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estiens

pal-analyze

by estiens

composable meta-cognitive scaffolds for LLM chats

0🍴 0📅 2025年12月29日
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SKILL.md


name: pal-analyze description: Comprehensive code analysis for architecture, performance, security, and quality using PAL MCP. Use when reviewing codebases, assessing technical decisions, or planning improvements. Triggers on analysis requests, architecture reviews, or code quality assessments.

PAL Analyze - Code Analysis

Systematic code analysis covering architecture, performance, maintainability, and patterns.

When to Use

  • Understanding unfamiliar codebases
  • Architectural review and assessment
  • Performance analysis and optimization
  • Code quality evaluation
  • Pattern identification
  • Technical debt assessment

Quick Start

# Start architecture analysis
result = mcp__pal__analyze(
    step="Analyzing authentication system architecture",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Beginning architecture review",
    analysis_type="architecture",
    output_format="detailed",
    relevant_files=[
        "/app/auth/service.py",
        "/app/auth/middleware.py"
    ],
    confidence="exploring"
)

Analysis Types

TypeFocus
architectureSystem design, patterns, modularity
performanceBottlenecks, optimization opportunities
securityVulnerabilities, auth issues
qualityCode smells, maintainability
generalComprehensive overview

Output Formats

FormatDescription
summaryHigh-level overview
detailedIn-depth analysis
actionablePrioritized recommendations

Required Parameters

ParameterTypeDescription
stepstringAnalysis narrative
step_numberintCurrent step
total_stepsintEstimated total
next_step_requiredboolMore analysis needed?
findingsstringDiscoveries and insights

Optional Parameters

ParameterTypeDescription
analysis_typeenumarchitecture/performance/security/quality/general
output_formatenumsummary/detailed/actionable
confidenceenumexploring → certain
relevant_fileslistFiles under analysis
files_checkedlistAll files examined
issues_foundlistIssues with severity
continuation_idstringContinue session
modelstringOverride model

Example: Performance Analysis

mcp__pal__analyze(
    step="Identifying performance bottlenecks in data processing pipeline",
    step_number=1,
    total_steps=2,
    next_step_required=True,
    findings="Scanning for N+1 queries, inefficient loops, missing caching",
    analysis_type="performance",
    output_format="actionable",
    relevant_files=[
        "/app/services/data_processor.py",
        "/app/models/report.py"
    ],
    confidence="exploring"
)

What to Document in Findings

Include both strengths and concerns:

  • Architecture: Patterns used, coupling, cohesion
  • Performance: Complexity, caching, query patterns
  • Security: Auth flows, input validation, secrets
  • Quality: Duplication, naming, test coverage

Best Practices

  1. Be systematic - Cover all relevant aspects
  2. Document strengths - Not just problems
  3. Prioritize issues - By severity and impact
  4. Consider context - Team size, timeline, constraints
  5. Provide evidence - Reference specific code

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