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maintainer-review

Review an ioredis issue or pull request as an ioredis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another ioredis design, and worth maintainer and contributor effort. Use when assessing ioredis issue validity or severity, deciding whether an issue should be prioritized or closed, determining whether a requested feature represents an unmet need rather than a discoverability or usage gap, judging whether a PR is worth bringing to mergeable quality, comparing open PRs or alternative designs, separating code quality from repository readiness, or drafting a concise maintainer assessment. When closure, additional evidence, or code changes should be requested, also produce a polite, concise, complete, copy-paste-ready maintainer comment.

71

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%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-engineered decision-procedure skill: concrete statuses, gates, file paths, and checkpoints make it highly actionable and clearly sequenced, with a properly signaled one-level reference. The main cost is token efficiency — repeated policy statements and duplicated rubric content between the body and the reference file inflate the body beyond what clarity requires.

Suggestions

State the `Demonstrated` need gate rule once (in section 2) and reference it from sections 4 and 6 instead of restating it three times.

Deduplicate the severity rubric between section 5 and `references/evaluation-framework.md` — keep only the one-line level names in the body and defer definitions to the reference.

Add a compact worked example of a `Preliminary assessment` report and one maintainer comment inline (or point to the exact section of the reference containing the compact report variants) so the output format is concrete without reading the full reference.

DimensionReasoningScore

Conciseness

The body is dense domain policy rather than concepts Claude already knows, but the `Demonstrated` need gate is restated nearly verbatim in sections 2, 4 (stage 1), and 6, and several long legalistic sentences (e.g., the 60+ word sentences in section 2 and the interleaving pass) could be tightened without losing meaning. It is mostly efficient but includes unnecessary repetition that could be trimmed.

3 / 5

Actionability

Guidance is fully executable for a judgment skill: named `Need evidence` statuses, a concrete severity rubric with definitions, exact ownership file paths (`lib/Redis.ts`, `lib/cluster/`, `lib/autoPipelining.ts`), specific interleaving sequences to trace (`A pending -> B starts -> A fails -> B succeeds`), a fixed report field list, and explicit approval checkpoints before runtime probes. Every step specifies exactly what to do and what to conclude.

5 / 5

Workflow Clarity

The 7-step workflow is clearly sequenced with explicit validation checkpoints: a need gate that must pass before implementation review, a two-stage evidence flow with a mandatory interleaving pass, a stop-and-request-approval rule before any runtime probe, and error-recovery loops (request only the evidence needed, keep the result preliminary if the probe is declined). Decision points and fallbacks are unambiguous.

5 / 5

Progressive Disclosure

The single reference (`references/evaluation-framework.md`) is real, well-signaled, one level deep, and read-conditionally ("Read it when validity, severity, or merge value is not immediately clear"). However, the body is itself a full framework and duplicates reference material — the section 5 severity rubric restates the reference's Severity section, and evidence checks and competing-PR guidance appear in both — so the split between overview and detail could be cleaner.

4 / 5

Total

17

/

20

Passed

Description

92%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 strong description: third-person voice, concrete staged-assessment actions, an explicit multi-scenario "Use when..." clause, and a tightly scoped ioredis maintainer niche that minimizes wrong-skill triggering. Its only weaknesses are length (each clause earns its place, but the sentence runs long) and a few missing natural trigger phrasings like triage.

DimensionReasoningScore

Specificity

"Review an ioredis issue or pull request as an ioredis maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another ioredis design, and worth maintainer and contributor effort" lists multiple specific concrete assessment actions with comprehensive coverage, plus a concrete output action ("produce a polite, concise, complete, copy-paste-ready maintainer comment").

5 / 5

Completeness

It explicitly answers both questions: the "what" is a staged maintainer assessment with named dimensions, and the "when" is an explicit multi-clause "Use when..." list of concrete trigger scenarios plus a conditional trigger for producing the maintainer comment. Both are concrete rather than implied.

5 / 5

Trigger Term Quality

Natural phrases users would say are well covered: "assessing ioredis issue validity or severity", "deciding whether an issue should be prioritized or closed", "judging whether a PR is worth bringing to mergeable quality", "comparing open PRs or alternative designs". A few common variations (e.g., "triage an issue", "review this PR", "is this issue a duplicate") are not covered, keeping it just below comprehensive.

4 / 5

Distinctiveness Conflict Risk

The niche is distinct — ioredis maintainer triage of issues/PRs with a need-evidence and merge-worthiness lens — and every trigger is scoped to ioredis, so it would not naturally fire for generic code review, library debugging, or non-ioredis PRs. Conflict risk with adjacent review skills is minimal.

5 / 5

Total

19

/

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
redis/ioredis
Reviewed

Table of Contents

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