skill-seekers-reference/docs/UML_ARCHITECTURE.md

Skill Seekers Architecture

Generated 2026-03-22 | StarUML project: docs/UML/skill_seekers.mdj

Overview

Skill Seekers converts documentation from 17 source types into production-ready formats for 24+ AI platforms. The architecture follows a layered module design with 8 core modules and 5 utility modules.

Package Diagram

Core Modules (upper area):

• CLICore -- Git-style command dispatcher, entry point for all skill-seekers commands
• Scrapers -- 17 source-type extractors (web, GitHub, PDF, Word, EPUB, video, etc.)
• Adaptors -- Strategy+Factory pattern for 20+ output platforms (Claude, Gemini, OpenAI, RAG frameworks)
• Analysis -- C3.x codebase analysis pipeline (AST parsing, 10 GoF pattern detectors, guide builders)
• Enhancement -- AI-powered skill improvement (API mode + LOCAL mode, --enhance-level 0-3)
• Packaging -- Package, upload, and install skills to AI agent directories
• MCP -- FastMCP server exposing 34 tools via stdio/HTTP transport
• Sync -- Documentation change detection and re-scraping triggers

Utility Modules (lower area):

• Parsers -- CLI argument parsers (30+ SubcommandParser subclasses)
• Storage -- Cloud storage abstraction (S3, GCS, Azure)
• Embedding -- Multi-provider vector embedding generation
• Benchmark -- Performance measurement framework
• Utilities -- Shared helpers (LanguageDetector, RAGChunker, MarkdownCleaner, etc.)

Core Module Diagrams

CLICore

Entry point: skill-seekers CLI. CLIDispatcher maps subcommands to modules via COMMAND_MODULES dict. CreateCommand auto-detects source type via SourceDetector.

Scrapers

18 scraper classes implementing IScraper. Each has a main() entry point. Notable: GitHubScraper (3-stream fetcher) + GitHubToSkillConverter (builder), UnifiedScraper (multi-source orchestrator).

Adaptors

SkillAdaptor ABC with 3 abstract methods: format_skill_md(), package(), upload(). Two-level hierarchy: direct subclasses (Claude, Gemini, OpenAI, Markdown, OpenCode, RAG adaptors) and OpenAICompatibleAdaptor intermediate (MiniMax, Kimi, DeepSeek, Qwen, OpenRouter, Together, Fireworks).

Analysis (C3.x Pipeline)

UnifiedCodebaseAnalyzer controller orchestrates: CodeAnalyzer (AST, 9 languages), PatternRecognizer (10 GoF detectors via BasePatternDetector), TestExampleExtractor, HowToGuideBuilder, ConfigExtractor, SignalFlowAnalyzer, DependencyAnalyzer, ArchitecturalPatternDetector.

Enhancement

Two enhancement hierarchies: AIEnhancer (API mode, Claude API calls) and UnifiedEnhancer (C3.x pipeline enhancers). Each has specialized subclasses for patterns, test examples, guides, and configs. WorkflowEngine orchestrates multi-stage EnhancementWorkflow.

Packaging

PackageSkill delegates to adaptors for format-specific packaging. UploadSkill handles platform API uploads. InstallSkill/InstallAgent install to AI agent directories. OpenCodeSkillSplitter handles large file splitting.

MCP Server

SkillSeekerMCPServer (FastMCP) with 34 tools in 8 categories. Supporting classes: SourceManager (config CRUD), AgentDetector (environment detection), GitConfigRepo (community configs).

Sync

SyncMonitor controller schedules periodic checks via ChangeDetector (SHA-256 hashing, HTTP headers, content diffing). Notifier sends alerts when changes are found. Pydantic models: PageChange, ChangeReport, SyncConfig, SyncState.

Utility Module Diagrams

Parsers

SubcommandParser ABC with 27 subclasses -- one per CLI subcommand (Create, Scrape, GitHub, PDF, Word, EPUB, Video, Unified, Analyze, Enhance, Package, Upload, Jupyter, HTML, OpenAPI, AsciiDoc, Pptx, RSS, ManPage, Confluence, Notion, Chat, Config, Estimate, Install, Stream, Quality, SyncConfig).

