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 skills add Dokkabei97/forged-claude-code --skill experiment-designergit clone --depth 1 https://github.com/Dokkabei97/forged-claude-codeWrote 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/skills/dokkabei97/forged-claude-code/experiment-designer)<a href="https://agentmods.dev/skills/dokkabei97/forged-claude-code/experiment-designer"><img src="https://agentmods.dev/badge/skills/dokkabei97/forged-claude-code/experiment-designer.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 | $0.00037 | $0.01075 |
| Opus 5 | $0.00018 | $0.00537 |
| Sonnet 5 | $0.00007 | $0.00215 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
experiment-designer 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 7d 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Designer - A/B Test & Product Experiment Guide
Overview
Designs statistically sound experiments so you make decisions based on data, not opinions. Especially critical when traffic is low and every experiment counts.
1. When to Apply
| Trigger | Behavior |
|---|---|
| Testing a product change | Full experiment design |
| "A/B test", "experiment" | Interactive experiment builder |
| Interpreting test results | Statistical analysis guide |
2. Experiment Design Template
# Experiment: [Name]
## Hypothesis
> If we [change X], then [metric Y] will [improve/decrease] by [Z%],
> because [rationale based on user behavior/data].
## Design
| Field | Value |
|-------|-------|
| **Type** | A/B / A/B/n / Multivariate |
| **Primary Metric** | [e.g., conversion rate] |
| **Guardrail Metrics** | [metrics that should NOT worsen] |
| **Baseline** | [current value of primary metric] |
| **MDE** | [Minimum Detectable Effect, e.g., 5%] |
| **Significance** | 95% (α = 0.05) |
| **Power** | 80% (β = 0.20) |
## Sample Size & Duration
| Variant | Traffic Split | Required Sample | Est. Duration |
|---------|--------------|----------------|---------------|
| Control (A) | 50% | [N] | [days] |
| Variant (B) | 50% | [N] | [days] |
## Variants
### Control (A)
[Current experience — no changes]
### Variant (B)
[Specific change being tested]
## Success Criteria
- **Win**: Primary metric improves by ≥ MDE AND guardrails stable
- **Lose**: Primary metric does not improve OR guardrails degrade
- **Inconclusive**: Not enough data (extend or abandon)
## Risks & Mitigations
- [Risk]: [Mitigation]
3. Sample Size Quick Reference
For conversion rate experiments (95% significance, 80% power):
| Baseline Rate | MDE 5% | MDE 10% | MDE 20% |
|---|---|---|---|
| 1% | 380K | 95K | 24K |
| 5% | 72K | 18K | 4.6K |
| 10% | 34K | 8.6K | 2.2K |
| 20% | 16K | 3.9K | 1K |
| 50% | 3.1K | 780 | 200 |
Per variant. Total = N × number of variants.
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.
- 7d ago First seen · 152 lines · 37 tokens per session scan A 6bcaef7ed5c8
experiment-designer is a skill published in the GitHub repository Dokkabei97/forged-claude-code (2 stars, last pushed 6mo ago), licensed MIT. It adds 37 tokens to every session and 1,075 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 2026-08-31.
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