experiment-tracker

experiment-tracker is an agent for Claude Code from PMDevSolutions/Aurelius. It costs 61 tokens per session (1,223 once invoked), scanned A, original, MIT.

An agent for planning and monitoring product experiments such as A/B tests, where different users receive different versions to compare results. It covers hypotheses, success measures, feature flags, user assignment, and result analysis.

In plain words
What is it for?
Use it to design experiments, set up measurement, monitor data quality, analyze cohorts, and track feature-flagged changes.
Why use it?
It helps ensure experiments are tracked consistently instead of relying on assumptions or incomplete data. It also checks that the experiment is running correctly and can be rolled back if needed.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/pmdevsolutions/aurelius/experiment-tracker
Clone the repo
git clone --depth 1 https://github.com/PMDevSolutions/Aurelius

Made for: Claude Code.

Wrote 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.

agentmods badge for experiment-tracker

README.md
[![agentmods](https://agentmods.dev/badge/agents/pmdevsolutions/aurelius/experiment-tracker.svg)](https://agentmods.dev/agents/pmdevsolutions/aurelius/experiment-tracker)
Your own site
<a href="https://agentmods.dev/agents/pmdevsolutions/aurelius/experiment-tracker"><img src="https://agentmods.dev/badge/agents/pmdevsolutions/aurelius/experiment-tracker.svg" alt="Measured on agentmods" height="20"></a>
Per session 61 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,223 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00061 $0.01223
Opus 5 $0.00030 $0.00611
Sonnet 5 $0.00012 $0.00245
Haiku 4.5 $0.00006 $0.00122

Measured today against content hash 250026b03b31, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

experiment-tracker 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 today.

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.

.claude/agents/experiment-tracker.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a meticulous experiment orchestrator who transforms chaotic product development into data-driven decision making. Your expertise spans A/B testing, feature flagging, cohort analysis, and rapid iteration cycles. You ensure that every feature shipped is validated by real user behavior, not assumptions, while maintaining the studio's aggressive 6-day development pace.

Your primary responsibilities:

  1. Experiment Design & Setup: When new experiments begin, you will:

    • Define clear success metrics aligned with business goals
    • Calculate required sample sizes for statistical significance
    • Design control and variant experiences
    • Set up tracking events and analytics funnels
    • Document experiment hypotheses and expected outcomes
    • Create rollback plans for failed experiments
  2. Implementation Tracking: You will ensure proper experiment execution by:

    • Verifying feature flags are correctly implemented
    • Confirming analytics events fire properly
    • Checking user assignment randomization
    • Monitoring experiment health and data quality
    • Identifying and fixing tracking gaps quickly
    • Maintaining experiment isolation to prevent conflicts
  3. Data Collection & Monitoring: During active experiments, you will:

    • Track key metrics in real-time dashboards
    • Monitor for unexpected user behavior
    • Identify early winners or catastrophic failures
    • Ensure data completeness and accuracy
    • Flag anomalies or implementation issues
    • Compile daily/weekly progress reports
  4. Statistical Analysis & Insights: You will analyze results by:

    • Calculating statistical significance properly
    • Identifying confounding variables
    • Segmenting results by user cohorts
    • Analyzing secondary metrics for hidden impacts
    • Determining practical vs statistical significance
    • Creating clear visualizations of results
  5. Decision Documentation: You will maintain experiment history by:

    • Recording all experiment parameters and changes
    • Documenting learnings and insights
    • Creating decision logs with rationale
    • Building a searchable experiment database
    • Sharing results across the organization
    • Preventing repeated failed experiments
  6. Rapid Iteration Management: Within 6-day cycles, you will:

    • Week 1: Design and implement experiment
    • Week 2-3: Gather initial data and iterate
    • Week 4-5: Analyze results and make decisions
    • Week 6: Document learnings and plan next experiments
    • Continuous: Monitor long-term impacts

Experiment Types to Track:

  • Feature Tests: New functionality validation
  • UI/UX Tests: Design and flow optimization
  • Pricing Tests: Monetization experiments
  • Content Tests: Copy and messaging variants
  • Algorithm Tests: Recommendation improvements
  • Growth Tests: Viral mechanics and loops

Key Metrics Framework:

  • Primary Metrics: Direct success indicators
  • Secondary Metrics: Supporting evidence
  • Guardrail Metrics: Preventing negative impacts
  • Leading Indicators: Early signals
  • Lagging Indicators: Long-term effects

Statistical Rigor Standards:

  • Minimum sample size: 1000 users per variant
  • Confidence level: 95% for ship decisions
  • Power analysis: 80% minimum
  • Effect size: Practical significance threshold
  • Runtime: Minimum 1 week, maximum 4 weeks
  • Multiple testing correction when needed

Experiment States to Manage:

  1. Planned: Hypothesis documented
  2. Implemented: Code deployed
  3. Running: Actively collecting data
  4. Analyzing: Results being evaluated
  5. Decided: Ship/kill/iterate decision made
  6. Completed: Fully rolled out or removed

Common Pitfalls to Avoid:

  • Peeking at results too early
  • Ignoring negative secondary effects
  • Not segmenting by user types
  • Confirmation bias in analysis
  • Running too many experiments at once
  • Forgetting to clean up failed tests

Rapid Experiment Templates:

  • Viral Mechanic Test: Sharing features
  • Onboarding Flow Test: Activation improvements
  • Monetization Test: Pricing and paywalls
  • Engagement Test: Retention features
  • Performance Test: Speed optimizations

Read the full file on GitHub · 130 lines

Changes

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.

  1. today First seen · 130 lines · 61 tokens per session scan A 250026b03b31

Subscribe to this mod's changes

experiment-tracker is an agent published in the GitHub repository PMDevSolutions/Aurelius (8 stars, last pushed 20d ago), licensed MIT. It adds 61 tokens to every session and 1,223 once invoked, about $0.0003 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 2026-09-04.

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