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open-code-review-delegate

Delegation mode for open-code-review (OCR). Instead of OCR calling an LLM endpoint, this skill instructs the host agent to perform the code review itself, using OCR only for deterministic engineering: file selection and rule resolution. Use when the host agent should drive the review with its own LLM capabilities.

Quality

84%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

An exemplarily actionable skill body: every step is executable, the batch-review workflow carries mandatory checklists, coverage validation, and error-recovery loops, and there is essentially no filler. The only refinements are trimming minor over-explanations, isolating the version-sensitive compatibility note, and considering a reference file for the CLI flag/troubleshooting material.

DimensionReasoningScore

Conciseness

The body is efficient — dominated by exact commands, tables, and tool-specific facts rather than concepts Claude already knows. Minor trims remain: the sentence explaining the diff flags ('disable external diff programs, text conversion, and color') and the version pin 'v1.9.0', which is time-sensitive information not placed in a deprecated/old-patterns section.

4 / 5

Actionability

Fully executable throughout: exact 'ocr delegate preview/rule' invocations, mode-specific 'git --no-pager diff --no-ext-diff --no-textconv --no-color' commands, a structured comment field table, concrete limits ('must not exceed 1 MiB', '8000 characters'), and an exact failure signature ('unknown flag: --format') with a fallback procedure. Commands are copy-paste ready and cover the common cases.

5 / 5

Workflow Clarity

Steps 1–7 are clearly sequenced with explicit validation and feedback loops for this batch operation: a mandatory per-file checklist ending in 'reviewed' or 'skipped with a concrete reason', Step 6 coverage verification ('total_files, reviewed_files, skipped_files, coverage_rate'), Git-failure recovery ('retry or record skipped... Check unexpectedly empty output against a fresh preview'), and dedicated recovery procedures for oversized background context and version incompatibility.

5 / 5

Progressive Disclosure

A single ~190-line file, well-sectioned (Workflow, Sub-commands Reference, Shared Flags, Gotchas, two troubleshooting subsections) with no nested references and nothing buried. The 50-line simple-skill exception does not apply at this length, and the flags table plus the two long troubleshooting subsections could plausibly live in a reference file, which keeps it at 'good structure, minor organization gaps' rather than a model split.

4 / 5

Total

18

/

20

Passed

Description

71%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 specific, well-scoped description with a clear what/when structure and minimal conflict risk. Its main weakness is trigger phrasing: the 'Use when' clause speaks in integration-architecture terms ('host agent... own LLM capabilities') rather than natural user language, which weakens discoverability.

Suggestions

Rewrite the 'Use when' clause around user-natural phrasing, e.g. 'Use when the user asks for a code review of workspace changes, a branch, or a commit and the ocr CLI is available' — this adds the natural trigger terms ('code review', 'review my changes', 'branch/commit') currently missing.

Add one or two concrete review activities to the capability list (e.g. producing structured findings with severity/category, or verifying full file coverage) so the 'what' reflects the skill's actual output, not just input-side engineering.

Mention the underlying deliverables a user cares about — per-file review comments with severity and a coverage summary — to sharpen the distinction from generic code-review helpers.

DimensionReasoningScore

Specificity

Names several concrete actions — 'instructs the host agent to perform the code review itself, using OCR only for deterministic engineering: file selection and rule resolution' — tied to a specific tool. Falls short of 5 because it omits other workflow stages (diff acquisition, output formatting, coverage reporting) that the skill actually covers.

4 / 5

Completeness

Explicitly answers both: what ('Delegation mode for open-code-review (OCR)... file selection and rule resolution') and when ('Use when the host agent should drive the review'). The 'when' clause is present so the score-3 cap does not apply, but it describes an integration preference rather than concrete user-task triggers, keeping it below 5.

4 / 5

Trigger Term Quality

Relevant keywords exist ('code review', 'delegation', 'OCR') but the trigger condition is architectural jargon — 'when the host agent should drive the review with its own LLM capabilities' — rather than phrases a user would naturally say, and common variations like 'review my changes/PR' are absent. Not 4 because natural-term coverage is genuinely incomplete, not just missing a few synonyms.

3 / 5

Distinctiveness Conflict Risk

'Delegation mode for open-code-review (OCR)' carves a clear niche anchored to a specific named CLI and a distinct operating mode, so it is unlikely to trigger for unrelated review skills. Not 4: the tool-specific framing leaves only minimal, not 'minor but real', overlap risk.

5 / 5

Total

16

/

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

alibaba/open-code-review

Path

skills/open-code-review-delegate/SKILL.md

Reviewed

Table of Contents

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