baseDir}/scripts/fn2.py agents list                  # see your agents
python3 {baseDir}/scripts/fn2.py agents run <agent-id>        # trigger a run now
python3 {baseDir}/scripts/fn2.py runs list <agent-id>         # list that agent's runs
python3 {baseDir}/scripts/fn2.py runs get <agent-id> <run-id> # read a run's full answer
python3 {baseDir}/scripts/fn2.py agents pause <agent-id>       # pause / resume
python3 {baseDir}/scripts/fn2.py agents resume <agent-id>
python3 {baseDir}/scripts/fn2.py agents delete <agent-id>      # delete it and its history

Account & models

python3 {baseDir}/scripts/fn2.py models # which models you can use (★ = your default)
python3 {baseDir}/scripts/fn2.py usage  # your plan and token usage

Good habits

• Quote the user's question closely in research — FN2 does the interpreting.
• After creating a scheduled agent, confirm its id and schedule back to the user.
• A run started with agents run is asynchronous: poll runs get until its status is completed, then share the result text.
• A 403 Missing scope means the user's key needs the relevant scope (chat for research, agents for agents, models for the model list) — they can edit it at https://fn2.ai.
• A 429 is a quota limit — show python3 {baseDir}/scripts/fn2.py usage.

See references/api.md for the full command and endpoint reference.