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Enhanced docs validation with AI-powered features. Enhanced with Context7 MCP for up-to-date documentation.
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npx skills add https://github.com/AJBcoding/claude-skill-eval --skill moai-docs-validationコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SKILL.md を表示中
Enterprise database architecture specialist with PostgreSQL 17, MySQL 8.4 LTS, MongoDB 8.0, Redis 7.4 expertise. Master connection pooling, query optimization, caching strategies, and database DevOps automation. Build scalable, resilient database systems with comprehensive monitoring and disaster recovery.
Enterprise Frontend Development with AI-powered modern architecture, Context7 integration, and intelligent component orchestration for scalable user interfaces
Enterprise-grade security expertise with production-ready patterns for OWASP Top 10 2021, zero-trust architecture, threat modeling (STRIDE, PASTA), secure SDLC, DevSecOps automation, cloud security, cryptography, identity & access management, and compliance frameworks (SOC 2, ISO 27001, GDPR, CCPA).
| name | moai-docs-validation |
| version | 4.0.0 |
| created | 2025-11-12T00:00:00.000Z |
| updated | 2025-11-12T00:00:00.000Z |
| status | stable |
| tier | specialization |
| description | Enhanced docs validation with AI-powered features. Enhanced with Context7 MCP for up-to-date documentation. |
| allowed-tools | Read, Glob, Grep, WebSearch, WebFetch, mcp__context7__resolve-library-id, mcp__context7__get-library-docs |
| primary-agent | doc-syncer |
| secondary-agents | ["alfred"] |
| keywords | ["docs","validation","auth","cd","test"] |
| tags | ["documentation"] |
| orchestration | null |
| can_resume | true |
| typical_chain_position | terminal |
| depends_on | [] |
Docs Validation
Primary Agent: doc-syncer
Secondary Agents: alfred
Version: 4.0.0
Keywords: docs, validation, auth, cd, test
📚 Content
Documentation validation ensures content accuracy, completeness, and compliance with MoAI-ADK standards. This skill covers comprehensive validation strategies for:
Key Benefits:
Rules:
- Requirement Coverage: All SPEC requirements addressed in documentation
Example - Good:
# Feature: User Authentication
---
### Level 2: Practical Implementation (Common Patterns)
📚 Content
### Section 1: Validation Framework Overview
Documentation validation ensures content accuracy, completeness, and compliance with MoAI-ADK standards. This skill covers comprehensive validation strategies for:
- **SPEC Compliance**: Verify documentation references valid SPECs and TAG chains
: Validate code examples, API signatures, configuration patterns
: Ensure all required sections present and filled
: Measure readability, coverage, translation quality
: Verify structure and content alignment across languages
:
Catch inaccurate documentation before publication
Ensure SPEC-documentation traceability
Maintain quality standards across all documents
Automate quality gate enforcement
Enable data-driven documentation improvements
Example - Good:
# Feature: User Authentication
---
Implementation
Example - Bad:
# User Authentication
---
How to Authenticate
[No SPEC reference]
[No TAG chain]
[No testing guidance]
Rules:
- Code Examples: Tested, executable, syntax-correct
- API Signatures: Match actual implementation
- Parameter Types: Accurate type annotations
- Return Values: Documented behavior matches actual behavior
- Configuration: All config examples work in practice
Validation Pattern:
def validate_code_examples(self, doc_path: Path) -> List[ValidationError]:
"""Verify code examples are syntactically correct"""
errors = []
# Extract all ```language code blocks
code_blocks = self._extract_code_blocks(doc_path)
for block in code_blocks:
# Verify syntax
syntax_errors = self._check_syntax(block.code, block.language)
if syntax_errors:
errors.append(ValidationError(
file=doc_path,
line=block.line_number,
message=f"Invalid {block.language} syntax",
details=syntax_errors
))
return errors
Metrics:
- Readability Score: 60-100 (Flesch-Kincaid readability)
- Coverage: 80%+ of specification requirements
- Code Example Ratio: 1 example per 300 words (target)
- Link Validity: 100% of internal/external links valid
- Translation Completeness: 100% structure alignment across languages
- Image Optimization: All images <500KB, proper alt text
Quality Score Calculation:
def calculate_quality_score(self, doc_path: Path) -> float:
"""Calculate documentation quality (0-100)"""
scores = {
'spec_compliance': self._check_spec_compliance(doc_path), # 25%
'content_accuracy': self._validate_content_accuracy(doc_path), # 25%
'completeness': self._check_completeness(doc_path), # 20%
'readability': self._calculate_readability(doc_path), # 15%
'formatting': self._check_formatting(doc_path), # 15%
}
weights = {
'spec_compliance': 0.25,
