{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-yusufkaraaslan--skill_seekers","slug":"yusufkaraaslan--skill_seekers","name":"Skill_Seekers","type":"skill","url":"https://skillseekersweb.com/","page_url":"https://unfragile.ai/yusufkaraaslan--skill_seekers","categories":["automation"],"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-yusufkaraaslan--skill_seekers__cap_0","uri":"capability://data.processing.analysis.multi.source.documentation.scraping.with.unified.pipeline","name":"multi-source documentation scraping with unified pipeline","description":"Ingests documentation from websites (via BFS HTML traversal), GitHub repositories (API or local mode), PDFs (OCR-enabled), and local codebases through a five-phase unified pipeline. Each scraper implements language detection and smart categorization, feeding normalized content into a conflict detection system that identifies overlapping information across sources and applies synthesis strategies to merge or deduplicate content.","intents":["I need to extract API documentation from a website, GitHub repo, and PDF simultaneously without writing separate parsers","I want to automatically detect when multiple sources describe the same concept and merge them intelligently","I need to handle large documentation sites without hitting rate limits or memory constraints"],"best_for":["Teams building Claude skills from fragmented documentation across multiple platforms","Open-source maintainers consolidating docs from website, GitHub, and PDF sources","Developers automating knowledge base ingestion for AI agents"],"limitations":["HTML scraping via BFS traversal may miss dynamically-loaded content (JavaScript-rendered pages not supported)","GitHub API mode subject to rate limits (60 req/hr unauthenticated, 5000 req/hr authenticated); local mode requires git clone","PDF OCR accuracy depends on document quality; scanned PDFs with poor resolution may produce garbled text","Conflict detection uses heuristic synthesis strategies, not semantic understanding — may incorrectly merge unrelated content with similar names"],"requires":["Python 3.9+","GitHub API token (optional, for authenticated API scraping)","Local git installation (for GitHub local mode)","Internet connectivity for website scraping","Sufficient disk space for caching (varies by documentation size)"],"input_types":["website URLs (HTTP/HTTPS)","GitHub repository URLs or local paths","PDF file paths","Local codebase directories"],"output_types":["normalized markdown content","structured metadata (language, category, source origin)","conflict resolution reports","merged/deduplicated content"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_1","uri":"capability://data.processing.analysis.conflict.detection.and.intelligent.content.synthesis","name":"conflict detection and intelligent content synthesis","description":"Analyzes scraped content from multiple sources to identify overlapping information using configurable synthesis strategies and formulas. The system detects when different sources describe the same concept, API, or code pattern and applies merge rules (union, intersection, priority-based selection) to produce deduplicated output. Conflict metadata is tracked throughout the pipeline for transparency and debugging.","intents":["I want to merge API documentation from a website and GitHub README without manual deduplication","I need to detect when two sources contradict each other and choose the authoritative version","I want to understand which source contributed each piece of information in the final skill"],"best_for":["Documentation teams managing multiple versions of the same content","AI skill builders consolidating overlapping documentation sources","Quality assurance workflows requiring conflict transparency"],"limitations":["Conflict detection is heuristic-based (string matching, structural similarity) — semantic conflicts (e.g., contradictory API behavior descriptions) are not detected","Synthesis strategies are rule-based, not learned — custom conflict resolution requires manual configuration","No built-in versioning or history tracking — conflicts are resolved once and merged state is final","Performance degrades with very large content sets (>10k unique concepts) due to pairwise comparison overhead"],"requires":["Multiple content sources (minimum 2) to detect conflicts","Unified configuration schema defining synthesis strategies","Python 3.9+"],"input_types":["normalized markdown content from multiple sources","metadata tags (source origin, content type, language)"],"output_types":["merged content with conflict resolution applied","conflict report (what was merged, which source won)","metadata tracking source attribution per content block"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_10","uri":"capability://automation.workflow.docker.and.kubernetes.deployment.with.github.actions","name":"docker and kubernetes deployment with github actions","description":"Provides containerized deployment via Docker with Kubernetes support (Helm charts) for running Skill Seekers as a service. Includes