{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-study8677--antigravity-workspace-template","slug":"study8677--antigravity-workspace-template","name":"antigravity-workspace-template","type":"mcp","url":"https://github.com/study8677/antigravity-workspace-template","page_url":"https://unfragile.ai/study8677--antigravity-workspace-template","categories":["mcp-servers","app-builders"],"tags":["agentic-ai","agentic-coding","ai-agents","ai-coding","ai-ide","ai-workspace","claude-code","claude-code-template","code-search","codex","codex-cli","cursor-ide","developer-tools","gemini-cli","google-antigravity","llm-tools","mcp-server","prompt-engineering","rag","windsurf"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-study8677--antigravity-workspace-template__cap_0","uri":"capability://automation.workflow.artifact.first.cognitive.architecture.injection.via.cli","name":"artifact-first cognitive architecture injection via cli","description":"A lightweight command-line tool (ag init) that scaffolds cognitive architecture files (.cursorrules, CLAUDE.md, .antigravity/rules.md, AGENTS.md) into any project directory without modifying existing code. This approach encodes agent behavior as declarative files rather than IDE plugins, enabling universal compatibility across Cursor, Claude Code, Windsurf, VS Code + Copilot, and other AI development environments. The CLI generates a standardized project structure with zero configuration required.","intents":["Initialize a new AI agent project with production-grade cognitive architecture in seconds","Migrate existing codebases to agentic workflows without vendor lock-in to a specific IDE","Enable multiple AI IDEs to work with the same agent configuration simultaneously","Establish consistent agent behavior patterns across teams using declarative rules files"],"best_for":["Teams building AI agents across heterogeneous IDE environments","Developers seeking vendor-agnostic agent frameworks","Organizations migrating from single-IDE to multi-IDE AI development workflows"],"limitations":["Requires manual file editing to customize cognitive architecture — no GUI configuration builder","IDE-specific features (e.g., Cursor's native agent capabilities) may not fully integrate with declarative rules","File-based configuration can become unwieldy for complex multi-agent systems with 50+ rules"],"requires":["Python 3.8+","Git repository or any project directory","Write permissions to target directory"],"input_types":["project directory path","optional configuration parameters"],"output_types":["generated .cursorrules file","generated CLAUDE.md entry point",".antigravity/ directory with rules and configuration","AGENTS.md multi-agent manifest"],"categories":["automation-workflow","agent-scaffolding"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_1","uri":"capability://tool.use.integration.zero.config.tool.discovery.and.execution.from.python.modules","name":"zero-config tool discovery and execution from python modules","description":"Automatically discovers Python functions in src/tools/ directory and registers them as callable tools without explicit configuration. The runtime introspects function signatures, docstrings, and type hints to generate tool schemas compatible with Claude, Codex, and other LLM function-calling APIs. Tools are executed in isolated sandbox environments with automatic input validation and error handling. This eliminates boilerplate tool registration code and enables rapid tool development.","intents":["Add new tools to an agent without writing tool registration or schema definition code","Ensure tools are automatically available to all agent instances without manual configuration","Execute untrusted or experimental tools in sandboxed environments to prevent system compromise","Generate accurate tool schemas from Python type hints for LLM function calling"],"best_for":["Rapid prototyping teams building agents with frequently changing tool sets","Security-conscious teams requiring sandboxed tool execution","Developers unfamiliar with tool registration patterns in LLM frameworks"],"limitations":["Tool discovery only works for Python modules — no support for shell scripts, JavaScript, or compiled binaries without wrapper functions","Sandbox execution adds ~50-200ms latency per tool invocation compared to direct function calls","Complex type hints (e.g., Union types, generics) may not translate cleanly to LLM function schemas","No built-in rate limiting or quota management for tool execution"],"requires":["Python 3.8+","Tools must be Python functions in src/tools/ directory","Type hints on function parameters (recommended for schema generation)","Docstrings for tool descriptions (recommended)"],"input_types":["Python function definitions with type hints","Function docstrings describing behavior"],"output_types":["LLM-compatible tool schemas (JSON)","Tool execution results with error handling","Sandbox execution logs"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_10","uri":"capability://automation.workflow.configuration.management.with.environment.variable.substitution.and.validation","name":"configuration