CtrlK
BlogDocsLog inGet started
Tessl Logo
SkillPublic

ab-testing

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program.

Quality

70%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide
SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/antigravity-ab-testing/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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.

A well-structured, domain-rich skill: concrete templates, checklists, and tables with genuine progressive disclosure into two real, clearly-signaled reference files. The main costs are token-level repetition (peeking guidance appears three times), one overloaded safety paragraph, and a bulky inline growth-program section that belongs in a reference.

Suggestions

Consolidate the anti-peeking guidance into one place (e.g., keep 'The Peeking Problem' and cut the duplicated bullets in Core Principles and During the Test).

Break the 'Assignment and safety checks' wall-of-text paragraph into a short bulleted checklist so each check is scannable and actionable.

Move the Growth Experimentation Program section (loop, ICE, velocity, playbook, cadence) to a references/experiment-program.md and link it from a two-line summary in the main file, mirroring the existing sample-size and templates pattern.

DimensionReasoningScore

Conciseness

The body is dense with domain-specific tables, checklists, and templates rather than padded tutorials, but it repeats the anti-peeking guidance in three places ('Don't peek and stop early' in Core Principles, 'Avoid: Peek at results and stop early' in During the Test, and the dedicated 'The Peeking Problem' section), and the 'Assignment and safety checks' paragraph crams many checks into one long unstructured sentence. Mostly efficient with clear tightening opportunities, matching anchor 3; not 4 because the repetition is more than a minor instance, and not 2 because there is no padding that explains fundamentals Claude doesn't need.

3 / 5

Actionability

For an instruction-only skill the guidance is concrete and immediately usable: a fill-in hypothesis template with a weak-vs-strong example, named calculators with URLs, metric-selection examples, traffic allocation tables, pre-launch and analysis checklists, an ICE formula, and a copy-paste-ready playbook template. Not 5 because a few spots stay vague ('Dig deeper, maybe segment'; 'See the reference guide' for the central sample-size numbers without any inline rule of thumb) — minor gaps keep it below fully covering the common cases.

4 / 5

Workflow Clarity

The sections are sequenced along the test lifecycle (assessment → hypothesis → design → sample size → implementation → launch checklist → monitoring rules → analysis checklist → documentation), and the Growth Experimentation Loop is an explicit numbered cycle with a cadence schedule. Checkpoints exist (pre-launch checklist with 'Tracking verified' and 'QA completed', the six-step analysis checklist, guardrail stop policy), matching 'clear sequence with most checkpoints present'. Not 5 because the end-to-end sequence is implicit in heading order rather than stated as one workflow, and a couple of checkpoints (e.g., how to handle SRM/bot detection findings) are named but not wired into an explicit fix-and-recheck loop.

4 / 5

Progressive Disclosure

The two bundle files (references/sample-size-guide.md and references/test-templates.md) are real, clearly signaled with purpose ('For detailed sample size tables and duration calculations: See...', 'For templates: See...'), one level deep, and correctly pull bulk tables/templates out of the main file; the body itself is well-sectioned with headers. Not 5 because the ~90-line Growth Experimentation Program section is substantial program-level material that could equally live in its own reference, keeping the main file long, and the sample-size inline guidance defers to the reference without a minimal fallback.

4 / 5

Total

15

/

20

Passed

Description

61%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.

The description has strong, natural trigger phrasing and a distinct niche, but it is purely a 'when to use' clause: it never explicitly states what the skill does, and its action verbs are generic rather than enumerating concrete capabilities. Adding a capability statement and a synonym like 'split test' would lift it substantially.

Suggestions

Add an explicit 'what' statement before the trigger clause, e.g., 'Designs and runs statistically valid A/B tests: frames hypotheses, calculates sample size, defines primary/guardrail metrics, and analyzes results.'

Include common synonyms such as 'split test' or 'split testing' in the trigger phrasing to improve natural-term coverage.

Distinguish the one-off test use case from the experimentation-program use case more crisply (e.g., '...or when building an ongoing experimentation program with prioritization and a win playbook').

DimensionReasoningScore

Specificity

The description names the domain (A/B testing/experimentation) and the verbs 'plan, design, or implement' and 'build a growth experimentation program', but these are near-synonymous generic verbs applied to the domain rather than distinct concrete capabilities (e.g., no mention of hypothesis framing, sample size calculation, or results analysis). It matches the 'names domain and 1-2 concrete actions, but not comprehensive' anchor; not 4 because the actions are not specific capabilities with minor gaps, and not 2 because it goes well beyond a bare domain label.

3 / 5

Completeness

The description is structured entirely as a 'when' clause ('When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program') with an explicit trigger, but there is no separate statement of what the skill does (e.g., 'Designs statistically valid experiments, calculates sample sizes, and analyzes results') — the 'what' is only weakly implied through the verbs. Scoring only what is explicitly stated, this sits between the 'only when present without what' anchor (2) and a clear both-parts answer; it is noticeably above 2 because the when-clause names concrete actions and the domain, but below 4 because the what must be inferred.

3 / 5

Trigger Term Quality

'A/B test', 'experiment', and 'growth experimentation program' are exactly the natural phrases a user would say when needing this skill. Not 5 because common synonyms such as 'split test'/'split testing', 'test variants', or 'conversion' phrasing are missing; not 3 because the core natural terms users actually use are covered, not just technical jargon.

4 / 5

Distinctiveness Conflict Risk

'A/B test' and 'growth experimentation program' carve a clear niche with distinct triggers, and the phrasing is unlikely to fire for unrelated skills. Not 5 because there is minor overlap risk with closely related marketing/analytics skills (the body itself lists 'cro' and 'analytics' related skills, and 'experiment' could overlap with a general analytics skill).

4 / 5

Total

14

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

boisenoise/skills-collections

Path

skills/antigravity-ab-testing/SKILL.md

Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.