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Agent skill

Initialize

by frenzymath in frenzymath/Danus

First-run setup interview for a Danus deployment. An agent skill from frenzymath/Danus.

Apache-2.0Auto-check passedDevelopment

Install Initialize

skills CLI
$ npx skills add frenzymath/Danus --skill initialize -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install frenzymath/Danus initialize --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/frenzymath/Danus.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/initialize .claude/skills/initialize && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
initialize
GitHub stars
475
Token cost
~1.4k tokens
SKILL.md length
712 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
Apache-2.0

At a glance

First-run setup interview for a Danus deployment. An agent skill from frenzymath/Danus.

  • Works in 5 steps: Greet + orient (brief) → Read current state (so you don't ask… → Ask the choices — as plain questions in… → …
  • Tasks that involve Git workflow
  • SKILL.md covers 0. Greet + orient (brief), 1. Read current state (so you…, 2. Ask the choices — as plain… and 3. Provision — act on the…, plus 1 more section
  • Calls bash and git

What it does

Initialize is an agent skill from frenzymath/Danus. First-run setup interview for a Danus deployment. Run it on the FIRST session, whenever runtime/.danus-initialized is absent or OPERATOR.md is still the blank template, or when the operator asks to set up / initialize / onboard / re-configure. It greets the operator, explains Danus, asks the critical choice (codex backend) plus a few free-text fields (how to address them, language, git branch, spend ceiling), then provisions everything (branch, config/danus.env, OPERATOR.md, codex login, verify service) and marks…

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Git workflow. It works with OpenAI. The repository describes itself as: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Git workflow

Example prompts

  • “/initialize”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Greet + orient (brief)
  2. Read current state (so you don't ask about what's already done)
  3. Ask the choices — as plain questions in the conversation
  4. Provision — act on the answers, persisting each before moving on
  5. Hand off

What it can do on your machine

Read from SKILL.md and the folder at commit 6d92e8d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • bash
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Initialize loads about 1.4k tokens when it runs. Until then it costs about 155 tokens; SKILL.md has 712 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~155
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from frenzymath/Danus at commit 6d92e8d, republished under its Apache-2.0 licence (© frenzymath). 712 words, ~1,436 tokens.

Download SKILL.mdSave it as .claude/skills/initialize/SKILL.md (or your agent's skills folder).
name
initialize
description
First-run setup interview for a Danus deployment. Run it on the FIRST session, whenever runtime/.danus-initialized is absent or OPERATOR.md is still the blank template, or when the operator asks to set up / initialize / onboard / re-configure. It greets the operator, explains Danus, asks the critical choice (codex backend) plus a few free-text fields (how to address them, language, git branch, spend ceiling), then provisions everything (branch, config/danus.env, OPERATOR.md, codex login, verify service) and marks runtime/.danus-initialized. The system cannot run without these answers, so do not skip it.

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
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.

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.md records 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

  • Branch: if on main, git checkout -b <branch> (never work on main).
  • Config: cp -n config/danus.env.example config/danus.env; set CODEX_BACKEND to their choice. If the backend is the OpenAI-compatible key, cp -n config/codex.env.example config/codex.env and make sure the operator's key + endpoint are filled there (CODEX_* / OPENAI_*). Never put secrets anywhere but config/*.env.
  • OPERATOR.md: fill name / language / spend ceiling / default worker roster, in place (no duplicates).
  • codex: backend=api → bash scripts/check-codex.sh (confirm reachable); backend=chatgpt → you run bash scripts/setup-codex.sh login and give the operator the printed URL + device code (they only open it and authorize).
  • Services (must persist beyond your session — services.sh uses setsid): bash scripts/services.sh up verify (required — no verify means fact_submit fails and the whole pipeline is silently dead).
  • Verify the stack: bash scripts/doctor.sh; report green/red plainly.
  • Mark done: mkdir -p runtime && date -u +%FT%TZ > runtime/.danus-initialized.
  • Commit (git discipline): commit OPERATOR.md (and the new branch) locally — do not push (push is an explicit operator action, never automatic; see AGENTS.md). Never commit config/*.env or runtime/.
Show full SKILL.md (254 more words)Show less

4. Hand off

Summarize the chosen backend, confirm the system is up, then ask for the math problem (or return to the operator's original request). When they give it, write runtime/projects/<p>/PROBLEM.md and begin the strategic loop.

