{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-scaffold","slug":"scaffold","name":"Scaffold","type":"repo","url":"https://github.com/Beer-Bears/scaffold","page_url":"https://unfragile.ai/scaffold","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-scaffold__cap_0","uri":"capability://data.processing.analysis.multi.language.source.code.parsing.with.ast.extraction","name":"multi-language source code parsing with ast extraction","description":"Scaffold parses source code across multiple programming languages using language-specific parsers (tree-sitter based) to extract Abstract Syntax Trees (ASTs). The system decomposes code into structural entities (files, classes, methods, functions) and captures their syntactic relationships, enabling downstream graph generation. This approach preserves code semantics rather than relying on regex or simple text analysis.","intents":["I need to understand the structural composition of a codebase without manual annotation","I want to extract all function definitions, class hierarchies, and method signatures automatically","I need to identify code entities and their syntactic relationships for context injection into LLMs"],"best_for":["Teams building AI agents that need precise code understanding","Developers maintaining large polyglot codebases (Python, JavaScript, Java, Go, etc.)","Organizations automating code analysis and documentation generation"],"limitations":["Parser accuracy depends on language support; unsupported or legacy languages fall back to basic text parsing","AST extraction adds processing latency proportional to codebase size (~100ms per 10K LOC)","Requires language-specific parser bindings; custom DSLs or domain-specific languages may not parse correctly"],"requires":["Source code in supported language (Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, etc.)","Tree-sitter parser bindings installed for target languages","Minimum 512MB RAM for AST caching during large codebase parsing"],"input_types":["source code files (raw text)","directory paths to codebase roots","git repository references"],"output_types":["Abstract Syntax Trees (JSON/structured format)","Entity metadata (name, type, location, signature)","Relationship tuples (parent-child, call-graph edges)"],"categories":["data-processing-analysis","code-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_1","uri":"capability://memory.knowledge.dual.database.knowledge.graph.persistence.postgresql.neo4j","name":"dual-database knowledge graph persistence (postgresql + neo4j)","description":"Scaffold persists parsed code structure into two complementary databases: PostgreSQL stores relational metadata (files, entities, timestamps, ownership) while Neo4j maintains the knowledge graph with semantic relationships (inheritance, method calls, imports, dependencies). This polyglot persistence strategy optimizes for both structured queries (SQL) and graph traversal operations (Cypher), enabling efficient context retrieval at scale. The system maintains bidirectional sync between databases to ensure consistency.","intents":["I need to query code relationships using graph traversal (e.g., 'find all callers of this function')","I want to store and retrieve structured metadata about code entities efficiently","I need to maintain a persistent, queryable representation of codebase architecture across sessions"],"best_for":["Teams deploying Scaffold as a persistent service for multiple AI agents","Organizations with large codebases (>100K LOC) requiring sub-second context retrieval","Development teams needing both relational queries and graph-based dependency analysis"],"limitations":["Dual-database architecture adds operational complexity; requires managing two separate database instances and sync logic","Graph traversal queries on very deep dependency chains (>10 levels) may incur 500ms+ latency","PostgreSQL and Neo4j must be kept in sync; inconsistencies can occur during partial failures or concurrent updates","Storage overhead ~2-3x raw codebase size due to graph representation and metadata duplication"],"requires":["PostgreSQL 12+ with psycopg2 driver","Neo4j 4.4+ with neo4j-driver Python package","Network connectivity between application and both database instances","Minimum 2GB combined database storage for typical 100K LOC codebase"],"input_types":["Parsed AST entities and relationships","Code entity metadata (name, type, location, signature)","Relationship tuples (call edges, inheritance, imports)"],"output_types":["Structured entity records (SQL rows)","Graph traversal results (Neo4j node/relationship sets)","Relationship chains (dependency paths, call stacks)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_10","uri":"capability://search.retrieval.codebase.search.with.semantic.and.structural.filtering","name":"codebase search with semantic and structural filtering","description":"Scaffold provides a search interface that combines keyword matching with semantic and structural filtering. Users can search for code entities by name, type, or relationship (e.g., 'find all classes that inherit from BaseController'). The search engine leverages the knowledge graph to understand entity types, relationships, and context, enabling more precise results than simple text search. Results