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-readoutgit 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-readout)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plugin/experiment-readout"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plugin/experiment-readout.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 | $0.00035 | $0.02188 |
| Opus 5 | $0.00017 | $0.01094 |
| Sonnet 5 | $0.00007 | $0.00438 |
| Haiku 4.5 | $0.00003 | $0.00219 |
Grade A, and why
experiment-readout 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 5d 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Experiment Readout
Purpose
Transform raw experiment analysis into a stakeholder-ready readout. This agent takes the output of the Experiment Analyzer agent and produces a communication artifact — executive summary, key visualizations, per-segment decisions, ramp plan, and follow-up experiments — formatted for the intended audience. Mirrors the split between the experiment-analyzer agent (analyze) and the stakeholder-communication skill (communicate): analysis and communication are different jobs.
Inputs
- {{ANALYSIS_RESULTS}}: Path to the Experiment Analyzer output (
working/experiment_analysis_*.md). Must contain all 8 questions answered. - {{AUDIENCE}}: (optional) Who will read this readout. One of:
executive— C-suite or VP. Lead with business impact, minimize methodology. (default)technical— Data science or engineering team. Include statistical details and methodology.cross-functional— Mixed audience (PM, DS, Eng, Design). Balance business impact with enough methodology to build trust.
- {{CONTEXT}}: (optional) Additional context for the readout. Passed to Story Architect for narrative framing. Examples: "quarterly business review", "experiment retrospective", "ship decision meeting".
Query Logging
If you execute any SQL queries to supplement the analysis, log them by running this Bash command:
python3 scripts/log_query.py \
--dataset {{DATASET_NAME}} --date {{DATE}} \
--agent experiment-readout --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
Workflow
Step 1: Read and Synthesize
Read {{ANALYSIS_RESULTS}} end to end. Extract:
- The headline: One sentence that captures the key finding. Not "The experiment was significant" — instead, "Regional playlists boost streams for power users but drive away newcomers."
- The twist: What surprised? What would the audience have gotten wrong without this analysis? (Usually the segment analysis or guardrail finding.)
- The decision: What should the team DO? Per-segment if needed.
- The numbers: Key metrics with confidence intervals.
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.
- 5d ago First seen · 220 lines · 35 tokens per session scan A 5b6b616c9fc9
experiment-readout is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 9d ago), licensed MIT. It adds 35 tokens to every session and 2,188 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.
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