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-plus/experiment-interpretergit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-plusWrote 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-plus/experiment-interpreter)<a href="https://agentmods.dev/agents/ai-analyst-lab/ai-analyst-plus/experiment-interpreter"><img src="https://agentmods.dev/badge/agents/ai-analyst-lab/ai-analyst-plus/experiment-interpreter.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 5.1 | $0.00000 | $0.01637 |
| Opus 5 | $0.00000 | $0.00818 |
| Sonnet 5 | $0.00000 | $0.00327 |
| Haiku 4.5 | $0.00000 | $0.00164 |
Grade A, and why
experiment-interpreter 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 6d 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.
This is a copy
95% identical to experiment-interpreter — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Experiment Interpreter
Purpose
Classify experiment outcomes using a structured decision framework. Takes raw analysis results and pre-registered decision rules, walks the Result Interpretation Tree, and produces one of four verdicts: Ship, Abort, Learn, or Invalid. Prevents post-hoc rationalization by anchoring every decision to pre-registered criteria.
Inputs
- {{ANALYSIS_RESULTS}}: Path to analysis output (JSON or markdown from Experiment Analyzer).
- {{EXPERIMENT_CONFIG}}: Path to
experiment.yamlwith pre-registered decision rules.
Framework: Result Interpretation Tree
Branch 1: Check Validity First
Before interpreting results, verify the experiment itself was valid:
SRM verdict?
├── BLOCK → INVALID: Randomization broken. Cannot trust any results.
├── WARNING → Flag but continue with caution.
└── PASS → Proceed to interpretation.
Data quality issues?
├── >10% missing outcomes → INVALID: Too much missing data.
├── Implementation bug detected → INVALID: Treatment didn't deploy correctly.
└── Clean → Proceed.
If INVALID: Stop. Do not interpret. Report what went wrong and recommend fixes.
Branch 2: Interpret Primary Metric
Primary metric result?
├── Significant POSITIVE (p < alpha, lift > 0):
│ └── Check guardrails → Branch 3
├── Significant NEGATIVE (p < alpha, lift < 0):
│ └── ABORT: Treatment hurt the primary metric.
├── Not significant (p >= alpha):
│ ├── Was the experiment adequately powered (≥80%)?
│ │ ├── YES → ABORT: Powered null. No evidence of benefit.
│ │ │ Note: "The experiment had sufficient power to detect a
│ │ │ [MDE] effect. Observing no significant effect means the
│ │ │ true effect is likely smaller than [MDE]."
│ │ └── NO → LEARN: Underpowered null. Effect may exist but
│ │ we couldn't detect it.
│ │ Recommendations:
│ │ - Extend the experiment
│ │ - Increase traffic allocation
│ │ - Choose a more sensitive metric
│ │ - Accept the inconclusive result and move on
│ └── Compute the CI. If CI includes practically meaningful effects,
│ flag: "We cannot rule out a [X]% effect."
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.
- 6d ago First seen · 170 lines · 0 tokens per session scan A 5e141b8b64b1
experiment-interpreter is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plus (19 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,637 tokens. A static security scan graded it A with 0 findings. It is 95% identical to experiment-interpreter, differing in 19 lines, and is treated as a copy.
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