{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-yusufkaraaslan-skill_seekers","slug":"mcp-yusufkaraaslan-skill_seekers","name":"Skill_Seekers","type":"repo","url":"https://github.com/yusufkaraaslan/Skill_Seekers","page_url":"https://unfragile.ai/mcp-yusufkaraaslan-skill_seekers","categories":["app-builders"],"tags":["ai-tools","ast-parser","automation","claude-ai","claude-skills","code-analysis","conflict-detection","documentation","documentation-generator","github","github-scraper","mcp","mcp-server","multi-source","ocr","pdf","python","web-scraping"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_0","uri":"capability://data.processing.analysis.multi.source.documentation.scraping.with.unified.ingestion.pipeline","name":"multi-source documentation scraping with unified ingestion pipeline","description":"Extracts content from documentation websites, GitHub repositories, and PDFs through a five-phase pipeline (scrape → parse → analyze → enhance → package) that normalizes heterogeneous sources into a unified intermediate representation. Uses BFS traversal for HTML scraping, GitHub API with fallback local mode for large repos, and OCR for PDF text extraction, with automatic language detection and code block categorization across all sources.","intents":["I want to convert my documentation website into a Claude skill without manual content curation","I need to extract code examples and API references from multiple GitHub repositories at once","I want to process large PDFs with embedded code and convert them into structured skill knowledge"],"best_for":["Documentation maintainers building AI-native skill libraries","Open-source project maintainers automating skill generation from existing docs","Teams consolidating knowledge from multiple sources into unified AI skills"],"limitations":["Rate limiting on GitHub API (60 req/hour unauthenticated, 5000 authenticated) requires checkpoint/resume for large repos","PDF OCR accuracy depends on document quality; scanned PDFs with poor contrast may have extraction errors","HTML scraping via BFS may timeout on extremely large documentation sites (>10k pages) without pagination configuration","Language detection uses heuristics and may misclassify mixed-language content"],"requires":["Python 3.9+","GitHub API token (optional but recommended for higher rate limits)","Internet connectivity for web scraping and GitHub API access","For PDF processing: poppler-utils or equivalent PDF rendering library"],"input_types":["URL (documentation website)","GitHub repository URL or local path","PDF file path","Local codebase directory"],"output_types":["Structured skill JSON","SKILL.md markdown format","Vector database chunks","Platform-specific adaptor formats (Claude, Smithery, etc.)"],"categories":["data-processing-analysis","search-retrieval","web-scraping"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_1","uri":"capability://data.processing.analysis.automatic.conflict.detection.and.resolution.across.merged.sources","name":"automatic conflict detection and resolution across merged sources","description":"Detects and resolves conflicts when merging content from multiple sources (e.g., same API documented in both GitHub README and official docs site) using configurable synthesis strategies and formulas. Implements conflict scoring based on content similarity, source authority, and freshness, then applies user-defined resolution rules (prefer newest, prefer authoritative source, merge with deduplication, etc.) to produce a single canonical skill.","intents":["I'm combining docs from multiple sources and need to automatically detect duplicate content","I want to merge conflicting API documentation from different sources with a clear resolution strategy","I need to ensure my final skill has no redundant or contradictory information"],"best_for":["Teams consolidating documentation from multiple official and community sources","Maintainers managing skills across multiple platforms with overlapping content","Organizations building comprehensive skill libraries from fragmented documentation"],"limitations":["Conflict detection relies on semantic similarity; minor rewording may not trigger conflict detection","Synthesis strategies are rule-based and cannot handle nuanced conflicts requiring human judgment","No built-in conflict visualization UI; conflicts are reported in JSON/CLI output only","Authority scoring requires manual configuration; no automatic source credibility inference"],"requires":["Multiple source inputs (at least 2 sources to detect conflicts)","Configuration file specifying synthesis strategy and conflict resolution rules","Optional: API key for semantic similarity scoring (if using LLM-based conflict detection)"],"input_types":["Parsed content from multiple sources (documentation, GitHub, PDF)","Conflict resolution configuration (JSON schema)"],"output_types":["Conflict report (JSON with detected conflicts and resolution applied)","Merged skill content with conflict metadata","Resolution audit