AgentSkillsCN

unfault-review

运行unfault review，对本代码库进行生产就绪性分析：检查是否存在缺失的错误处理、扩展性瓶颈、正确性缺陷，以及稳定性风险。在被要求审查代码、排查问题，或准备PR评审时使用。注意：此命令会调用外部LLM，可能需要30–60秒的时间。

SKILL.md
--- frontmatter
name: unfault-review
description: >-
  Run unfault review to analyze this codebase for production-readiness issues: missing error handling, scalability bottlenecks, correctness bugs, and stability risks. Use when asked to review code, check for issues, or prepare a PR review. NOTE: this command calls an external LLM and may take 30-60 seconds.
disable-model-invocation: true

unfault review — production-readiness analysis

Run a full static analysis of the current workspace for production-readiness issues.

Command

bash
unfault review

Common flags

FlagDescription
--output basicHeader + summary only (default)
--output conciseBrief findings list
--output fullDetailed analysis with explanations
--output jsonMachine-readable JSON
--output sarifSARIF for GitHub Code Scanning
--dimension <DIM>Limit to: stability, correctness, performance, scalability
--profile <PROFILE>Override auto-detected profile (e.g. python_fastapi_backend)
--fixAuto-apply all suggested fixes
--dry-runPreview fixes without applying them

Supported profiles

python_fastapi_backend, python_django_backend, python_flask_backend, python_generic_backend, go_gin_service, go_generic_service, rust_axum_service, rust_actix_service, typescript_express_backend, typescript_nextjs_app

Examples

bash
# Full analysis with detailed output
unfault review --output full

# Check only correctness issues
unfault review --dimension correctness --output full

# Get JSON findings for programmatic processing
unfault review --output json

# Preview auto-fixes
unfault review --dry-run

# Apply all fixes
unfault review --fix

Interpreting output

Findings include a severity (error / warning / info) and a dimension:

  • •correctness — logic bugs, improper error handling, unsafe assumptions
  • •stability — missing retries, poor fault-tolerance, crash-prone patterns
  • •performance — N+1 queries, blocking calls, inefficient algorithms
  • •scalability — shared mutable state, unbounded growth, missing pagination

How to act on findings:

  1. •Prioritize error-level findings first — these indicate likely bugs in production.
  2. •Group warning-level findings by dimension and address the most impactful ones.
  3. •Use --fix for mechanical fixes (missing null checks, unused imports, etc.).
  4. •For architectural findings, discuss trade-offs with the developer before applying changes.
  5. •Cross-reference high-severity findings with the files identified as critical by unfault graph critical — issues in hub files have the highest blast radius.