Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/fn2ai/fn2-skill/fn2npx skills add fn2ai/fn2-skill --skill fn2git clone --depth 1 https://github.com/fn2ai/fn2-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fn2ai/fn2-skill/fn2)<a href="https://agentmods.dev/skills/fn2ai/fn2-skill/fn2"><img src="https://agentmods.dev/badge/skills/fn2ai/fn2-skill/fn2.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00197 | $0.02547 |
| Opus 5 | $0.00098 | $0.01273 |
| Sonnet 5 | $0.00039 | $0.00509 |
| Haiku 4.5 | $0.00020 | $0.00255 |
Grade A, and why
fn2 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FN2 AI research agents, from your assistant
This skill drives the public fn2.ai API so you can start, watch, and
schedule FN2 AI research agents without leaving Claude, Codex, or Pi. It is a
single, dependency-free Python 3 helper: standard library only, no pip install, no build step.
First run: connect an account (no secret copying)
Connecting is a browser device flow: the CLI prints a short code, the user
enters it at https://fn2.ai/start and clicks Approve. Drive it in two
steps so the user is never left watching a silent terminal. Resolve the helper
first as described in Locating the helper.
Step 1 — get the code (instant, foreground).
python3 "$FN2" auth login --no-wait
# Open https://fn2.ai/start
# Enter code XXXX-XXXX
# (opened in your browser)
This starts (or resumes) a sign-in, prints the URL and code, opens the user's browser itself whenever the machine has a display, and exits immediately.
Step 2 — relay the code, then wait in the background. Tell the user the code and the URL in chat right away, e.g.:
Your sign-in code is XXXX-XXXX. I opened https://fn2.ai/start — sign in (or create an account) and approve the code there. If no tab appeared, click the link.
Then run the finishing wait as a background task so the conversation is not blocked:
python3 "$FN2" auth login --no-browser # resumes the same code, waits for approval
It completes the moment the user approves (codes live 15 minutes) and stores a
scoped, revocable device credential: macOS Keychain when available, otherwise
~/.config/fn2/credentials.json with 0600 permissions. No key is ever
pasted, printed, or sent to chat. Confirm with python3 "$FN2" auth status.
Two rules that matter more than the mechanics:
- Never run bare
auth loginas a blocking foreground command in an assistant turn; it waits up to 15 minutes and hides the code exactly when the user needs it. - Never make the user dig the code out of a task log. The code and URL go into chat the moment Step 1 prints them.
What ships with it
16 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 214 B
- references/api.md 11 KB
- references/scheduling.md 2.5 KB
- references/troubleshooting.md 5.9 KB
- scripts/fn2 553 B
- scripts/fn2cli/__init__.py 843 B runs code
- scripts/fn2cli/__main__.py 176 B runs code
- scripts/fn2cli/api.py 3.0 KB runs code
- scripts/fn2cli/cli.py 45 KB runs code
- scripts/fn2cli/client.py 6.7 KB runs code
- scripts/fn2cli/config.py 6.8 KB runs code
- scripts/fn2cli/deviceauth.py 5.0 KB runs code
- scripts/fn2cli/render.py 10 KB runs code
- scripts/fn2cli/schedule.py 11 KB runs code
- scripts/fn2cli/storage.py 10 KB runs code
- scripts/fn2cli/style.py 4.2 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 208 lines · 197 tokens per session scan A f1de2bb253ba
fn2 is a skill published in the GitHub repository fn2ai/fn2-skill (2 stars, last pushed 25d ago), licensed MIT. It adds 197 tokens to every session and 2,547 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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