trail"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_10","uri":"capability://data.processing.analysis.pdf.scraping.with.ocr.and.text.extraction","name":"pdf scraping with ocr and text extraction","description":"Extracts text and structured content from PDF files using OCR (optical character recognition) for scanned documents and native text extraction for digital PDFs. Handles embedded images, tables, and code blocks, preserving document structure and formatting. Supports large PDFs through streaming ingestion and page-by-page processing. Automatically detects and extracts code blocks from PDF content.","intents":["I want to convert a PDF documentation file into a Claude skill","I need to extract code examples from a scanned PDF document","I want to process large PDF files without loading them entirely into memory","I need to preserve table structure and formatting from PDF content"],"best_for":["Teams converting legacy PDF documentation into AI skills","Organizations with scanned technical documentation needing digitization","Builders processing large PDF files with memory constraints"],"limitations":["OCR accuracy depends on PDF quality; scanned documents with poor contrast may have extraction errors","Table extraction is best-effort; complex table layouts may not be preserved perfectly","Large PDFs (>1000 pages) require significant processing time; no parallelization","Embedded images are extracted but not analyzed; image content is not converted to text"],"requires":["PDF file path","poppler-utils or equivalent PDF rendering library","Optional: OCR engine (Tesseract) for scanned PDFs"],"input_types":["PDF file path","Optional: OCR configuration"],"output_types":["Extracted text content","Structured content (tables, code blocks, images)","Page-by-page extraction metadata"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_11","uri":"capability://search.retrieval.llms.txt.detection.and.processing.for.documentation.sites","name":"llms.txt detection and processing for documentation sites","description":"Automatically detects and processes llms.txt files in documentation websites (a standard for exposing machine-readable documentation metadata). Extracts structured content hints, API endpoints, and documentation structure from llms.txt, using this information to optimize scraping strategy and improve content extraction. Falls back to standard BFS scraping if llms.txt is not found.","intents":["I want to leverage llms.txt metadata to improve documentation scraping","I need to detect if a documentation site supports machine-readable metadata","I want to use llms.txt hints to optimize my scraping strategy"],"best_for":["Teams scraping modern documentation sites with llms.txt support","Builders optimizing scraping efficiency for documentation with metadata","Organizations standardizing on llms.txt for AI-friendly documentation"],"limitations":["llms.txt support is optional; many documentation sites don't implement it","llms.txt format is not standardized; different sites may use different metadata structures","Fallback to BFS scraping if llms.txt is not found; no error if metadata is incomplete","llms.txt hints are advisory only; actual scraping may differ from hints"],"requires":["Documentation website URL","Optional: llms.txt path (if non-standard location)"],"input_types":["Documentation website URL"],"output_types":["Detected llms.txt metadata","Optimized scraping strategy","Fallback BFS scraping if llms.txt not found"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_12","uri":"capability://automation.workflow.unified.cli.with.workflow.orchestration.and.natural.language.invocation","name":"unified cli with workflow orchestration and natural language invocation","description":"Provides a unified command-line interface for all Skill Seekers operations (scraping, enhancement, distribution, workflow orchestration) with natural language workflow invocation through MCP integration. Supports workflow commands that chain multiple operations (e.g., scrape → enhance → package) in a single invocation. Implements argument parsing, validation, and help system for all commands.","intents":["I want to run a complete documentation-to-skill workflow from the command line","I need to chain multiple operations (scrape, enhance, package) in a single command","I want to invoke Skill Seekers workflows through natural language prompts","I need help understanding available commands and their options"],"best_for":["Developers automating skill generation through CI/CD pipelines","Teams using Skill Seekers as a command-line tool","Organizations integrating Skill Seekers into larger automation workflows"],"limitations":["CLI is Python-based; requires Python 3.9+ installation","Natural language invocation depends on MCP integration; not available in standalone CLI mode","Workflow orchestration is sequential; no parallel execution of independent operations","Error handling is CLI-based; no built-in retry logic for failed commands"],"requires":["Python 3.9+","Skill Seekers package installed (pip install skill-seekers)"],"input_types":["Command-line