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 question-routergit 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/question-router)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/question-router"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/question-router/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/question-router"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/question-router.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 | $0.00084 | $0.03971 |
| Opus 5 | $0.00042 | $0.01985 |
| Sonnet 5 | $0.00017 | $0.00794 |
| Haiku 4.5 | $0.00008 | $0.00397 |
Grade A, and why
question-router 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 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.
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 — 387 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Question Router
Purpose
Classify incoming user questions into complexity levels (L1-L5) and route them to the appropriate response path.
When to Use
- At the start of every user interaction that looks like an analytical request
- Before launching the full 18-step pipeline
- When the user asks a follow-up question mid-analysis
Classification Levels
L1: Factual Lookup
Pattern: User wants a specific number or fact from the data. Examples:
- "How many users signed up in March?"
- "What's the average order value?"
- "How many products are in the electronics category?"
Response path: Query the data directly. Return the answer with source citation (table, column, filter). No agents needed.
L2: Simple Comparison
Pattern: User wants to compare two things or see a breakdown. Examples:
- "Compare conversion rates by device"
- "Show me revenue by category"
- "What's the split of users by acquisition channel?"
Response path: Query + quick chart. Use chart_helpers directly.
Apply Visualization Patterns skill. No full pipeline.
L3: Guided Analysis
Pattern: User has a specific analytical question requiring multiple steps. Examples:
- "Why did conversion drop last month?"
- "Which user segment has the highest LTV?"
- "Is our new checkout flow performing better?"
Response path: Subset of the pipeline — Frame → Explore → Analyze → Validate → Present findings. Skip storyboard/deck unless requested. Use 3-5 agents.
L4: Deep Investigation
Pattern: User needs root cause analysis, opportunity sizing, or experiment design. Examples:
- "Investigate why mobile revenue dropped 15% in Q3"
- "Size the opportunity if we fix the cart abandonment issue"
- "Design an A/B test for the new pricing page"
Response path: Full pipeline minus deck. Frame → Hypothesize → Explore → Analyze → Root Cause → Validate → Size → Present findings. Use 6-10 agents.
L5: Full Presentation
Pattern: User wants a complete analysis with a polished slide deck. Examples:
- "Run the full pipeline on Q4 performance"
/run-pipeline- "Build me a board-ready deck on our retention problem"
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 · 387 lines · 84 tokens per session scan A 78e9d6bd6c2d
question-router is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 84 tokens to every session and 3,971 once invoked, about $0.0004 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 2026-09-12.
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