can be filtered by entity type, location, or relationship properties.","intents":["I need to find code entities by name, type, or relationship","I want to search for code patterns or architectural structures","I need to locate all implementations of an interface or all subclasses of a base class"],"best_for":["Developers navigating large codebases","Teams building code search or IDE integration features","Organizations automating code discovery and analysis"],"limitations":["Search performance depends on graph size; very large graphs (>100K entities) may have slow query times","Keyword matching is exact or prefix-based; fuzzy matching is not supported","Relationship-based search requires knowledge of graph schema; users must understand entity types and relationship names","Search results are limited to indexed code; external dependencies are not searchable"],"requires":["Populated Neo4j knowledge graph with entity metadata","Search query parser (Cypher or custom DSL)","Entity type and relationship definitions"],"input_types":["Search keywords (entity names, types)","Relationship filters (inheritance, calls, imports)","Entity type filters (class, function, module)"],"output_types":["Ranked list of matching entities","Entity metadata (name, type, location, relationships)","Search result snippets (code context)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_2","uri":"capability://automation.workflow.incremental.codebase.indexing.with.change.detection","name":"incremental codebase indexing with change detection","description":"Scaffold monitors source code changes (via file system watchers or git hooks) and incrementally updates the knowledge graph without re-parsing the entire codebase. The system detects modified, added, and deleted files, re-parses only affected code, and updates both PostgreSQL and Neo4j with delta changes. This approach avoids expensive full re-indexing and enables near-real-time graph synchronization as developers commit code.","intents":["I want the knowledge graph to stay synchronized with code changes without manual re-indexing","I need fast feedback loops where AI agents see updated code context within seconds of commits","I want to avoid re-parsing unchanged code to reduce indexing latency and resource consumption"],"best_for":["Development teams using Scaffold in continuous integration pipelines","Organizations with active codebases where code changes frequently (multiple commits per day)","Teams deploying Scaffold as a long-running service that must stay synchronized with live repositories"],"limitations":["Change detection relies on file system events or git hooks; may miss changes if watchers are disabled or git hooks fail","Incremental updates add complexity; bugs in delta logic can cause graph inconsistencies (e.g., stale references to deleted entities)","Large refactorings (e.g., moving 100+ files) may trigger cascading updates that negate incremental benefits","Requires persistent state tracking (file hashes, timestamps) which adds ~10-50MB overhead per 100K LOC"],"requires":["File system watcher library (watchdog for Python) or git post-commit hooks","Git repository access or file system monitoring permissions","Persistent state store for tracking file hashes and last-indexed timestamps"],"input_types":["File system change events (created, modified, deleted)","Git commit metadata (changed files, commit hash)","Delta code snippets (only modified portions)"],"output_types":["Updated entity records in PostgreSQL","Modified graph nodes/relationships in Neo4j","Change logs (audit trail of indexing operations)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_3","uri":"capability://search.retrieval.context.aware.code.entity.retrieval.via.graph.queries","name":"context-aware code entity retrieval via graph queries","description":"Scaffold provides a query interface (Cypher for Neo4j, SQL for PostgreSQL) to retrieve code entities and their relationships based on semantic context. Queries can traverse dependency graphs (e.g., 'find all functions called by this method'), retrieve related code (e.g., 'find all classes in the same module'), or identify architectural patterns (e.g., 'find all implementations of this interface'). Results are ranked by relevance and formatted as structured context suitable for LLM injection.","intents":["I need to retrieve all code entities related to a specific function or class for context injection","I want to understand the dependency graph around a code entity (what calls it, what it calls)","I need to find similar code patterns or related implementations across the codebase"],"best_for":["AI agents and LLMs requiring precise, context-aware code understanding","Developers building code search or navigation tools","Teams automating code review, refactoring, or impact analysis"],"limitations":["Query performance degrades with graph depth; traversing >10 levels of dependencies may timeout","Relevance ranking is heuristic-based (edge weights, node centrality); may not match human intuition for complex architectures","Requires manual query construction for custom relationship types; no natural language query interface","Results are limited to entities in the indexed codebase; external dependencies or dynamically loaded code are not