arguments","Configuration file (for workflow commands)","Natural language prompts (for MCP invocation)"],"output_types":["Command output (JSON, text, or structured data)","Skill artifacts","Workflow execution logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_13","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.github.actions.integration","name":"docker and kubernetes deployment with github actions integration","description":"Provides Docker containerization for Skill Seekers with pre-configured images for common use cases (scraping, enhancement, distribution). Includes Kubernetes deployment manifests and Helm charts for production-scale deployments. Integrates with GitHub Actions for automated skill generation workflows triggered by documentation changes. Supports CI/CD pipeline integration for continuous skill updates.","intents":["I want to deploy Skill Seekers in a containerized environment","I need to run Skill Seekers at scale using Kubernetes","I want to automate skill generation when my documentation changes","I need to integrate Skill Seekers into my CI/CD pipeline"],"best_for":["Teams deploying Skill Seekers in production environments","Organizations using Kubernetes for infrastructure","Projects using GitHub for version control and CI/CD"],"limitations":["Docker images are predefined; custom configurations require image rebuilding","Kubernetes deployment requires cluster setup and management expertise","GitHub Actions integration is GitHub-specific; other CI/CD platforms require custom adapters","Scaling is horizontal (multiple containers); no built-in load balancing"],"requires":["Docker installed (for containerization)","Kubernetes cluster (for K8s deployment)","GitHub repository (for GitHub Actions integration)","Helm (for Helm chart deployment)"],"input_types":["Docker configuration","Kubernetes manifests or Helm values","GitHub Actions workflow definition"],"output_types":["Docker image","Kubernetes deployment","GitHub Actions workflow execution logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_2","uri":"capability://code.generation.editing.ast.based.codebase.analysis.with.design.pattern.detection","name":"ast-based codebase analysis with design pattern detection","description":"Analyzes local codebases using abstract syntax tree (AST) parsing to extract architectural patterns, design patterns, test examples, configuration patterns, and dependency graphs. Supports multiple languages (Python, JavaScript, Go, Rust, etc.) through language-specific parsers, generates ARCHITECTURE.md documentation, extracts how-to guides from test files, and detects signal flow in game engine code (Godot). Produces structured analysis output that enriches skill content with code-level insights.","intents":["I want to automatically generate ARCHITECTURE.md from my codebase structure","I need to extract design patterns and architectural patterns from my code to document them","I want to generate how-to guides by analyzing test files and example code","I need to understand and document the dependency graph of my project"],"best_for":["Open-source maintainers automating architecture documentation generation","Teams building skills from complex codebases with multiple design patterns","Game engine developers documenting signal flow and architecture"],"limitations":["AST parsing is language-specific; unsupported languages fall back to regex-based analysis with lower accuracy","Design pattern detection uses heuristic matching and may have false positives/negatives for non-standard implementations","Large codebases (>100k lines) may require significant memory and processing time for full AST analysis","Configuration pattern extraction assumes standard naming conventions; custom config formats may not be detected"],"requires":["Local codebase directory with source files","Language-specific parser installed (tree-sitter for most languages)","Python 3.9+ for AST analysis engine"],"input_types":["Local codebase directory path","Configuration specifying which patterns to detect (design patterns, test examples, etc.)"],"output_types":["ARCHITECTURE.md file","Design pattern report (JSON)","Dependency graph (JSON/DOT format)","Test example extraction (code snippets with context)","How-to guide fragments"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_3","uri":"capability://text.generation.language.ai.powered.skill.enhancement.with.local.and.api.based.workflows","name":"ai-powered skill enhancement with local and api-based workflows","description":"Enhances raw scraped content through two pathways: local CLI-based enhancement using local LLM inference, or API-based enhancement using Claude/OpenAI APIs. Applies configurable enhancement presets (improve-clarity, add-examples, generate-summaries, etc.) to enrich skill content with better explanations, additional examples, and structured metadata. Supports streaming ingestion for large documents and checkpoint/resume for interrupted enhancement jobs.","intents":["I want to improve the clarity and completeness of my scraped documentation automatically","I