Storage

BaseStorageAdaptor ABC with S3StorageAdaptor, GCSStorageAdaptor, AzureStorageAdaptor. StorageObject dataclass for file metadata.

Embedding

EmbeddingGenerator (multi-provider: OpenAI, Sentence Transformers, Voyage AI). EmbeddingPipeline coordinates provider, caching, and cost tracking. EmbeddingProvider ABC with OpenAI and Local implementations.

Benchmark

BenchmarkRunner orchestrates Benchmark instances. BenchmarkResult collects timings/memory/metrics and produces BenchmarkReport. Supporting data types: Metric, TimingResult, MemoryUsage, ComparisonReport.

Utilities

16 shared helper classes: LanguageDetector, MarkdownCleaner, RAGChunker, RateLimitHandler, ConfigManager, ConfigValidator, SkillQualityChecker, QualityAnalyzer, LlmsTxtDetector/Downloader/Parser, ConfigSplitter, ConflictDetector, IncrementalUpdater, MultiLanguageManager, StreamingIngester.

Key Design Patterns

Pattern	Where	Classes
Strategy + Factory	Adaptors	SkillAdaptor ABC + get_adaptor() factory + 20+ implementations
Strategy + Factory	Storage	BaseStorageAdaptor ABC + S3/GCS/Azure
Strategy + Factory	Embedding	EmbeddingProvider ABC + OpenAI/Local
Command	CLI	CLIDispatcher + COMMAND_MODULES lazy dispatch
Template Method	Pattern Detection	BasePatternDetector + 10 GoF detectors
Template Method	Parsers	SubcommandParser + 27 subclasses

Behavioral Diagrams

Create Pipeline Sequence

CreateCommand is a dispatcher, not a pipeline orchestrator. Flow: User → execute() → SourceDetector.detect(source) → validate_source() → _validate_arguments() → _route_to_scraper() → scraper.main(argv). The 5 phases (scrape, build_skill, enhance, package, upload) all happen inside each scraper's main() — CreateCommand only sees the exit code.

GitHub Unified Flow + C3.x

UnifiedScraper orchestrates GitHub scraping (3-stream fetch) then delegates to analyze_codebase(enhance_level) for C3.x analysis. Shows all 5 C3.x stages: PatternRecognizer (C3.1), TestExampleExtractor (C3.2), HowToGuideBuilder with examples from C3.2 (C3.3), ConfigExtractor (C3.4), and ArchitecturalPatternDetector (C3.5). Note: enhance_level is the sole AI control parameter — enhance_with_ai/ai_mode are internal to C3.x classes only.

Source Auto-Detection

Activity diagram showing source_detector.py decision tree in correct code order: file extension first (.json config, .pdf/.docx/.epub/.ipynb/.html/.pptx/etc) → video URL → os.path.isdir() (Codebase) → GitHub pattern (owner/repo or github.com URL) → http/https URL (Web) → bare domain inference → error.

MCP Tool Invocation

MCP Client (Claude Code/Cursor) → FastMCPServer (stdio/HTTP) with two invocation paths: Path A (scraping tools) uses subprocess.run(["skill-seekers", ...]), Path B (packaging/config tools) uses direct Python imports (get_adaptor(), sync_config()). Both return TextContent → JSON-RPC.

Enhancement Pipeline

--enhance-level decision flow with precise internal variable mapping: Level 0 sets ai_mode=none, skips all AI. Level ≥ 1 selects ai_mode=api (if ANTHROPIC_API_KEY set) or ai_mode=local (Claude Code CLI), then SKILL.md enhancement happens post-build via enhance_command. Level ≥ 2 enables enhance_config=True, enhance_architecture=True inside analyze_codebase(). Level 3 adds enhance_patterns=True, enhance_tests=True.

Runtime Components

Component diagram with corrected runtime dependencies. Key flows: CLI Core dispatches to Scrapers (via scraper.main(argv)) and to Adaptors (via package/upload commands). Scrapers call Codebase Analysis via analyze_codebase(enhance_level). Codebase Analysis uses C3.x Classes internally and Enhancement when level ≥ 2. MCP Server reaches Scrapers via subprocess and Adaptors via direct import.

File Locations

• StarUML project: docs/UML/skill_seekers.mdj
• Diagram exports: docs/UML/exports/*.png
• Source code: src/skill_seekers/