GitHub Actions workflow for automated skill generation on repository changes, enabling CI/CD integration. Supports environment-based configuration and secrets management for secure deployment.","intents":["I want to run Skill Seekers as a containerized service in Kubernetes","I need to automatically generate skills whenever my documentation changes","I want to deploy Skill Seekers with proper secrets management and configuration"],"best_for":["Teams deploying Skill Seekers as a microservice","Organizations with CI/CD pipelines wanting to automate skill generation","Developers running Skill Seekers in Kubernetes clusters"],"limitations":["Docker image size is large (>500MB) due to dependencies — may impact deployment speed","Kubernetes deployment requires cluster setup and maintenance — not suitable for simple use cases","GitHub Actions integration is GitHub-specific — other CI/CD platforms require custom workflows","Secrets management relies on platform-specific mechanisms (GitHub Secrets, Kubernetes Secrets) — requires careful configuration","Resource requirements vary by documentation size — no automatic scaling based on load"],"requires":["Docker or Kubernetes cluster","GitHub repository (for GitHub Actions)","Sufficient compute resources (CPU, memory, disk)"],"input_types":["Docker configuration (Dockerfile, docker-compose.yml)","Kubernetes manifests (Helm charts)","GitHub Actions workflow files"],"output_types":["Docker image","Kubernetes deployments","GitHub Actions workflow runs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_11","uri":"capability://code.generation.editing.multi.language.code.extraction.with.language.detection","name":"multi-language code extraction with language detection","description":"Automatically detects programming languages in documentation and code snippets, then extracts and categorizes code examples by language. Supports syntax highlighting, language-specific parsing, and intelligent categorization of code blocks (examples, configuration, tests). Enables language-aware skill generation where code examples are organized by language preference.","intents":["I want to automatically extract code examples from documentation and organize them by language","I need to detect the programming language of code snippets without manual tagging","I want to generate language-specific skills (Python skill, JavaScript skill, etc.) from polyglot documentation"],"best_for":["Teams documenting libraries that support multiple languages","Developers building polyglot skills for frameworks like Django, Express, etc.","Organizations standardizing code example extraction"],"limitations":["Language detection is heuristic-based (file extension, syntax patterns) — may misidentify languages with similar syntax","Code extraction assumes standard markdown code blocks — custom documentation formats may not be recognized","Language-specific parsing requires parser implementation for each language — not all languages are supported","Categorization (example vs config vs test) is rule-based — may misclassify ambiguous code blocks","Syntax highlighting is optional — requires additional dependencies"],"requires":["Python 3.9+","Markdown or HTML documentation with code blocks"],"input_types":["documentation with code blocks (markdown, HTML)","source code files"],"output_types":["extracted code examples organized by language","language detection metadata","categorized code blocks (example, config, test)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_12","uri":"capability://data.processing.analysis.llms.txt.detection.and.processing.for.documentation.discovery","name":"llms.txt detection and processing for documentation discovery","description":"Detects and processes llms.txt files (machine-readable documentation metadata) during website scraping to improve documentation discovery and structure. llms.txt files provide hints about documentation organization, language, and content type, enabling smarter scraping decisions. Integrates with BFS traversal to prioritize high-value documentation pages.","intents":["I want to automatically discover documentation structure from llms.txt files","I need to prioritize important documentation pages during scraping","I want to respect documentation metadata hints for better content extraction"],"best_for":["Teams scraping websites that provide llms.txt files","Developers building documentation discovery systems","Organizations standardizing documentation metadata"],"limitations":["llms.txt support is optional — websites without llms.txt fall back to standard BFS traversal","llms.txt format is not standardized — different websites may use different metadata structures","Metadata hints are advisory — actual content may not match metadata descriptions","llms.txt discovery requires HTTP access to /.well-known/llms.txt — may be blocked by robots.txt or authentication"],"requires":["Python 3.9+","Website with llms.txt file (optional)"],"input_types":["website URLs","llms.txt files (JSON or YAML format)"],"output_types":["documentation metadata from llms.txt","prioritized scraping order