need to add missing examples and use cases to my skill content","I want to generate summaries and structured metadata for my documentation","I need to enhance content without sending it to external APIs (privacy-sensitive docs)"],"best_for":["Teams enhancing documentation quality without manual editing","Organizations with privacy requirements needing local enhancement","Builders wanting to customize enhancement workflows with presets"],"limitations":["Local enhancement requires running a local LLM (Ollama, LM Studio, etc.); quality depends on model size and capability","API-based enhancement incurs per-token costs; large documentation sets can be expensive","Enhancement presets are predefined; custom enhancement logic requires code modification","Streaming ingestion adds ~200ms latency per chunk due to buffering and processing overhead","Checkpoint/resume system requires persistent storage; no built-in cloud storage integration"],"requires":["For local enhancement: Ollama or compatible local LLM server running","For API enhancement: API key for Claude (Anthropic) or OpenAI","Python 3.9+"],"input_types":["Raw skill content (SKILL.md or JSON)","Enhancement preset configuration (JSON)","Optional: custom enhancement prompts"],"output_types":["Enhanced skill content (SKILL.md or JSON)","Enhancement metadata (which sections were enhanced, by which preset)","Checkpoint files for resume capability"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_4","uri":"capability://tool.use.integration.mcp.server.integration.with.multi.agent.support","name":"mcp server integration with multi-agent support","description":"Implements a FastMCP-based server that exposes Skill Seekers capabilities as MCP tools, enabling integration with Claude and other AI agents. Supports multi-agent orchestration with automatic setup/auto-configuration, natural language workflow invocation, and unified CLI commands for scraping, enhancement, and distribution. Agents can invoke scraping, enhancement, and skill packaging workflows through natural language prompts without direct CLI interaction.","intents":["I want Claude to automatically scrape and convert my documentation into a skill","I need to orchestrate multi-step workflows (scrape → enhance → package) through natural language","I want to integrate Skill Seekers into my AI agent's tool ecosystem"],"best_for":["AI agent builders integrating Skill Seekers into multi-tool systems","Teams automating documentation-to-skill conversion through Claude","Organizations building custom AI workflows with Skill Seekers as a component"],"limitations":["MCP server requires FastMCP framework; integration with non-MCP agents requires custom adapters","Natural language workflow invocation depends on agent's ability to parse and invoke tools correctly","Multi-agent orchestration has no built-in conflict resolution if multiple agents modify the same skill","Auto-configuration may fail for non-standard project structures; manual configuration may be required"],"requires":["FastMCP framework installed","Claude API key for agent integration","Python 3.9+"],"input_types":["Natural language prompts (for agent invocation)","MCP tool calls with structured parameters"],"output_types":["MCP tool responses (JSON)","Skill artifacts","Workflow execution logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_5","uri":"capability://automation.workflow.skill.packaging.and.platform.agnostic.distribution","name":"skill packaging and platform-agnostic distribution","description":"Packages enhanced skills into platform-specific formats using a strategy pattern adaptor system. Supports distribution to Claude, Smithery registry, vector databases (for RAG), and custom platforms. Implements quality validation checks (completeness, accuracy, format compliance), chunking strategies for vector database export, and platform-specific metadata generation. Handles large documentation through router skills and hub architecture for modular skill distribution.","intents":["I want to package my skill for distribution to Claude and other platforms","I need to export my skill content to a vector database for RAG applications","I want to validate my skill meets quality standards before distribution","I need to split large documentation into modular router skills"],"best_for":["Skill library maintainers distributing to multiple platforms","Teams building RAG systems with vector database backends","Organizations managing large documentation sets requiring modular skills"],"limitations":["Platform adaptors are predefined; adding new platforms requires code modification","Quality validation rules are configurable but cannot capture domain-specific quality criteria","Vector database chunking strategies are fixed; custom chunking logic requires code changes","Router skill architecture adds complexity for large documentation; not suitable for small skills (<10k tokens)"],"requires":["Enhanced skill content (from enhancement phase)","Platform-specific API keys (for uploading to Smithery, Claude, etc.)","Optional: vector database