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mint

Generating test data and fixtures. Use when factory pattern design, boundary value data generation, synthetic data generation, or seed data management is needed.

56

Quality

63%

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tessl review fix ./.archive/mint/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

56%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 body is highly actionable with concrete code, exact boundary values, and a validated five-phase workflow, but it is padded with redundant restatements, collaboration boilerplate, and verbose tool citations. Its reference-based disclosure is structurally sound yet undermined because none of the referenced files actually exist in the bundle.

Suggestions

Consolidate the determinism guidance (faker.seed + setDefaultRefDate) into a single authoritative section — it is currently repeated across Core Contract, Always, Output Requirements, Favorite Tactics, and Avoids — and cut the CAPABILITIES_SUMMARY/COLLABORATION_PATTERNS HTML comment that duplicates the Collaboration section.

Move the four external-tool bullets (GoReplay/Speedscale, MOSTLY AI/Gretel, DP-SGD, MSW v2) into a reference file, keeping one-line pointers in the body to reclaim roughly 30 lines of core context.

Create the referenced files (reference/factory-patterns.md, boundary-values.md, seed-management.md, etc.) or remove the pointers — as delivered, every reference in the Recipes table and References section is a broken path, breaking progressive disclosure for the multi-step recipes.

DimensionReasoningScore

Conciseness

The ~400-line body is noticeably verbose: the same determinism rule is restated in Core Contract, Always, Output Requirements, Favorite Tactics, and Avoids; a large HTML comment block (CAPABILITIES_SUMMARY, COLLABORATION_PATTERNS, BIDIRECTIONAL_PARTNERS) duplicates the Collaboration section; and four multi-sentence Core Contract bullets cite external tools (GoReplay, MOSTLY AI, Gretel, DP-SGD, MSW) with marketing-grade justification. It does not explain basics Claude already knows, so it stays above anchor 1 but fits anchor 2 ("several unnecessary explanations or padded sections").

2 / 5

Actionability

Concrete, executable guidance dominates: a runnable Fishery TypeScript factory example (basic, relational with associations, transient-params traits), specific seed commands ("faker.seed(N)" + "faker.setDefaultRefDate(fixed)"), a per-type boundary value table with exact values (""", MIN_SAFE_INTEGER, leap day), and a subcommand dispatch rule with a keyword-to-recipe table. Minor gaps — the FK build order and idempotency verification are described but never shown as commands — keep it at anchor 4 rather than 5.

4 / 5

Workflow Clarity

The ANALYZE → DESIGN → GENERATE → VALIDATE → DELIVER workflow is clearly sequenced in a phase table with outputs per phase, and the VALIDATE phase names concrete checkpoints ("Run against schema constraints, verify FK consistency, confirm idempotency, check PII leaks") — so the destructive/batch cap of 3 does not apply since validation is explicitly present. However, there is no error-recovery feedback loop (what to do when validation fails), which anchor 5 requires, placing it at anchor 4 ("most checkpoints present; minor validation gaps").

4 / 5

Progressive Disclosure

Sections are well organized and consistently point to reference files ("Full catalog ... -> reference/factory-patterns.md", a References table), but no bundle files exist in the skill directory — every referenced path (reference/*.md, _common/*.md) is broken, so the disclosure chain dead-ends. Additionally, the long external-tool bullets in Core Contract are inlined material that belongs in those references. This fits anchor 3 ("references present but not clearly signaling usable structure; content that should be separate is inline") — not 4, since the referenced layer is unusable as written.

3 / 5

Total

13

/

20

Passed

Description

70%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 solid third-person description with an explicit 'Use when' clause and mostly natural trigger terms. Its main weakness is a thin 'what' — a single generic action phrase — that leaves capability coverage incomplete relative to the skill's actual scope.

DimensionReasoningScore

Specificity

The description names the domain ("Generating test data and fixtures") with one concrete action verb, and the when-clause hints at more capabilities ("factory pattern design", "boundary value data generation"), but these read as trigger phrases rather than stated actions — coverage of what the skill does is not comprehensive (anonymization, snapshots, property-based generators are absent). This matches anchor 3 ("Names domain and 1-2 concrete actions, but not comprehensive") and falls short of anchor 4, which expects several specific listed actions.

3 / 5

Completeness

Both parts are present: what ("Generating test data and fixtures") and an explicit when ("Use when factory pattern design, boundary value data generation, synthetic data generation, or seed data management is needed") with concrete trigger phrases. However, the 'what' is a single thin clause that undersells the capability set, fitting anchor 4 ('when' could be more explicit/specific, 'what' not fully enumerated) rather than the fully comprehensive anchor 5.

4 / 5

Trigger Term Quality

Terms like "test data", "fixtures", "factory pattern", "seed data", and "synthetic data generation" are natural developer phrasing with reasonable synonym coverage. Missing common variations such as "fake data", "Faker", "test fixtures", or "mock data", which keeps it at anchor 4 ("Good keyword coverage; a few natural terms missing") rather than 5.

4 / 5

Distinctiveness Conflict Risk

Test data generation is a clear niche with distinct triggers, unlikely to fire for unrelated skills. Minor overlap risk remains: "factory pattern design" could pull general design-pattern requests and "synthetic data generation" could collide with a data-platform synthetic data skill, so anchor 4 ("Mostly distinct; minor overlap risk") fits better than 5.

4 / 5

Total

15

/

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
simota/agent-skills
Reviewed

Table of Contents

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