included"],"requires":["Neo4j instance with populated knowledge graph","PostgreSQL instance with entity metadata","Cypher or SQL query knowledge for custom queries","Context formatting logic to convert query results to LLM-friendly format"],"input_types":["Entity identifiers (function name, class name, file path)","Query parameters (relationship type, depth limit, relevance threshold)","Cypher or SQL query strings"],"output_types":["Ranked list of related code entities","Relationship chains (dependency paths)","Formatted context strings (code snippets, signatures, documentation)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_4","uri":"capability://tool.use.integration.model.context.protocol.mcp.integration.for.ai.agent.communication","name":"model context protocol (mcp) integration for ai agent communication","description":"Scaffold implements the Model Context Protocol (MCP) standard, providing a standardized interface through which AI agents and LLMs can request code context without direct database access. The MCP layer exposes Scaffold's knowledge graph as a set of tools/resources (e.g., 'get_entity_context', 'find_related_code', 'get_dependency_graph') that agents can invoke via standard MCP messages. This abstraction decouples agents from Scaffold's internal architecture and enables multi-agent coordination.","intents":["I want AI agents to query code context using a standardized protocol rather than custom APIs","I need to provide controlled, read-only access to code context without exposing database credentials","I want to enable multiple AI agents to coordinate context requests through a single interface"],"best_for":["Teams deploying Scaffold as a service for multiple AI agents (Devin, Claude, custom agents)","Organizations requiring standardized, protocol-based integration between code analysis and AI systems","Development teams building agent-based code automation workflows"],"limitations":["MCP is a relatively new standard; not all LLM providers have native MCP support (requires adapter/wrapper)","MCP message serialization adds ~50-100ms latency per request compared to direct API calls","Tool discovery and schema validation are synchronous; large tool sets (>100 tools) may cause startup delays","No built-in caching of MCP responses; repeated queries for the same context incur full latency"],"requires":["MCP server implementation (included in Scaffold)","MCP client support in AI agent/LLM (Claude, Devin, or custom implementation)","Network connectivity between agent and Scaffold MCP server","MCP protocol version compatibility (Scaffold targets MCP 1.0+)"],"input_types":["MCP tool invocation messages (JSON-RPC format)","Tool parameters (entity names, query filters, context depth)","Resource requests (code snippets, metadata)"],"output_types":["MCP tool results (structured JSON)","Code context (formatted snippets, signatures, relationships)","Metadata (file paths, line numbers, entity types)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_5","uri":"capability://text.generation.language.living.knowledge.graph.with.automatic.documentation.generation","name":"living knowledge graph with automatic documentation generation","description":"Scaffold generates and maintains living documentation by extracting code structure, relationships, and patterns from the knowledge graph and synthesizing them into human-readable documentation. Unlike static docs, this documentation is automatically updated whenever code changes are indexed, ensuring it stays synchronized with the actual codebase. The system can generate architecture diagrams, dependency maps, API documentation, and module overviews directly from graph data.","intents":["I want to generate accurate, up-to-date documentation without manual effort","I need to visualize codebase architecture and dependency relationships automatically","I want to identify and document architectural patterns and design decisions from code structure"],"best_for":["Teams with large codebases where manual documentation maintenance is unsustainable","Organizations onboarding new developers who need rapid architectural understanding","Projects requiring compliance documentation that must stay synchronized with code"],"limitations":["Generated documentation captures structure but not intent; comments and docstrings are not automatically extracted or synthesized","Documentation generation adds processing overhead (~500ms-2s per 10K LOC) during indexing","Diagram generation for very large graphs (>1000 nodes) may produce cluttered, hard-to-read visualizations","No built-in support for custom documentation templates; output format is fixed"],"requires":["Populated Neo4j knowledge graph with entity relationships","Documentation generation templates (Markdown, HTML, or custom format)","Graph visualization library (e.g., Graphviz, D3.js) for diagram generation"],"input_types":["Knowledge graph nodes and relationships","Entity metadata (names, types, locations)","Relationship types (inheritance, calls, imports)"],"output_types":["Markdown documentation files","HTML documentation pages","SVG/PNG architecture diagrams","JSON documentation metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_6","uri":"capability://memory.knowledge.codebase.aware.context.injection.for.llm.prompts","name":"codebase-aware