connection (for RAG export)"],"input_types":["Enhanced skill content (SKILL.md or JSON)","Platform configuration (specifying target platforms)","Quality validation rules (JSON schema)"],"output_types":["Platform-specific skill packages","Vector database chunks (JSON with embeddings metadata)","Quality validation report","Distribution audit trail"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_6","uri":"capability://data.processing.analysis.configuration.system.with.schema.validation.and.preset.management","name":"configuration system with schema validation and preset management","description":"Provides a unified configuration schema for all Skill Seekers operations (scraping, enhancement, distribution) with JSON schema validation. Supports analysis presets (predefined configurations for common scenarios), config API service for programmatic configuration management, and private config repositories for team collaboration. Enables users to define custom configurations without code modification through declarative YAML/JSON files.","intents":["I want to define reusable configurations for my documentation scraping workflows","I need to share configurations with my team through a private repository","I want to validate my configuration before running a workflow","I need to use predefined presets for common documentation types"],"best_for":["Teams standardizing Skill Seekers workflows across projects","Organizations managing multiple skills with consistent configurations","Builders creating custom analysis presets for specific documentation types"],"limitations":["Configuration schema is fixed; extending with custom fields requires schema modification","Private config repositories require manual setup; no built-in Git integration","Preset management is file-based; no UI for creating/editing presets","Configuration validation is schema-based only; semantic validation (e.g., conflicting options) is limited"],"requires":["Configuration file (YAML or JSON)","Optional: private Git repository for config sharing"],"input_types":["Configuration file (YAML/JSON)","Preset name (for using predefined configurations)"],"output_types":["Validated configuration object","Configuration validation report","Preset list"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_7","uri":"capability://automation.workflow.caching.and.checkpoint.resume.system.for.rapid.iteration","name":"caching and checkpoint/resume system for rapid iteration","description":"Implements multi-level caching (scrape cache, parse cache, analysis cache) and checkpoint/resume system enabling interrupted workflows to resume without reprocessing completed phases. Stores intermediate results in a structured cache directory, allowing rapid iteration on enhancement and distribution phases without re-scraping. Supports dry-run mode for testing configurations without side effects.","intents":["I want to re-run enhancement on cached content without re-scraping","I need to resume a large scraping job that was interrupted","I want to test my configuration without actually scraping or enhancing","I need to iterate quickly on skill packaging without re-processing earlier phases"],"best_for":["Teams iterating on large documentation sets","Builders testing configurations before full runs","Organizations with unreliable network connections needing resume capability"],"limitations":["Cache invalidation is manual; no automatic cache expiration or staleness detection","Checkpoint/resume requires persistent storage; no built-in cloud storage integration","Cache directory can grow large for big projects; no automatic cleanup","Dry-run mode skips actual API calls but still performs local processing, so doesn't fully simulate execution"],"requires":["Persistent local storage for cache directory","Sufficient disk space (varies by project size)"],"input_types":["Cache directory path","Checkpoint identifier (for resume)"],"output_types":["Cached intermediate results","Checkpoint metadata"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_8","uri":"capability://automation.workflow.rate.limit.management.and.large.file.handling","name":"rate limit management and large file handling","description":"Implements intelligent rate limit management for GitHub API (60 req/hour unauthenticated, 5000 authenticated) with automatic backoff and retry logic. Handles large files and repositories through streaming ingestion, pagination, and file size detection. Provides rate limit status reporting and proactive warnings when approaching limits. Supports authenticated requests with token management for higher rate limits.","intents":["I want to scrape large GitHub repositories without hitting rate limits","I need to process large PDF files without memory exhaustion","I want to know when I'm approaching GitHub API rate limits","I need to resume scraping after hitting rate limits"],"best_for":["Teams scraping large or multiple GitHub repositories","Builders processing large documentation sets with API constraints","Organizations with limited API quota needing efficient rate limit