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sc-analyze

sc-analyze

github.com/tony363Testing & quality2094 installsUpdated 30w ago
Repository

About sc-analyze

sc-analyze is a code analysis and troubleshooting skill for examining quality, security, performance, and architecture issues. It can prioritize findings, recommend actions, and optionally apply safe fixes, with PAL MCP analysis and debugging support.

Key features

  1. 01Analysis across four code domains
  2. 02Troubleshooting for bugs and builds
  3. 03Prioritized findings and recommendations
  4. 04Optional safe automatic fixes
  5. 05Text, JSON, and report output

Use cases

  1. 01Review source code for security issues
  2. 02Diagnose build failures or runtime bugs
  3. 03Investigate slow performance
  4. 04Assess architecture and dependencies

MCP support

Uses PAL MCP tools for deeper investigation, debugging, code review, and validation; can also use Rube MCP for external analysis, notifications, and ticket creation.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/tony363/superclaude --skill sc-analyze

After installing, type/sc-analyzein Claude Code to confirm it loaded.

Details

From the project README

Analysis & Troubleshooting Skill

Multi-domain code analysis with issue diagnosis and resolution capabilities.

Quick Start

# Quality analysis
/sc:analyze [target] --focus quality|security|performance|architecture

# Troubleshooting mode
/sc:analyze [issue] --troubleshoot --focus bug|build|performance|deployment

# With auto-fix
/sc:analyze "TypeScript errors" --troubleshoot --focus build --fix

Behavioral Flow

  1. Discover - Categorize source files, detect languages
  2. Scan - Apply domain-specific analysis techniques
  3. Evaluate - Generate prioritized findings with severity
  4. Recommend - Create actionable recommendations
  5. Report - Present comprehensive analysis with metrics

Flags

FlagTypeDefaultDescription
--focusstringqualityquality, security, performance, architecture, bug, build, deployment
--troubleshootboolfalseEnable issue diagnosis mode
--traceboolfalseDetailed trace analysis for debugging
--fixboolfalseAuto-apply safe fixes
--depthstringstandardquick, standard, deep
--formatstringtexttext, json, report

Analysis Domains

Quality Analysis
  • Code smells and maintainability issues
  • Pattern violations and anti-patterns
  • Technical debt assessment
Security Analysis
  • Vulnerability scanning
  • Compliance validation
  • Authentication/authorization review
Performance Analysis
  • Bottleneck identification
  • Resource utilization patterns
  • Optimization opportunities
Architecture Analysis
  • Component coupling assessment
  • Dependency analysis
  • Design pattern evaluation

Troubleshooting Mode

When --troubleshoot is enabled:

FocusBehavior
bugError analysis, stack traces, code inspection
buildBuild logs, dependencies, config validation
performanceMetrics analysis, bottleneck identification
deploymentEnvironment analysis, service validation

Examples

Security Deep Dive
/sc:analyze src/auth --focus security --depth deep
Build Failure Fix
/sc:analyze "compilation errors" --troubleshoot --focus build --fix
Performance Diagnosis
/sc:analyze "slow API response" --troubleshoot --focus performance --trace

MCP Integration

PAL MCP (Always Use)
ToolWhen to UsePurpose
mcp__pal__thinkdeepComplex issuesMulti-stage investigation with hypothesis testing
mcp__pal__debugBug troubleshootingSystematic root cause analysis
mcp__pal__codereviewQuality analysisComprehensive code quality, security, performance review
mcp__pal__consensusCritical findingsMulti-model validation of security/architecture issues
mcp__pal__challengeUncertain findingsForce critical thinking on ambiguous issues
mcp__pal__apilookupDependency issuesGet current API docs for version conflicts
PAL Usage Patterns
# Deep investigation (--depth deep)
mcp__pal__thinkdeep(
    step="Investigating performance bottleneck in API layer",
    hypothesis="Database queries lack proper indexing",
    confidence="medium",
    relevant_files=["/src/api/users.py"]
)

# Security analysis (--focus security)
mcp__pal__codereview(
    review_type="security",
    findings="Authentication, authorization, injection vectors",
    issues_found=[{"severity": "high", "description": "SQL injection risk"}]
)

# Critical finding validation
mcp__pal__consensus(
    models=[
        {"model": "gpt-5.2", "stance": "for"},
        {"model": "gemini-3-pro", "stance": "against"}
    ],
    step="Evaluate: Is this a critical security vulnerability?"
)
Rube MCP (When Needed)
ToolWhen to UsePurpose
mcp__rube__RUBE_SEARCH_TOOLSExternal analysisFind security scanners, linters
mcp__rube__RUBE_MULTI_EXECUTE_TOOLIssue trackingCreate tickets for findings
mcp__rube__RUBE_REMOTE_WORKBENCHBulk analysisProcess large codebases
Rube Usage Patterns
# Find and create Jira tickets for findings
mcp__rube__RUBE_SEARCH_TOOLS(queries=[
    {"use_case": "create jira issue", "known_fields": "project:SECURITY"}
])

# Notify team of critical findings
mcp__rube__RUBE_MULTI_EXECUTE_TOOL(tools=[
    {"tool_slug": "SLACK_SEND_MESSAGE", "arguments": {"channel": "#security", "text": "Critical finding..."}}
])

Flags (Extended)

FlagTypeDefaultDescription
--pal-deepboolfalseUse PAL thinkdeep for multi-stage analysis
--pal-reviewboolfalseUse PAL codereview for comprehensive review
--consensusboolfalseUse PAL consensus for critical findings
--notifystring-Notify via Rube (slack, jira, email)
--create-ticketsboolfalseCreate tickets for findings via Rube

Tool Coordination

  • Glob - File discovery and structure analysis
  • Grep - Pattern analysis and code search
  • Read - Source inspection and config analysis
  • Bash - External tool execution
  • Write - Report generation
  • PAL MCP - Multi-model analysis, debugging, code review
  • Rube MCP - External notifications, ticket creation

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