context injection for llm prompts","description":"Scaffold provides utilities to automatically inject relevant code context into LLM prompts based on the task at hand. Given a user query or code location, the system retrieves related entities from the knowledge graph and formats them as context (code snippets, signatures, relationships, documentation) that is prepended to the LLM prompt. This approach enables LLMs to understand codebase-specific patterns, conventions, and architecture without requiring the entire codebase in the prompt.","intents":["I want LLMs to understand my codebase's architecture and patterns without sending the entire codebase","I need to automatically select the most relevant code context for a given task","I want to reduce LLM token usage by injecting only necessary context"],"best_for":["Teams using LLMs for code generation, refactoring, or analysis tasks","Developers building AI-assisted coding tools that need codebase awareness","Organizations optimizing LLM API costs by reducing token usage"],"limitations":["Context selection is heuristic-based; may include irrelevant code or miss important context","Token budget for context is fixed; very large codebases may require aggressive filtering","Context formatting adds latency (~100-500ms) before LLM invocation","No feedback loop to improve context selection based on LLM output quality"],"requires":["Populated knowledge graph with entity relationships","Context retrieval queries (Cypher or SQL)","LLM API integration (OpenAI, Anthropic, etc.)","Prompt formatting templates"],"input_types":["User query or task description","Code location (file path, line number, entity name)","Context depth/token budget parameters"],"output_types":["Formatted LLM prompt with injected context","Context metadata (source entities, relevance scores)","Token count estimates"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_7","uri":"capability://data.processing.analysis.multi.level.code.entity.abstraction.files.classes.methods.functions","name":"multi-level code entity abstraction (files, classes, methods, functions)","description":"Scaffold represents code at multiple levels of abstraction—files, modules, classes, methods, functions, and variables—each with their own graph nodes and relationships. This hierarchical representation enables context retrieval at different granularities: asking for 'all methods in a class' vs. 'all functions in a file' vs. 'all callers of a specific method'. The system maintains parent-child relationships and scope information, enabling precise context selection based on the level of detail needed.","intents":["I need to understand code at different levels of abstraction (file, class, method)","I want to retrieve context at the appropriate granularity for a given task","I need to identify scope and visibility relationships between code entities"],"best_for":["Teams analyzing complex codebases with deep hierarchies (e.g., large OOP systems)","Developers building code navigation or refactoring tools","Organizations automating code review or impact analysis at multiple levels"],"limitations":["Multi-level abstraction adds graph complexity; queries must specify entity type to avoid ambiguity","Scope resolution is language-specific; some languages (e.g., Python) have complex scoping rules that may not be fully captured","Nested entity relationships can create deep graph paths; traversal queries may timeout for deeply nested structures","Entity deduplication across abstraction levels requires careful handling (e.g., method overloading, nested classes)"],"requires":["Language-specific parser that extracts hierarchical entity relationships","Graph schema supporting multiple entity types and parent-child relationships","Query logic to handle entity type filtering and scope resolution"],"input_types":["Parsed AST with hierarchical structure","Entity metadata (name, type, scope, location)","Parent-child relationship tuples"],"output_types":["Hierarchical entity lists (files > classes > methods)","Scoped entity references (fully qualified names)","Relationship chains at multiple levels"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_8","uri":"capability://planning.reasoning.dependency.graph.analysis.and.impact.assessment","name":"dependency graph analysis and impact assessment","description":"Scaffold analyzes code dependencies (imports, function calls, class inheritance, module references) and constructs a dependency graph that enables impact analysis. Given a code change, the system can identify all downstream dependents (what code depends on this entity) and upstream dependencies (what this entity depends on). This enables developers and AI agents to understand the blast radius of changes and identify affected code without manual analysis.","intents":["I need to understand what code will be affected by a change to this function or class","I want to identify all dependencies of a module to understand its requirements","I need to detect circular dependencies or problematic dependency patterns"],"best_for":["Teams performing refactoring or large-scale code changes","Developers building impact analysis or change management tools","Organizations automating code review and dependency