Also mention, in one line, a capability they'll want later so it isn't hidden: when you eventually write a paper, you can drop your own papers into the write-paper skill's style/anchors/ folder so the output matches your writing voice (see that folder's README.md; a complete paper is produced either way).

Rules:

  • Ask, don't assume — the choices are the operator's call. "Use the defaults" is fine, but record it explicitly. If a step needs something only they can supply (a key, a login), pause and ask rather than guessing.
  • Verify, never claim unchecked. Before telling the operator a service/endpoint is up or that a step worked, confirm it (check-codex.sh exits non-zero on ping failure; doctor.sh for the rest). A premature "it's up" that turns out to be a failure is exactly what to avoid.
  • Never work on main — branch first if on main.
  • Secrets only in config/*.env — never commit config/*.env or runtime/; only OPERATOR.md (and the branch) are committed.
  • Invoke scripts from the repo root (or by absolute path). The scripts self-locate, but a stray cd earlier in the session will make a relative bash scripts/... call fail — cd to the project dir first if unsure.
  • Persist each answer before moving on, and only write runtime/.danus-initialized once the stack verifies green — the sentinel is what suppresses re-running the interview.

© frenzymath, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/initialize of frenzymath/Danus.

Open the folder on GitHubat commit 6d92e8d

Compare with similar skills

Initialize next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Initialize compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Initialize this skillfrenzymath/Danus475—~1.4kAutomated safety check: PassApache-2.0
Pypi ReleasealchemiststudiosDOTai/tunacode125—~2.2kAutomated safety check: PassMIT
Openai Security Ownership Maptrailofbits/skills-curated5125 repos~2.2kAutomated safety check: NotesCC-BY-SA-4.0
LLM To Bedrockaws/agent-toolkit-for-aws2.8k—~16kAutomated safety check: PassApache-2.0
Codex CLImajiayu000/claude-skill-registry6661 repos~4.2kAutomated safety check: PassMIT
Code Design Rationale Investigatorcursor/plugins10k9 repos~2.6kAutomated safety check: PassNone

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Works with

Categories

Questions about Initialize

What does Initialize do?

First-run setup interview for a Danus deployment. An agent skill from frenzymath/Danus. Initialize is an agent skill from frenzymath/Danus. First-run setup interview for a Danus deployment.

When should I use Initialize?

Initialize fits situations like: tasks that involve Git workflow.

How do I install Initialize in Claude Code?

Run `npx skills add frenzymath/Danus --skill initialize -a claude-code`. Or copy the skill folder (.agents/skills/initialize in frenzymath/Danus) into .claude/skills/initialize in your project. Claude Code loads it when a task matches its description.

How do I install Initialize in Codex?

Run `npx skills add frenzymath/Danus --skill initialize -a codex`. Or copy the skill folder (.agents/skills/initialize in frenzymath/Danus) into .agents/skills/initialize in your project. Codex loads it when a task matches its description.

Can I use Initialize in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add frenzymath/Danus --skill initialize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/initialize, .gemini/skills/initialize, .github/skills/initialize and .opencode/skills/initialize in your project.

What does Initialize need to run?

Going by SKILL.md and its folder, Initialize needs the command-line tools its instructions call (bash and git).

Does Initialize access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Initialize safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Initialize use?

Initialize is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Initialize use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Initialize?

Skills that share tags, products or a category with Initialize: Pypi Release (alchemiststudiosDOTai/tunacode, 125 stars), Openai Security Ownership Map (trailofbits/skills-curated, 512 stars), LLM To Bedrock (aws/agent-toolkit-for-aws, 2.8k stars) and Codex CLI (majiayu000/claude-skill-registry, 666 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Initialize?

frenzymath (a GitHub organization) maintains it in frenzymath/Danus, which has 475 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on August 27, 2026.

Source: frenzymath/Danus on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.