setup

setup is a skill for Claude Code from ai-analyst-lab/ai-analyst. It costs 200 tokens per session (5,902 once invoked), scanned C, original, MIT.

A four-phase conversational setup for configuring an AI analyst. It asks about your real working context and stores the answers in its knowledge system.

In plain words
What is it for?
Use it to onboard the analyst, configure your profile and environment, resume unfinished setup, view status, or reset configuration.
Why use it?
It replaces a blank or incomplete configuration with information the analyst can use. You can also check setup progress or reset stored settings.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to onboard the analyst, configure your profile and environment, resume unfinished setup, view status, or reset configuration.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ai-analyst-lab/ai-analyst/setup
Install

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.

Any agent
npx skills add ai-analyst-lab/ai-analyst --skill setup
Clone the repo
git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst

Made for: Claude Code.

Wrote 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.

agentmods badge for setup

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup/github.svg)](https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/setup)
Your own site
<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/setup"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for setup

Your own site · 80×15
<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/setup"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 200 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,902 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00200 $0.05902
Opus 5 $0.00100 $0.02951
Sonnet 5 $0.00040 $0.01180
Haiku 4.5 $0.00020 $0.00590

Measured 2d ago against content hash a1906124775e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-13, from the pricing page.

Security

Grade C, and why

setup scanned grade C with 1 finding 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 2d 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf .knowledge/datasets/
.claude/skills/setup/SKILL.md · 693 lines

How it starts

The opening of the file, as written. The whole thing — 693 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: /setup

Run a 4-phase conversational interview that populates the knowledge system from the user's real context. Turns a blank .knowledge/ directory into a fully configured analytical environment.

Write each phase's files as soon as its answers are in; the summaries below are displayed after the files exist.

Parameters

  • No arguments: Start from Phase 1 (or resume from last incomplete phase)
  • /setup status: Show current setup state
  • /setup reset: Reset profile and preferences (Tier 1)
  • /setup reset everything: Full reset including dataset connections (Tier 2)

Trigger Phrases

  • /setup
  • set up my environment
  • configure the analyst
  • onboard me

Design Principles

  1. Conversational, not interrogative. You are a colleague getting to know someone, not a form engine. Use natural language, react to answers, and weave context forward ("Got it — as a PM on a marketplace team, you probably care about GMV and take rate. Let me ask about your data next.").
  2. 2-3 questions at a time, max. Never dump a wall of questions. Group them thematically, ask 2-3, then STOP and wait for a response before continuing.
  3. Validate responses. If a role sounds unusual or a path does not exist, confirm before recording. ("You said your CSV directory is data/sales/. I do not see that directory — did you mean data/?")
  4. Allow skipping. Mark optional fields clearly. If the user says "skip" or "I'll do this later", record null and move on. Never block progress on optional fields.
  5. Show progress. After each phase, display the exact summary format specified.

State File

All setup state lives in .knowledge/setup-state.yaml. Create it on first run if it does not exist.

Schema

# .knowledge/setup-state.yaml
setup_version: 1
started_at: "YYYY-MM-DDTHH:MM:SS"
last_updated: "YYYY-MM-DDTHH:MM:SS"
status: "complete" | "partial" | "in-progress"

phases:
  role_and_team:
    status: "complete" | "skipped" | "pending"
    completed_at: "YYYY-MM-DDTHH:MM:SS" | null
  data_connection:
    status: "complete" | "partial" | "skipped" | "pending"
    completed_at: "YYYY-MM-DDTHH:MM:SS" | null
    partial_reason: null | "warehouse_mcp_needed"
  business_context:
    status: "complete" | "skipped" | "pending"
    completed_at: "YYYY-MM-DDTHH:MM:SS" | null
  preferences:
    status: "complete" | "skipped" | "pending"
    completed_at: "YYYY-MM-DDTHH:MM:SS" | null

Read the full file on GitHub · 693 lines

Changes

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.

  1. 2d ago First seen · 693 lines · 200 tokens per session scan C a1906124775e

Subscribe to this mod's changes

setup is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 200 tokens to every session and 5,902 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.

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