validation"],"limitations":["Dependency analysis is static; does not capture runtime dependencies or dynamic imports (e.g., reflection, eval)","Circular dependency detection requires expensive graph cycle detection algorithms; may timeout on very large graphs","External dependencies (third-party libraries) are not fully analyzed; only direct imports are captured","Transitive dependency chains can be very long; impact analysis may identify hundreds of affected entities"],"requires":["Parsed code with import/call relationships","Neo4j graph with dependency edges","Graph traversal algorithms (BFS, DFS) for impact analysis"],"input_types":["Entity identifiers (function, class, module names)","Dependency type filters (imports, calls, inheritance)","Traversal depth limits"],"output_types":["Downstream dependents (entities that depend on the given entity)","Upstream dependencies (entities that the given entity depends on)","Dependency chains (paths from source to target)","Circular dependency lists"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-scaffold__cap_9","uri":"capability://planning.reasoning.architectural.pattern.detection.and.code.smell.identification","name":"architectural pattern detection and code smell identification","description":"Scaffold analyzes the knowledge graph to detect common architectural patterns (e.g., MVC, dependency injection, factory pattern) and identify code smells (e.g., circular dependencies, god classes, unused code). The system uses graph-based heuristics (e.g., node degree, clustering coefficients, path lengths) to identify suspicious patterns that may indicate design issues. Results are surfaced as warnings or insights that developers and AI agents can act upon.","intents":["I want to identify architectural patterns in my codebase automatically","I need to detect code smells and design issues without manual review","I want to understand if my codebase follows common architectural conventions"],"best_for":["Teams performing code quality assessment or architectural reviews","Organizations automating code smell detection in CI/CD pipelines","Developers building code analysis or refactoring recommendation tools"],"limitations":["Pattern detection is heuristic-based; false positives and false negatives are common","Patterns are language-agnostic; language-specific idioms may be misclassified","Detection rules are fixed; no support for custom pattern definitions","Graph-based heuristics may not align with human intuition about code quality"],"requires":["Populated knowledge graph with entity relationships and metrics","Pattern detection algorithms (graph clustering, centrality analysis)","Configurable thresholds for pattern/smell detection"],"input_types":["Knowledge graph nodes and relationships","Entity metrics (degree, clustering coefficient, betweenness centrality)","Pattern/smell definitions (heuristic rules)"],"output_types":["Detected patterns (list of entities matching pattern)","Code smell warnings (entity, smell type, severity)","Architectural insights (pattern prevalence, design metrics)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Source code in supported language (Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, etc.)","Tree-sitter parser bindings installed for target languages","Minimum 512MB RAM for AST caching during large codebase parsing","PostgreSQL 12+ with psycopg2 driver","Neo4j 4.4+ with neo4j-driver Python package","Network connectivity between application and both database instances","Minimum 2GB combined database storage for typical 100K LOC codebase","Populated Neo4j knowledge graph with entity metadata","Search query parser (Cypher or custom DSL)","Entity type and relationship definitions"],"failure_modes":["Parser accuracy depends on language support; unsupported or legacy languages fall back to basic text parsing","AST extraction adds processing latency proportional to codebase size (~100ms per 10K LOC)","Requires language-specific parser bindings; custom DSLs or domain-specific languages may not parse correctly","Dual-database architecture adds operational complexity; requires managing two separate database instances and sync logic","Graph traversal queries on very deep dependency chains (>10 levels) may incur 500ms+ latency","PostgreSQL and Neo4j must be kept in sync; inconsistencies can occur during partial failures or concurrent updates","Storage overhead ~2-3x raw codebase size due to graph representation and metadata duplication","Search performance depends on graph size; very large graphs (>100K entities) may have slow query times","Keyword matching is exact or prefix-based; fuzzy matching is not supported","Relationship-based search requires knowledge of graph schema; users must understand entity types and relationship names","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=scaffold","compare_url":"https://unfragile.ai/compare?artifact=scaffold"}},"signature":"+wfMr42bnutv2zIOmVLo5aFPVm81R7ezs8MTo2NNYE8aJpvehL3z+duimHdjjBaZ9QdDF88bagrZ8WOkzQMPCw==","signedAt":"2026-06-22T09:14:41.811Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/scaffold","artifact":"https://unfragile.ai/scaffold","verify":"https://unfragile.ai/api/v1/verify?slug=scaffold","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"}}