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testland/experiment-sdk-testing

Use when code instrumented with Statsig, Optimizely, Split.io (Harness FME), Amplitude Experiment or VWO needs tests. Initializes the SDK offline from a committed config fixture, pins each test to an arm and checks assignment integrity, with per-vendor references.

Quality

97%

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Overview
Quality
Evals
Security
Files

Quality

Content

100%Weight 40%Scale 1-5

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

An exemplary skill body: the shared hermetic-init abstraction is stated once, vendor mechanics are pushed to real, well-signaled reference files, and what remains inline is executable and dense. The routing table in particular delivers high value per token.

DimensionReasoningScore

Conciseness

The body is lean throughout — a routing table, a terse five-step pattern, compact code, and table-formatted anti-patterns with no padding. The Overview earns its tokens by stating the non-obvious unifying insight ('ships the same hermetic-test mechanism under a different name'), not by explaining concepts Claude already knows.

5 / 5

Actionability

The routing table gives the exact offline switch and arm-pinning API for each vendor ('localMode: true', 'set_forced_decision', "authorizationKey: 'localhost'", 'bootstrap', 'is_development_mode'), and the worked Optimizely example is complete, executable Python covering the common case. Each anti-pattern row pairs a failure with a concrete fix.

5 / 5

Workflow Clarity

The shared pattern is a clearly sequenced five-step checklist with explicit validation built in ('Determinism - the same user ID gets the same arm', 'Distribution - across many user IDs, more than one arm actually occurs'), and the anti-patterns table maps each failure mode to its fix. Not 4: no validation gaps remain — the two integrity tests and assert-on-keys-not-IDs rule are explicit checkpoints.

5 / 5

Progressive Disclosure

SKILL.md is a genuine overview holding only the vendor-neutral pattern, with all seven bundle files verified to exist and directly linked one level deep (including the optimizely-recipes.md and split-io-example.md secondaries). Cross-links between references are lateral sibling links, so navigation never nests.

5 / 5

Total

20

/

20

Passed

Description

92%Weight 40%Scale 1-5

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

A strong description with an explicit 'Use when' trigger, concrete third-person actions, and a distinctive vendor-scoped niche. Its only weakness is keyword coverage: users who say 'feature flag tests' or 'A/B test SDK' without naming a vendor would not match it.

DimensionReasoningScore

Specificity

'Initializes the SDK offline from a committed config fixture, pins each test to an arm and checks assignment integrity' lists multiple concrete actions covering the skill's full scope. Not 4: there are no minor gaps — init, pinning, and integrity checking are the complete workflow.

5 / 5

Completeness

'Use when code instrumented with ... needs tests' is an explicit when-clause with concrete trigger conditions, and the what is fully explicit in third person ('Initializes', 'pins', 'checks'). Both halves are answered clearly and concretely.

5 / 5

Trigger Term Quality

Vendor names (Statsig, Optimizely, Split.io, Amplitude Experiment, VWO) plus the natural phrase 'needs tests' give strong keyword coverage, but generic synonyms a user might say — 'feature flag', 'A/B test', 'experimentation' — are absent. Not 5: common natural variations are missing; not 3: the vendor terms are exactly what affected users say.

4 / 5

Distinctiveness Conflict Risk

Naming five specific experimentation SDKs carves out a clear niche with minimal conflict risk — a generic testing or analytics skill would not match these triggers. Not 4: no meaningful overlap with closely related skills; the vendor list plus 'offline from a committed config fixture' is unambiguous.

5 / 5

Total

19

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

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