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testland/peeking-problem-reference

Pure-reference catalog of the peeking problem in online A/B testing. Defines the problem (repeatedly looking at experiment results inflates the false-positive rate above the declared alpha because each look is a separate test), the canonical mitigations (fixed-horizon test with pre-declared sample size; sequential testing with alpha-spending functions e.g., O'Brien-Fleming, Pocock; always-valid inference / mSPRT per Johari et al.), and the policy choices (data-peek schedule, stop-early thresholds, decision-time guard rails). Use when designing an experimentation platform's stop-early policy or auditing why a result was declared significant.

75

Quality

94%

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SecuritybySnyk

Passed

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Overview
Quality
Evals
Security
Files

Quality

Content

85%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is a lean, well-structured reference with concrete schedules, math, a worked numeric example, and clear use/anti-pattern guidance. Its only weakness is mild redundancy of the core false-positive-inflation mechanism between the Overview and a dedicated section that could be merged.

Suggestions

Merge or trim the 'Why naive peeking inflates false positives' section since the Overview already states the inflation mechanism; keep only the new detail (the compounding/30-day framing and the Microsoft UI-pattern examples).

Move the Microsoft Experimentation Platform URL and the arXiv/Optimizely citations into the References section rather than interrupting the prose, reducing inline link clutter.

Consider folding the 'Decision boundary in tests' table behind a brief pointer if this reference is meant to stay a pure catalog, since it reads as platform-implementation guidance rather than conceptual reference.

DimensionReasoningScore

Conciseness

Mostly efficient with terse tables and no padding of concepts Claude knows, but the core inflation mechanism is restated in both 'Overview' ('Looking at the data and stopping when significance is reached before the pre-declared end inflates the false-positive rate') and 'Why naive peeking inflates false positives' ('at each look, the test has a fresh chance to spuriously hit'), which could be consolidated.

2 / 3

Actionability

Concrete, actionable guidance throughout: named schedules with their alpha patterns, the math 'Σ alpha_k = alpha_total', and a worked example giving exact per-look/per-metric alpha values (0.011 per look; 0.001 per (look, metric)).

3 / 3

Workflow Clarity

'How to use this reference' provides a clearly sequenced 5-step process (choose regime, pick sequential schedule, lock the look schedule, read peek-protected p-value, stack corrections), and the anti-patterns table supplies explicit fix guidance for each failure mode.

3 / 3

Progressive Disclosure

No bundle files exist, so the single well-organized file with clearly delineated sections (Overview, How to use, When to use, Three corrections, Worked example, Anti-patterns, Limitations, References) satisfies the simple-skill guideline for a top score; cross-skill catalog references are clearly signaled.

3 / 3

Total

11

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12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, complete, and distinctive: it names concrete mitigations and policy choices, gives explicit 'Use when...' trigger guidance, and occupies a clear niche within an experimentation-platform reference family. Third person is used throughout and no fluff or over-claims appear.

DimensionReasoningScore

Specificity

Lists multiple specific concrete contents: 'Defines the problem (repeatedly looking at experiment results inflates the false-positive rate...)', 'canonical mitigations (fixed-horizon test with pre-declared sample size; sequential testing with alpha-spending functions e.g., O'Brien-Fleming, Pocock; always-valid inference / mSPRT)', and 'policy choices (data-peek schedule, stop-early thresholds, decision-time guard rails)'.

3 / 3

Completeness

Explicitly answers what ('Pure-reference catalog of the peeking problem... Defines the problem... canonical mitigations... policy choices') and when ('Use when designing an experimentation platform's stop-early policy or auditing why a result was declared significant').

3 / 3

Trigger Term Quality

Good coverage of natural terms a user would say: 'A/B testing', 'experiment results', 'false-positive rate', 'experimentation platform', 'stop-early policy', and 'result was declared significant'.

3 / 3

Distinctiveness Conflict Risk

Clear niche (the peeking problem in online A/B testing) with distinct triggers and explicit companion-catalog references, making it unlikely to fire for the wrong skill.

3 / 3

Total

12

/

12

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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