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 cnfeat/top-pm-skills --skill experiment-designergit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote 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/cnfeat/top-pm-skills/experiment-designer)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/experiment-designer"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/experiment-designer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/experiment-designer"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/experiment-designer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00079 | $0.00874 |
| Opus 5 | $0.00039 | $0.00437 |
| Sonnet 5 | $0.00016 | $0.00175 |
| Haiku 4.5 | $0.00008 | $0.00087 |
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 8d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Designer Skill
Produce rigorous experiment designs from product hypotheses, and interpret results with statistical and practical significance — so you can defend every decision to a sceptical engineering lead or data scientist.
Required Inputs
Ask the user for these if not provided: For experiment design:
- Hypothesis (what change, what metric, what expected movement)
- Current baseline metric value
- Minimum detectable effect (MDE) — the smallest lift worth caring about
- Available daily sample size
For results interpretation:
- Control and variant results (raw numbers or percentages)
- P-value or confidence interval
- Run duration (days)
- Any anomalies observed during the test
Two-Phase Process
Phase 1: Experiment Design
- Restate hypothesis as: "If we [change], we expect [metric] to [move by X%] because [reason]"
- Define control and variant clearly
- Select primary metric (one only) and secondary guardrail metrics (2-3 max)
- Calculate required sample size from MDE and baseline
- Estimate run time in days
- Set pre-defined success criteria before the test runs — no moving goalposts
- Flag design risks: novelty effects, seasonal confounds, multiple testing issues, network effects, sample ratio mismatch
Phase 2: Results Interpretation
- Assess statistical significance (p < 0.05 threshold)
- Assess practical significance: was the lift meaningful for the business, not just real?
- Interpret confidence intervals
- Investigate confounding factors
- Recommend: Ship / Iterate / Kill / Run follow-up test
- Validate — Confirm the test ran for the full planned duration. Flag if it was stopped early (peeking problem). Confirm sample ratio mismatch did not occur.
Output Structure
[Design or Results header based on phase]
Hypothesis: "If we [change], we expect [metric] to [move by X%] because [reason]"
Primary metric: [One metric only] Guardrail metrics: [2-3 max] Required sample size: [n per variant] Estimated run time: [days] Pre-defined success threshold: [specific number] Design risk flags: [any concerns]
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.
- 8d ago First seen · 78 lines · 79 tokens per session scan A 36cc4759c88e
experiment-designer is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 79 tokens to every session and 874 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.