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maintainability-reviewer

Always-on code-review persona. Reviews code for premature abstraction, unnecessary indirection, dead code, coupling between unrelated modules, and naming that obscures intent.

60

Quality

72%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.agents/skills/maintainability-reviewer/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.

A tight, well-structured persona definition: concrete detection patterns, numeric confidence calibration, and explicit exclusions keep it actionable and lean, and the short single-file body is appropriately organized. The main gaps are minor — an empty findings example in the JSON output format and a few rhetorical flourishes that could be trimmed.

DimensionReasoningScore

Conciseness

The body assumes Claude's competence — no explanations of basic concepts — and every section carries detection criteria ("Interfaces with one implementor, factories for a single type"), matching 'efficient; minor instances that could be trimmed'. Not 5: occasional flavor text ("not because they're wrong today, but because they'll cost disproportionately tomorrow"; "Code that isn't called isn't an asset; it's a maintenance liability") adds tokens without adding guidance; not 3: these are minor, not 'unnecessary explanations'.

4 / 5

Actionability

Each hunt category gives concrete detection patterns ("more than two levels of delegation", "Boolean variables without is/has/should prefixes"), calibration gives numeric thresholds (0.80+, 0.60-0.79), and the output format is a complete executable JSON example — matching 'mostly executable guidance with minor gaps'. Not 5: the findings array is shown empty, with no example finding object showing how to encode a concrete issue; not 3: guidance is concrete and specific, not pseudocode.

4 / 5

Workflow Clarity

The flow (hunt -> calibrate confidence -> suppress <0.60 -> emit JSON) is coherent and unambiguous for this single-task skill, with confidence calibration acting as an explicit checkpoint, matching 'clear sequence with most checkpoints present'. Not 5: the sequence is conveyed through section ordering rather than an explicit step list, and no error-recovery loop is described; not 3: there are no real validation gaps — the task is read-only review.

4 / 5

Progressive Disclosure

The body is ~39 lines with no bundle files (references/, scripts/, assets/ are absent) and no external references needed, so per the rubric's simple-skill guideline 'progressive disclosure can score 5 with just well-organized sections' — and the sections (hunting for, calibration, exclusions, output format) are well-organized and navigable.

5 / 5

Total

17

/

20

Passed

Description

66%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 is specific, third-person, and lists five concrete review concerns, but lacks an explicit 'Use when...' trigger clause — 'Always-on' only weakly implies when to use it, which caps completeness. Trigger terms are good but miss common synonyms like 'over-engineering' or 'code smell'.

Suggestions

Add an explicit trigger clause, e.g. 'Use when reviewing code for maintainability, over-engineering, dead code, or naming clarity.'

State the output the skill produces (e.g. 'returns findings as JSON') to round out the 'what'.

Include natural synonyms such as 'over-engineering', 'code smells', and 'refactoring' alongside the technical concern names.

DimensionReasoningScore

Specificity

"Reviews code for premature abstraction, unnecessary indirection, dead code, coupling between unrelated modules, and naming that obscures intent" lists five specific concrete review concerns in third person, matching the 'several specific actions; minor gaps' anchor. Not 5: it never states what the skill produces (findings/JSON output); not 3: far more than 1-2 concrete actions.

4 / 5

Completeness

The 'what' is clear (five listed concern categories), but the 'when' is only weakly implied by "Always-on code-review persona" — there is no explicit 'Use when...' clause, which the judging guidelines state caps completeness at 3. Not 4: 'Always-on' is a persona descriptor, not explicit trigger guidance; not 2: the 'what' is fully clear, not vague.

3 / 5

Trigger Term Quality

"code-review", "dead code", "coupling", "naming" are natural terms users would say when requesting a maintainability review, matching 'good keyword coverage; a few natural terms missing'. Not 5: misses common synonyms like "over-engineering", "code smell", "refactor"; not 3: more than 'some relevant keywords' — four-plus natural phrases with only variation gaps.

4 / 5

Distinctiveness Conflict Risk

The concern list ("premature abstraction", "dead code", "coupling between unrelated modules") carves a distinct maintainability niche away from general code-review skills, matching 'mostly distinct; minor overlap risk with closely related skills'. Not 5: "Reviews code" still overlaps broadly with any generic code-review skill; not 3: the specific concern list keeps overlap to closely related skills only.

4 / 5

Total

15

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

metadata_field

'metadata' should map string keys to string values

Warning

frontmatter_unknown_keys

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

Warning

Total

13

/

16

Passed

Repository
udecode/plate
Reviewed

Table of Contents

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