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unit-testing-test-generate

Generate comprehensive, maintainable unit tests across languages with strong coverage and edge case focus.

50

Quality

63%

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tessl review fix ./skills/unit-testing-test-generate/SKILL.md

The canonical home for this skill is unit-testing-test-generate in rmyndharis/antigravity-skills

SKILL.md
Quality
Evals
Security

Quality

Content

50%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 delivers concrete, on-topic code for a multi-language test generation workflow, but it is a monolithic single file whose snippets contain gaps that prevent them from running as-is, and the workflow lacks any validation feedback loop (generated tests are never executed or verified). Splitting per-language material into reference files and adding a run-and-fix cycle would substantially raise quality.

Suggestions

Add a validation step to the workflow: run the generated tests, report failures, and fix or regenerate until they pass — especially before the coverage-analysis step.

Move the per-language/per-framework code (pytest generation, Jest generator, React component tests, coverage analysis) into separate reference files (e.g. references/python.md, references/javascript.md) and keep SKILL.md as an overview.

Fix the non-executable snippets: define or remove the undefined helper methods (_generate_mock_args, _calculate_complexity, etc.) and correct the subprocess invocation to pass a proper argument list (e.g. ['pytest', '--cov', '--cov-report=json']).

Delete the redundant "Context" section and the restated opening paragraph to tighten token usage.

DimensionReasoningScore

Conciseness

The body is mostly code rather than concept explanation, but the "Context" section and opening paragraph are redundant restatements of the description, and ~250 lines of boilerplate test code (React component test templates, generic mock fixtures) restate knowledge Claude already has. This fits anchor 3's "mostly efficient but includes some unnecessary explanation or could be tightened" better than anchor 2, since the padding is limited to a few short sections.

3 / 5

Actionability

The instructions provide substantial concrete code, but key snippets are not executable as-is: section 2 calls undefined helpers (_generate_mock_args, _generate_empty_args, _generate_invalid_args, _calculate_complexity) and uses self at module level, and section 5's subprocess.run([test_command, '--coverage', '--json']) passes a whole command string inside a list and omits needed imports. That matches anchor 3 ("some concrete guidance but incomplete; missing key details") rather than anchor 4's "minor gaps".

3 / 5

Workflow Clarity

Instructions 1-6 give a recognizable sequence (analyze, generate, analyze coverage, mock), but there are no validation checkpoints: generated tests are never run to verify they pass, and the coverage step has no fix-and-regenerate loop. This matches anchor 3 ("sequence present but checkpoints missing or implicit") and the guideline about missing feedback loops in batch test-generation operations.

3 / 5

Progressive Disclosure

The file has well-organized, numbered section headers, but all language-specific material (~250 lines of Python, Jest, and React code) is inlined in a single 300+ line SKILL.md with no reference files at all. This fits anchor 3 ("content that should be separate is inline") rather than anchor 2, since structure is present and navigation is possible.

3 / 5

Total

12

/

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 communicates a clear, single-purpose capability (generating unit tests with coverage and edge-case focus) using mostly natural trigger terms, but it completely lacks a "Use when..." clause, which both caps completeness and weakens its usefulness for skill triggering. Adding explicit usage triggers and framework names (pytest, jest) would move it into the top tier.

Suggestions

Append a trigger clause, e.g. "Use when the user asks to write or generate unit tests, add test coverage, or create test suites for existing code."

Name concrete supporting actions to sharpen specificity, e.g. "analyzes code structure, generates pytest/jest test suites, creates mocks and fixtures, and detects coverage gaps".

Include common framework keywords users actually say — pytest, jest, unittest, mocking — to strengthen trigger-term coverage.

DimensionReasoningScore

Specificity

"Generate comprehensive, maintainable unit tests across languages" names the domain plus one concrete action, with attributes like "strong coverage and edge case focus" adding partial substance. It does not list several distinct specific actions (no analysis, mock generation, or coverage reporting as verbs), so it matches anchor 3 rather than anchor 4, and is clearly more concrete than anchor 2's generic domain naming.

3 / 5

Completeness

The "what" is clear (generate unit tests with coverage and edge-case focus), but there is no "Use when..." clause or equivalent trigger guidance anywhere in the description. The judging guidelines explicitly cap completeness at 3 for a missing when-clause, and anchor 3 ("clear what but when missing") is the exact fit.

3 / 5

Trigger Term Quality

Natural user phrases like "unit tests", "coverage", and "edge case" are present and would be said when needing this skill. A few natural terms are missing — framework names (pytest, jest), "write tests", "test suite" — which fits anchor 4's "good keyword coverage; a few natural terms missing" better than anchor 5's comprehensive synonym/extension coverage.

4 / 5

Distinctiveness Conflict Risk

Unit test generation is a clear, recognizable niche with distinct triggers, mostly distinguishable from related skills like integration testing or code review. "Across languages" is somewhat broad, creating minor overlap risk with language-specific testing skills, which matches anchor 4 better than anchor 5's fully minimal conflict risk.

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

administrakt0r/AI-Agents-Safe-Coding-Skills

Path

skills/unit-testing-test-generate/SKILL.md

Reviewed

Table of Contents

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