based on metadata","content type hints for better extraction"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_13","uri":"capability://safety.moderation.quality.validation.and.completeness.checks","name":"quality validation and completeness checks","description":"Implements automated quality validation checks on generated skills, including file presence verification, metadata completeness, content structure validation, and semantic quality assessment. Produces detailed quality reports with actionable recommendations for improvement. Supports custom validation rules and quality thresholds.","intents":["I want to ensure my generated skills meet quality standards before distribution","I need to identify missing sections or incomplete metadata","I want to validate skill structure and format before uploading to registries"],"best_for":["Teams maintaining skill quality standards","Organizations with strict documentation requirements","Developers validating skills before distribution"],"limitations":["Quality checks are rule-based — semantic quality (accuracy, clarity) is not assessed","Validation rules are predefined — custom quality criteria require code modification","Quality thresholds are configurable but not learned — no adaptive quality standards","Validation is static — does not check for broken links or outdated content","Performance degrades with very large skills (>10MB) due to full content scanning"],"requires":["Python 3.9+","Generated skill package"],"input_types":["skill package files","metadata","configuration"],"output_types":["quality report with pass/fail status","list of validation errors and warnings","recommendations for improvement"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_2","uri":"capability://code.generation.editing.ast.based.code.analysis.and.pattern.extraction","name":"ast-based code analysis and pattern extraction","description":"Parses source code across multiple languages (Python, JavaScript, TypeScript, Go, Rust, etc.) using AST (Abstract Syntax Tree) parsing to extract design patterns, test examples, configuration patterns, dependency graphs, and architectural insights. The C3.x codebase analysis features include design pattern detection, test example extraction, how-to guide generation, and ARCHITECTURE.md generation from code structure alone, without requiring manual documentation.","intents":["I want to automatically extract design patterns and architectural decisions from a codebase without reading source files manually","I need to generate test examples and usage patterns from existing test suites","I want to create dependency graphs and architectural diagrams from code structure"],"best_for":["Open-source maintainers generating skills from their codebases","Teams documenting legacy code without existing documentation","Developers building AI agents that need to understand codebase architecture"],"limitations":["AST parsing requires syntactically valid code — malformed or incomplete code will fail to parse","Pattern detection is rule-based, not ML-based — may miss domain-specific or novel patterns","Language support is limited to implemented parsers (Python, JavaScript, TypeScript, Go, Rust); other languages fall back to regex-based extraction","Test example extraction assumes standard testing frameworks (pytest, Jest, etc.) — custom test runners may not be recognized","Dependency graph analysis only captures explicit imports — runtime dependencies or plugin systems are not detected"],"requires":["Valid source code in supported language","Python 3.9+","AST parser library for target language"],"input_types":["source code files (.py, .js, .ts, .go, .rs, etc.)","test files","configuration files"],"output_types":["extracted design patterns (Singleton, Factory, Observer, etc.)","test examples with usage context","dependency graphs (JSON or DOT format)","ARCHITECTURE.md with pattern descriptions","configuration pattern catalog"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_3","uri":"capability://text.generation.language.ai.powered.content.enhancement.with.local.and.api.modes","name":"ai-powered content enhancement with local and api modes","description":"Enhances scraped content using Claude AI to improve clarity, add examples, generate missing sections, and enrich metadata. Supports both local enhancement (CLI-based, using local Claude models) and API-based enhancement (using Claude API with configurable presets). Enhancement workflows are composable and can be chained together, with caching to avoid redundant API calls and support for batch processing of large documentation sets.","intents":["I want to automatically improve documentation clarity and add examples without manual editing","I need to generate missing sections (quickstart, troubleshooting) from existing content","I want to enrich metadata (tags, categories) for better skill discoverability"],"best_for":["Teams with limited documentation resources wanting to improve content quality","Developers building skills from minimal or poorly-written documentation","Organizations standardizing documentation format