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antithesis-review-inputs

Review a workload's input generation for state space exploration: how well it uses randomness to drive the system under test into diverse regions of behavior.

Quality

73%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Passed

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tessl review fix ./antithesis-review-inputs/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

78%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.

The content is a well-structured, lean instruction-only skill: a clear workflow with embedded checkpoints, concrete probing questions and examples, tight scope boundaries, and exemplary progressive disclosure that pushes the full catalog to a single well-signaled reference file. Remaining improvements are small — trimming the duplicated framing/version info and tightening the Tone section.

DimensionReasoningScore

Conciseness

The body is efficient and assumes competence — probing questions, tier definitions, and scope boundaries are all operational guidance, not background explanation — but minor trims exist: the skill version duplicates the frontmatter metadata, and the Tone section restates the framing already given in Purpose and Goal ("A workload exhibiting multiple anti-patterns may still be productive" appears nearly verbatim in both).

4 / 5

Actionability

Concrete, executable guidance throughout: specific questions to ask when examining the workload ("Are action weights fixed, uniform, or varied per-run?", "Do payload sizes span orders of magnitude?"), a worked good-vs-bad finding example ("runs where only deletes fire would test behavior with zero keys"), tier definitions with examples, and an explicit three-part report structure. Minor gaps only — e.g., no guidance on handling very large workloads or the user-provided documentation mentioned in Purpose.

4 / 5

Workflow Clarity

A clear six-step sequence (locate workload → read reference → examine → build findings → tier → present report) with checkpoints present: "If the workload location isn't obvious, ask the user" and presenting the reviewer's workload model first "so the user can correct misunderstandings before reading findings", plus explicit instruction to flag findings requiring domain judgment. This is a read-only analysis skill, so the destructive/batch validation cap does not apply; it falls just short of the anchor-5 feedback-loop level.

4 / 5

Progressive Disclosure

The body is a genuine overview and the full anti-pattern catalog is correctly split into a single one-level-deep reference (`references/anti-patterns.md`, verified to exist and organized by Action Selection / Data Generation / Structural-Timing categories), clearly signaled twice — in Workflow step 2 with what it contains, and in a Reference Files table with when to read it.

5 / 5

Total

17

/

20

Passed

Description

53%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 communicates a clear and fairly specific capability — assessing how a workload's use of randomness limits reachable state space — in appropriately concise third-person form. Its main weaknesses are the complete absence of when-to-use trigger guidance and the omission of natural trigger terms (especially "antithesis" and "fuzzing"), which together cap the trigger and completeness dimensions at 3.

Suggestions

Append an explicit trigger clause, e.g. "Use when a user isn't finding bugs with Antithesis and wants to know whether their workload's input generation is limiting exploration, or as a periodic workload health check."

Include natural trigger terms users would actually say: "antithesis", "workload", "fuzz", "fuzzing", "test coverage", "input generation", "health check".

Name the Antithesis platform in the description itself (not just the skill name) to sharpen distinctiveness against generic test- and code-review skills.

DimensionReasoningScore

Specificity

Names the domain ("input generation for state space exploration") and one concrete action — reviewing "how well it uses randomness to drive the system under test into diverse regions of behavior" — but lists only that single action rather than several, matching the 1-2-concrete-actions anchor rather than the comprehensive ones above.

3 / 5

Completeness

The "what" is clearly stated (review how well input generation uses randomness to drive diverse behavior), but there is no "Use when..." clause or equivalent trigger guidance, which caps this dimension at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Relevant keywords are present ("workload", "input generation", "state space exploration", "randomness", "system under test") but common variations and synonyms are missing — notably "antithesis", "fuzz"/"fuzzing", "test coverage", and "health check", which users would naturally say.

3 / 5

Distinctiveness Conflict Risk

"Review a workload's input generation for state space exploration" carves a mostly distinct niche (randomness-driven exploration review), but omitting the Antithesis platform name leaves minor overlap risk with generic test-review or code-review skills.

4 / 5

Total

13

/

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.

Repository

antithesishq/antithesis-skills

Path

antithesis-review-inputs/SKILL.md

Reviewed

Table of Contents

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