'content_accuracy': 0.25,
'completeness': 0.20,
'readability': 0.15,
'formatting': 0.15,
}
total_score = sum(scores[k] * weights[k] for k in scores)
return round(total_score, 1)
TAG Chain Validation:
Valid Chains:
Chain Rules:
- No broken links (referenced TAGs must exist)
Verification Script Pattern:
class TAGVerifier:
def __init__(self, project_root: Path):
self.project_root = project_root
self.spec_docs = {}
self.test_docs = {}
self.code_refs = {}
self.doc_refs = {}
def verify_chain(self, spec_id: str) -> ValidationResult:
result = ValidationResult(spec_id)
# Check SPEC exists
if spec_id not in self.spec_docs:
return result
# Check TEST exists
if spec_id not in self.test_docs:
# Check CODE references
if spec_id not in self.code_refs:
# Check DOC exists
if spec_id not in self.doc_refs:
return result
Translation Consistency Checks:
class MultilingualValidator:
def validate_structure_consistency(self) -> List[str]:
"""Ensure all languages have same document structure"""
issues = []
# Get Korean file structure (source)
ko_structure = self._get_file_structure("docs/src/ko")
# Compare with other languages
for lang in ["en", "ja", "zh"]:
lang_structure = self._get_file_structure(f"docs/src/{lang}")
if ko_structure != lang_structure:
missing = set(ko_structure) - set(lang_structure)
extra = set(lang_structure) - set(ko_structure)
if missing:
issues.append(f"[{lang}] Missing files: {missing}")
if extra:
issues.append(f"[{lang}] Extra files: {extra}")
return issues
def validate_translation_quality(self, lang: str) -> float:
"""Score translation completeness (0-100)"""
ko_files = set(self._list_files("docs/src/ko", "*.md"))
lang_files = set(self._list_files(f"docs/src/{lang}", "*.md"))
translated = len(ko_files & lang_files)
translation_ratio = (translated / len(ko_files)) * 100
return round(translation_ratio, 1)
GitHub Actions Integration Pattern:
# Pre-commit validation
python3 .moai/scripts/validate_docs.py --mode pre-commit
# Pull request validation
python3 .moai/scripts/validate_docs.py --mode pr --files-changed
# Full documentation audit
python3 .moai/scripts/validate_docs.py --mode full --report comprehensive
Quality Gate Configuration:
# .moai/quality-gates.yml
documentation:
spec_compliance:
min_score: 90
required: true
action: block_merge
content_accuracy:
min_score: 85
required: true
action: block_merge
link_validity:
broken_links_allowed: 0
required: true
action: block_merge
multilingual_consistency:
max_missing_translations: 0
required: true
action: warning
Automated Reports:
def generate_validation_report(self, output_format: str = "markdown") -> str:
"""Generate comprehensive validation report"""
report = []
# Summary
report.append("# Documentation Validation Report")
report.append(f"Generated: {datetime.now()}")
report.append("")
# Quality Scores
report.append("## Quality Metrics")
for doc, score in self.quality_scores.items():
status = "✅" if score >= 85 else "⚠️" if score >= 70 else "❌"
report.append(f"{status} {doc}: {score}/100")
# Issues Summary
report.append("## Issues Found")
report.append(f"- Errors: {len(self.errors)}")
report.append(f"- Warnings: {len(self.warnings)}")
# Detailed Issues
for error in self.errors:
report.append(f"❌ {error.file}:{error.line} - {error.message}")
return "\n".join(report)
✅ Validation Checklist
Note: Advanced patterns for complex scenarios.
Coming soon: Deep dive into expert-level usage.
Must-Have:
Recommended:
Security:
When to Use Context7 for This Skill:
This skill benefits from Context7 when:
Example Usage:
# Fetch latest documentation
from moai_adk.integrations import Context7Helper
helper = Context7Helper()
docs = await helper.get_docs(
library_id="/org/library",
topic="docs",
tokens=5000
)
Relevant Libraries:
| Library | Context7 ID | Use Case |
|---|---|---|
| [Library 1] | /org/lib1 | [When to use] |
When to use moai-docs-validation:
Start
├─ Need docs?
│ ├─ YES → Use this skill
│ └─ NO → Consider alternatives
└─ Complex scenario?
├─ YES → See Level 3
└─ NO → Start with Level 1
Prerequisite Skills:
Complementary Skills:
Next Steps:
Metadata
skill_id: moai-docs-validation
skill_name: Documentation Validation & Quality Assurance
version: 1.0.0
created_date: 2025-11-10
updated_date: 2025-11-10
language: english
word_count: 1400
triggers:
- keywords: [documentation validation, content verification, quality assurance, spec compliance, tag verification, documentation audit, quality metrics]
- contexts: [docs-validation, @docs:validate, quality-audit, spec-compliance]
agents:
- docs-auditor
- quality-gate
- spec-builder
freedom_level: high
context7_references:
- url: "https://en.wikipedia.org/wiki/Software_quality"
topic: "Software Quality Metrics"
- url: "https://github.com/moai-adk/moai-adk"
topic: "MoAI-ADK SPEC Standards"
v4.0.0 (2025-11-12)
Generated with: MoAI-ADK Skill Factory v4.0
Last Updated: 2025-11-12
Maintained by: Primary Agent (doc-syncer)