across multiple projects"],"limitations":["API-based enhancement requires Claude API key and incurs per-token costs (varies by model and content size)","Local enhancement requires compatible local model (e.g., Claude running locally via Ollama) — not all models support all enhancement presets","Enhancement is non-deterministic — same content may produce slightly different results on different runs","Large documentation sets may hit API rate limits or timeout; requires checkpoint/resume support","Enhancement presets are predefined — custom enhancement logic requires code modification"],"requires":["Python 3.9+","Claude API key (for API-based enhancement) OR local Claude model (for local enhancement)","Internet connectivity (for API-based enhancement)"],"input_types":["normalized markdown content","metadata (language, category, source)"],"output_types":["enhanced markdown with improved clarity","generated sections (examples, quickstart, troubleshooting)","enriched metadata (tags, categories, difficulty level)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_4","uri":"capability://automation.workflow.skill.packaging.and.platform.agnostic.distribution","name":"skill packaging and platform-agnostic distribution","description":"Converts processed content into Claude skills using a standardized SKILL.md format and distributes to multiple AI platforms (Claude, OpenAI, Anthropic, etc.) through platform adaptor pattern. Implements chunking for vector database export, quality validation checks, and platform-specific formatting. Supports uploading to skill registries (Smithery, Claude Plugin marketplace) and installing directly into AI agents.","intents":["I want to package documentation as a Claude skill that can be imported into Claude","I need to export the same skill to multiple AI platforms without reformatting","I want to validate skill quality before distribution (completeness, format, metadata)"],"best_for":["Developers creating reusable skills for Claude and other AI platforms","Teams distributing documentation as installable AI artifacts","Organizations managing skill libraries across multiple AI platforms"],"limitations":["Platform adaptor pattern requires custom implementation for each target platform — new platforms require code changes","Chunking strategy is fixed (configurable chunk size but not strategy) — may not be optimal for all use cases","Quality validation checks are rule-based (file presence, metadata completeness) — semantic quality is not assessed","Vector database export requires external vector store (e.g., Pinecone, Weaviate) — no built-in vector storage","Skill registry upload requires authentication credentials for each platform"],"requires":["Python 3.9+","Platform API credentials (for uploading to registries)","Vector database credentials (for vector export)"],"input_types":["processed markdown content","metadata (title, description, version, author)","reference files (examples, code snippets)"],"output_types":["SKILL.md formatted file","chunked content for vector database","platform-specific formatted packages","distribution manifest with metadata"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_5","uri":"capability://tool.use.integration.mcp.server.integration.with.multi.agent.support","name":"mcp server integration with multi-agent support","description":"Exposes Skill Seekers functionality as an MCP (Model Context Protocol) server using FastMCP framework, enabling Claude and other AI agents to invoke scraping, enhancement, and packaging workflows programmatically. Supports multi-agent orchestration with auto-configuration, natural language workflow examples, and tool registry with native bindings for OpenAI, Anthropic, and Ollama function-calling APIs.","intents":["I want Claude to automatically scrape and convert documentation to skills without manual CLI invocation","I need to orchestrate complex workflows (scrape → enhance → package → distribute) through natural language commands","I want to integrate Skill Seekers into an agentic system where multiple AI agents collaborate"],"best_for":["Developers building AI agents that need to create skills dynamically","Teams automating documentation-to-skill conversion through natural language","Organizations deploying Skill Seekers as a service for multiple AI platforms"],"limitations":["MCP server requires FastMCP framework and Python 3.9+ — not available for other languages","Multi-agent orchestration is stateless — no built-in persistence for workflow state across agent invocations","Natural language workflow examples are predefined — custom workflows require manual tool composition","Tool registry bindings are limited to OpenAI, Anthropic, and Ollama — other platforms require custom adaptor implementation","MCP server adds ~200ms latency per tool invocation due to serialization and network overhead"],"requires":["Python 3.9+","FastMCP framework","MCP client (Claude, OpenAI, Anthropic, Ollama, etc.)","Network connectivity between MCP server and client"],"input_types":["natural language commands (e.g., 'scrape and convert https://example.com