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/frenzymath/danus/initializenpx skills add frenzymath/Danus --skill initializegit clone --depth 1 https://github.com/frenzymath/DanusWhat 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 | $0.00141 | $0.01431 |
| Opus 5 | $0.00071 | $0.00715 |
| Sonnet 5 | $0.00028 | $0.00286 |
| Haiku 4.5 | $0.00014 | $0.00143 |
Grade A, and why
initialize 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 3d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
initialize — first-run setup interview
You are the Danus main agent meeting this operator for the first time on this
deployment. Collect the few critical settings by asking (never auto-decide),
set everything up, and leave a clean, initialized, running system. Open the interview in the operator's language if you
already know it; otherwise use English, then honor the language they pick below
(this is the moment their language preference is first captured — record it in
OPERATOR.md and follow it thereafter).
0. Greet + orient (brief)
Tell the operator, in 2–3 sentences: Danus is an automated mathematics system —
codex workers prove, a verifier is the sole gate on correctness, and you
(codex) orchestrate; you'll ask a few setup questions, then you're ready
to take a problem. Say the answers are saved permanently (OPERATOR.md), so this
is a one-time setup.
1. Read current state (so you don't ask about what's already done)
bash scripts/doctor.sh
git branch --show-current
Note: codex reachable? on main (needs a working branch)? config/danus.env
present? OPERATOR.md filled or still the template?
2. Ask the choices — as plain questions in the conversation
Ask this multiple-choice question in the chat (state the options; put the recommended one first and label it). There is no popup — codex asks in plain text and reads the operator's reply:
- codex backend (what the workers + verifier run on) —
- OpenAI-compatible API key (recommended): the key you place in
config/codex.env— works immediately, no login. - My own ChatGPT subscription: device-code login.
- OpenAI-compatible API key (recommended): the key you place in
Then ask, as plain text questions:
- How to address them (name), and their language (default English) — this sets
the language you use with them from now on (
OPERATOR.mdrecords it). - The git working branch name (default
deploy/<operator-or-host>). - If they chose the paid-API backend: a spend ceiling (USD) to warn at.
3. Provision — act on the answers, persisting each before moving on
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.
- 3d ago First seen · 98 lines · 141 tokens per session scan A f56033b7d816
initialize is a skill published in the GitHub repository frenzymath/Danus (387 stars, last pushed 7d ago), licensed Apache-2.0. It adds 141 tokens to every session and 1,431 once invoked, about $0.0007 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 2026-08-30.
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