usage"],"limitations":["Rate limit management is GitHub-specific; other APIs require custom implementation","Backoff strategy is exponential with fixed max wait time; may not be optimal for all scenarios","Large file streaming adds complexity; some operations may be slower than batch processing","Rate limit status is reported but not automatically optimized; users must adjust concurrency manually"],"requires":["GitHub API token (optional but recommended)","Internet connectivity for GitHub API access"],"input_types":["GitHub repository URL","API token (optional)"],"output_types":["Rate limit status report","Scraped content (streamed for large files)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-yusufkaraaslan-skill_seekers__cap_9","uri":"capability://data.processing.analysis.language.detection.and.code.extraction.with.smart.categorization","name":"language detection and code extraction with smart categorization","description":"Automatically detects programming languages in code blocks and documentation using heuristic analysis and language-specific syntax patterns. Extracts code examples with context, categorizes them by language and purpose (example, test, configuration, etc.), and enriches skill content with language-tagged code snippets. Supports 40+ programming languages with fallback to generic code handling for unknown languages.","intents":["I want to automatically categorize code examples by programming language","I need to extract and organize code snippets from mixed-language documentation","I want to enrich my skill with language-specific examples","I need to detect and handle code blocks in different languages within the same document"],"best_for":["Documentation maintainers managing multi-language projects","Teams building polyglot skills with examples in multiple languages","Organizations extracting code examples from diverse documentation sources"],"limitations":["Language detection uses heuristics and may misclassify ambiguous code (e.g., JSON vs JavaScript)","Supports 40+ languages; unsupported languages fall back to generic code handling","Code extraction assumes standard code block formatting (markdown, HTML); custom formats may not be detected","Categorization is rule-based; complex code purposes (e.g., performance optimization) may not be detected"],"requires":["Documentation content with code blocks","Optional: language detection configuration"],"input_types":["Documentation text with code blocks","Code block content (raw or formatted)"],"output_types":["Language-tagged code snippets","Code categorization report","Enriched skill content with language metadata"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","GitHub API token (optional but recommended for higher rate limits)","Internet connectivity for web scraping and GitHub API access","For PDF processing: poppler-utils or equivalent PDF rendering library","Multiple source inputs (at least 2 sources to detect conflicts)","Configuration file specifying synthesis strategy and conflict resolution rules","Optional: API key for semantic similarity scoring (if using LLM-based conflict detection)","PDF file path","poppler-utils or equivalent PDF rendering library","Optional: OCR engine (Tesseract) for scanned PDFs"],"failure_modes":["Rate limiting on GitHub API (60 req/hour unauthenticated, 5000 authenticated) requires checkpoint/resume for large repos","PDF OCR accuracy depends on document quality; scanned PDFs with poor contrast may have extraction errors","HTML scraping via BFS may timeout on extremely large documentation sites (>10k pages) without pagination configuration","Language detection uses heuristics and may misclassify mixed-language content","Conflict detection relies on semantic similarity; minor rewording may not trigger conflict detection","Synthesis strategies are rule-based and cannot handle nuanced conflicts requiring human judgment","No built-in conflict visualization UI; conflicts are reported in JSON/CLI output only","Authority scoring requires manual configuration; no automatic source credibility inference","OCR accuracy depends on PDF quality; scanned documents with poor contrast may have extraction errors","Table extraction is best-effort; complex table layouts may not be preserved perfectly","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6909033698570411,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:31.492Z","last_commit":"2026-05-03T10:51:05Z"},"community":{"stars":13254,"forks":1366,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-yusufkaraaslan-skill_seekers","compare_url":"https://unfragile.ai/compare?artifact=mcp-yusufkaraaslan-skill_seekers"}},"signature":"IzNAqKlnZT9jnhbwcrSBGkxFYAKxz6SM9lzGCkUci9Qte7qsm5tFcgng/xBiakzoX+r9ptXgN+r54DE1qxCgAw==","signedAt":"2026-06-21T02:59:58.785Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-yusufkaraaslan-skill_seekers","artifact":"https://unfragile.ai/mcp-yusufkaraaslan-skill_seekers","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-yusufkaraaslan-skill_seekers","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}