to a Claude skill')","structured tool parameters (URLs, file paths, configuration)"],"output_types":["tool execution results (skill packages, metadata)","workflow status and progress updates","error messages and debugging information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_6","uri":"capability://automation.workflow.unified.configuration.schema.with.validation.and.presets","name":"unified configuration schema with validation and presets","description":"Defines a unified configuration schema that applies across all scraping, enhancement, and distribution workflows. Supports configuration validation, analysis presets (predefined configurations for common use cases), config API service for remote configuration management, and private config repositories for team collaboration. Configuration is composable and can be extended with custom fields.","intents":["I want to define scraping and enhancement rules once and reuse them across multiple projects","I need to validate configuration before running workflows to catch errors early","I want to share configurations across my team without duplicating settings"],"best_for":["Teams standardizing documentation-to-skill conversion across multiple projects","Organizations managing large numbers of skills with consistent quality standards","Developers building custom workflows on top of Skill Seekers"],"limitations":["Configuration schema is JSON/YAML-based — no GUI for configuration management","Validation is schema-based (type checking, required fields) — semantic validation (e.g., conflicting options) is not supported","Config API service requires separate deployment and authentication — not included in CLI-only installation","Private config repositories require git access and authentication — no built-in access control","Configuration changes require pipeline restart — no hot-reload support"],"requires":["Python 3.9+","JSON or YAML configuration file","Git access (for private config repositories)"],"input_types":["JSON or YAML configuration files","configuration presets (predefined templates)"],"output_types":["validated configuration object","configuration validation report (errors, warnings)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_7","uri":"capability://automation.workflow.caching.checkpoint.and.resume.with.streaming.ingestion","name":"caching, checkpoint, and resume with streaming ingestion","description":"Implements multi-level caching (content cache, API response cache) to avoid redundant scraping and API calls. Supports checkpoint/resume functionality to pause and resume long-running workflows without losing progress. Enables streaming ingestion for large documentation sets, processing content incrementally rather than loading everything into memory. Integrates with cloud storage for incremental updates and distributed processing.","intents":["I want to resume a scraping job that failed halfway through without starting over","I need to process very large documentation sites without running out of memory","I want to avoid re-scraping content that hasn't changed since the last run"],"best_for":["Teams processing large documentation sets (>1GB) with limited resources","Developers running long-running workflows on unreliable networks","Organizations with incremental documentation updates"],"limitations":["Caching requires persistent storage (disk or cloud) — no in-memory-only caching","Checkpoint format is implementation-specific — checkpoints from different versions may not be compatible","Streaming ingestion requires careful memory management — not all enhancement operations support streaming","Cloud storage integration requires credentials and network connectivity — adds complexity for offline workflows","Cache invalidation is time-based (TTL) — no content-hash-based invalidation"],"requires":["Python 3.9+","Persistent storage (local disk or cloud storage)","Sufficient disk space for cache (varies by documentation size)"],"input_types":["checkpoint files (JSON format)","cache metadata (timestamps, content hashes)"],"output_types":["checkpoint files for resuming workflows","cache statistics (hit rate, size, age)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_8","uri":"capability://automation.workflow.rate.limit.management.and.dry.run.testing","name":"rate limit management and dry-run testing","description":"Implements intelligent rate limit management for GitHub API and other external services, with automatic backoff, retry logic, and quota tracking. Provides dry-run mode to test workflows without making actual API calls or writing files, enabling safe validation before production runs. Includes detailed logging and progress reporting for transparency.","intents":["I want to scrape GitHub without hitting rate limits or getting blocked","I need to test my configuration without actually scraping or making API calls","I want to understand how much API quota my workflow will consume before running it"],"best_for":["Developers scraping large GitHub repositories with API rate limits","Teams validating configurations before production deployment","Organizations