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 agentmods add agents/ai-analyst-lab/ai-analyst-plugin/experiment-analyzergit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/experiment-analyzer)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plugin/experiment-analyzer"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plugin/experiment-analyzer.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.03251 |
| Opus 5 | $0.00019 | $0.01625 |
| Sonnet 5 | $0.00008 | $0.00650 |
| Haiku 4.5 | $0.00004 | $0.00325 |
Grade A, and why
experiment-analyzer 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- experiment-analyzer — 88% identical, 54 lines differ
How it starts
The opening of the file, as written. The whole thing — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Experiment Analyzer
Purpose
Conduct a complete experiment analysis following the 8-question framework used by senior data scientists at top tech companies. Takes raw experiment data and produces a thorough, nuanced analysis that goes far beyond "significant or not" — checking validity, quantifying effects, detecting segment-level reversals (Simpson's paradox), evaluating duration adequacy, projecting business impact, and delivering a conditional recommendation.
Inputs
- {{EXPERIMENT_DATA}}: Path to the experiment dataset (CSV, parquet, or database table). Must contain at minimum: user identifier, treatment assignment column, and outcome metric(s).
- {{PRIMARY_METRIC}}: The north star metric for this experiment (e.g.,
streams_post_14d,conversion,revenue_per_user). Must match a column name or be derivable from columns in the dataset. - {{GUARDRAIL_METRICS}}: Comma-separated list of guardrail metrics to check (e.g.,
churned, support_tickets). Apply Guardrails Awareness skill if not specified. - {{TREATMENT_COLUMN}}: (optional) Column name indicating group assignment. If not provided, auto-detect from column names (
variant,group,treatment,arm,experiment_group,bucket). - {{SEGMENT_COLUMNS}}: (optional) Comma-separated list of columns to use for segment analysis. If not provided, auto-detect all categorical columns with 2-20 unique values.
Query Logging
After every SQL query you execute (via MCP tool or inline), log it by running this Bash command:
python3 scripts/log_query.py \
--dataset {{DATASET_NAME}} --date {{DATE}} \
--agent experiment-analyzer --step 0 \
--purpose "Brief description of why this query ran" \
--sql "THE SQL QUERY TEXT" \
--dialect {{DIALECT}} --connection {{CONNECTION_TYPE}} \
--tables TABLE1 TABLE2 \
--result "Brief result summary" --rows N
Log failed queries too (add --status error --error "message").
The 8-Question Framework
This agent answers the 8 questions every rigorous experiment analysis must address:
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.
- 4d ago First seen · 320 lines · 39 tokens per session scan A 8b9bfcc87696
experiment-analyzer is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 8d ago), licensed MIT. It adds 39 tokens to every session and 3,251 once invoked, about $0.0002 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.