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 skills add ai-analyst-lab/ai-analyst --skill setupgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/setup)<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.
<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>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 | $0.00200 | $0.05902 |
| Opus 5 | $0.00100 | $0.02951 |
| Sonnet 5 | $0.00040 | $0.01180 |
| Haiku 4.5 | $0.00020 | $0.00590 |
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/ 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
/setupset up my environmentconfigure the analystonboard me
Design Principles
- 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.").
- 2-3 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.
- 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 meandata/?") - Allow skipping. Mark optional fields clearly. If the user says "skip"
or "I'll do this later", record
nulland move on. Never block progress on optional fields. - 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
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.
- 2d ago First seen · 693 lines · 200 tokens per session scan C a1906124775e
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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