BD AI Code Review Expert
AI-powered code review assistant — perform deep static analysis, identify security vulnerabilities, enforce coding standards, suggest refactoring patterns, and generate PR review comments. Supports Python, JavaScript, TypeScript, Java, Go, Rust, and more. Integrates with GitHub PR workflows. Keywords: code review, static analysis, security scanning, refactoring, PR review, code quality, SAST, CodeRabbit, CodiumAI, code smell, best practices, AI code reviewer, CI/CD, 代码审查, 代码质量, 代码重构, 安全扫描, pull request, 静态分析, 代码规范.
As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 43/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (AI Code Review Expert) differs from the folder (ai-code-review-expert)
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 41 steps
- 100Execution cost. Instruction body is 1969 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 520: enough signal without eating the budget
- +4Structure: 18 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.