monitoring API quota usage"],"limitations":["Rate limit management is service-specific — requires custom implementation for new services","Backoff strategy is exponential with fixed parameters — not adaptive to actual rate limit headers","Dry-run mode skips actual API calls — cannot validate that API credentials are valid","Progress reporting is log-based — no real-time UI or dashboard","Quota tracking is approximate — actual usage may differ due to API implementation details"],"requires":["Python 3.9+","API credentials (for rate limit tracking)"],"input_types":["workflow configuration","API credentials"],"output_types":["rate limit status (remaining quota, reset time)","dry-run report (estimated API calls, file writes)","detailed logs with timestamps"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-yusufkaraaslan--skill_seekers__cap_9","uri":"capability://automation.workflow.router.skills.and.hub.architecture.for.large.documentation","name":"router skills and hub architecture for large documentation","description":"Handles very large documentation sets (>10k pages) by implementing router skills that delegate to specialized sub-skills, and hub architecture that organizes skills hierarchically. Includes page estimation to predict documentation size before scraping, enabling proactive chunking and routing decisions. Supports skill composition where multiple skills can be combined into a single unified skill.","intents":["I need to convert a massive documentation site (>10k pages) into a manageable skill structure","I want to organize related skills into a hub with intelligent routing","I need to estimate how large a skill will be before scraping"],"best_for":["Teams documenting large frameworks or platforms (Django, Kubernetes, etc.)","Organizations managing skill libraries with hundreds of related skills","Developers building hierarchical skill structures"],"limitations":["Router skills add indirection — may increase latency for skill lookups","Hub architecture requires manual skill organization — no automatic hierarchical clustering","Page estimation is heuristic-based (URL count, average page size) — actual size may differ significantly","Skill composition is manual — no automatic merging of related skills","Router logic is rule-based — no learned routing strategies"],"requires":["Python 3.9+","Large documentation set (>1000 pages recommended for router skills)"],"input_types":["documentation URLs or repository paths","router configuration (routing rules, skill organization)"],"output_types":["router skill with delegation logic","sub-skills organized hierarchically","page estimation report"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","GitHub API token (optional, for authenticated API scraping)","Local git installation (for GitHub local mode)","Internet connectivity for website scraping","Sufficient disk space for caching (varies by documentation size)","Multiple content sources (minimum 2) to detect conflicts","Unified configuration schema defining synthesis strategies","Docker or Kubernetes cluster","GitHub repository (for GitHub Actions)","Sufficient compute resources (CPU, memory, disk)"],"failure_modes":["HTML scraping via BFS traversal may miss dynamically-loaded content (JavaScript-rendered pages not supported)","GitHub API mode subject to rate limits (60 req/hr unauthenticated, 5000 req/hr authenticated); local mode requires git clone","PDF OCR accuracy depends on document quality; scanned PDFs with poor resolution may produce garbled text","Conflict detection uses heuristic synthesis strategies, not semantic understanding — may incorrectly merge unrelated content with similar names","Conflict detection is heuristic-based (string matching, structural similarity) — semantic conflicts (e.g., contradictory API behavior descriptions) are not detected","Synthesis strategies are rule-based, not learned — custom conflict resolution requires manual configuration","No built-in versioning or history tracking — conflicts are resolved once and merged state is final","Performance degrades with very large content sets (>10k unique concepts) due to pairwise comparison overhead","Docker image size is large (>500MB) due to dependencies — may impact deployment speed","Kubernetes deployment requires cluster setup and maintenance — not suitable for simple use cases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3629766555223779,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.15,"quality":0.25,"ecosystem":0.1,"match_graph":0.45,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:56:56.344Z","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=yusufkaraaslan--skill_seekers","compare_url":"https://unfragile.ai/compare?artifact=yusufkaraaslan--skill_seekers"}},"signature":"VQ5JkMQ0QJ0CRy7hokEViGT0fZLRP3wR08cIISPGXtu6DF+Tr5EyEU7jj7s2q5nxELC0aMcWYeW94cH4OUpBBw==","signedAt":"2026-06-21T09:15:19.984Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/yusufkaraaslan--skill_seekers","artifact":"https://unfragile.ai/yusufkaraaslan--skill_seekers","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}