pal-analyze

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.

First seen Apr 24, 2026

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Package contents

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skill md SKILL.md 3,458 B

docs SUMMARY.md 283 B

History

Apr 24, 2026 First seen on skills.sh

Sep 9, 2026 First recorded snapshot · 1 installs

SKILL.md

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

Type	Focus
`architecture`	System design, patterns, modularity
`performance`	Bottlenecks, optimization opportunities
`security`	Vulnerabilities, auth issues
`quality`	Code smells, maintainability
`general`	Comprehensive overview

Output Formats

Format	Description
`summary`	High-level overview
`detailed`	In-depth analysis
`actionable`	Prioritized recommendations

Required Parameters

Parameter	Type	Description
`step`	string	Analysis narrative
`step_number`	int	Current step
`total_steps`	int	Estimated total
`nextsteprequired`	bool	More analysis needed?
`findings`	string	Discoveries and insights

Optional Parameters

Parameter	Type	Description
`analysis_type`	enum	architecture/performance/security/quality/general
`output_format`	enum	summary/detailed/actionable
`confidence`	enum	exploring → certain
`relevant_files`	list	Files under analysis
`files_checked`	list	All files examined
`issues_found`	list	Issues with severity
`continuation_id`	string	Continue session
`model`	string	Override 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

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