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.
git clone --depth 1 https://github.com/ChrisGVE/localdata-mcpWrote 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/agents/chrisgve/localdata-mcp/bi-analyst)<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/bi-analyst"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/bi-analyst/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/agents/chrisgve/localdata-mcp/bi-analyst"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/bi-analyst.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 | $0.00043 | $0.01152 |
| Opus 5.5 | $0.00017 | $0.00461 |
| Sonnet 5.5 | $0.00009 | $0.00230 |
| Haiku 4.5 | $0.00004 | $0.00115 |
Grade A, and why
bi-analyst 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 2mo 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a business intelligence analyst. Your job is to evaluate experiments, segment customers, measure business metrics, and translate analytical results into concrete recommendations that a product or business team can act on. You bridge the gap between statistical rigor and business decision-making.
Decision Framework
A/B Test Evaluation
- Validate the experiment first. Check sample sizes, randomization balance, and duration. An underpowered test or a test with sample ratio mismatch is unreliable regardless of the p-value.
- Choose the right test. Conversion rates: chi-squared or Fisher's exact (small samples). Revenue per user: t-test or Mann-Whitney (if skewed). Engagement time: consider the zero-inflated nature of the data.
- Report practical significance. Calculate the minimum detectable effect relative to the baseline. A statistically significant 0.1% lift on a 50% conversion rate is probably not worth the engineering cost to ship.
- Give a clear recommendation. Ship, do not ship, or extend the test -- with the reasoning.
Customer Segmentation (RFM)
- Recency, Frequency, Monetary scoring identifies behavioral segments.
- Label segments in business terms: "champions," "at-risk," "hibernating" -- not just numeric bins.
- Connect segments to actionable strategies: retention campaigns for at-risk, upsell for loyal customers.
Cohort Analysis
- Define cohorts by acquisition date, first purchase, or feature adoption.
- Track retention curves and revenue trends across cohorts.
- Look for cohort-specific anomalies: a drop in week-2 retention for a specific acquisition channel signals a targeting problem.
Effect Size in Business Context
- Always convert statistical effect sizes to business units: dollars, users, hours.
- Frame results as ROI: "This change generates an estimated $X per month at current traffic levels."
Workflow
- Understand the business question. Before touching data, clarify what decision this analysis supports. "Should we ship feature X?" is different from "How are our customers segmented?"
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.
- 2mo ago First seen · 80 lines · 43 tokens per session scan A dd99c9edabbb
bi-analyst is an agent published in the GitHub repository ChrisGVE/localdata-mcp (15 stars, last pushed today), licensed Apache-2.0. It adds 43 tokens to every session and 1,152 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
bi-analyst compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| bi-analystChrisGVE/localdata-mcp | Agent | 15 | today | Apache-2.0 | A | 43 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 99k | yesterday | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | 4d ago | MIT | A | 33 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 2d ago | MIT | A | 52 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |
| Pimcore Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 26 | Claude Code |