management with environment variable substitution and validation","description":"Provides a centralized configuration system that supports environment variable substitution, type validation, and schema-based configuration validation. Configuration can be defined in .antigravity/config.json, environment variables, or Python code. The system validates configuration against a schema to catch errors early and provides helpful error messages. Environment variables are substituted at runtime, enabling configuration to vary across environments (development, staging, production) without code changes. Configuration is loaded at agent startup and can be accessed by all components.","intents":["Manage agent configuration across multiple environments without code changes","Validate configuration at startup to catch errors before agent execution","Use environment variables for sensitive configuration (API keys, credentials)","Share configuration across multiple agents and components"],"best_for":["Teams deploying agents across multiple environments (dev, staging, prod)","Organizations with strict configuration management requirements","Projects where configuration changes frequently"],"limitations":["Configuration schema must be manually defined — no auto-generation from code","Environment variable substitution only works for string values — complex types require custom parsing","No built-in configuration versioning or rollback mechanism","Configuration validation happens at startup — runtime configuration changes are not validated"],"requires":["Python 3.8+",".antigravity/config.json or environment variables","Optional: JSON schema for configuration validation"],"input_types":["configuration files (JSON)","environment variables","configuration schema (JSON schema)"],"output_types":["validated configuration object","configuration validation errors","environment-specific configuration"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_11","uri":"capability://planning.reasoning.skill.system.for.composable.agent.capabilities","name":"skill system for composable agent capabilities","description":"Enables definition of reusable skills (in SKILLS.md or skill modules) that encapsulate common agent capabilities (e.g., 'code-review', 'test-generation', 'documentation-writing'). Skills are composed of tool sets, prompts, and execution patterns that can be combined to create specialized agents. Skills can be enabled or disabled per agent, allowing the same agent framework to be customized for different use cases. This enables rapid agent specialization without code duplication.","intents":["Create specialized agents (code reviewer, test writer, documenter) without duplicating agent logic","Reuse common agent capabilities across multiple agents","Enable agents to dynamically enable/disable capabilities based on task requirements","Build a library of reusable agent skills that teams can share"],"best_for":["Teams building multiple specialized agents with overlapping capabilities","Organizations wanting to create a library of reusable agent skills","Projects where agent capabilities need to be dynamically enabled/disabled"],"limitations":["Skill composition can become complex with many interdependent skills","No built-in mechanism to detect and resolve skill conflicts or overlaps","Skill documentation must be manually maintained","Skills are defined in SKILLS.md or Python modules — no visual skill composition tool"],"requires":["Python 3.8+","SKILLS.md file or skill modules defining available skills","Tool definitions for each skill"],"input_types":["skill definitions (SKILLS.md or Python modules)","skill composition (which skills to enable)","skill parameters"],"output_types":["specialized agent with selected skills","skill execution logs","skill composition validation"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_12","uri":"capability://automation.workflow.docker.based.deployment.with.containerized.agent.runtime","name":"docker-based deployment with containerized agent runtime","description":"Provides Docker configuration and deployment scripts that containerize the agent runtime, enabling deployment to cloud platforms (AWS, GCP, Azure) or on-premises infrastructure. The Docker image includes the Python runtime, agent framework, tools, and dependencies. Deployment scripts handle environment variable injection, volume mounting for persistent storage, and networking configuration. This enables agents to be deployed as microservices or serverless functions without manual infrastructure setup.","intents":["Deploy agents to cloud platforms without manual infrastructure configuration","Run agents in containerized environments for isolation and resource management","Enable agents to be deployed as microservices or serverless functions","Simplify agent deployment across development, staging, and production environments"],"best_for":["Teams deploying agents to cloud platforms (AWS, GCP, Azure)","Organizations with containerization and Kubernetes experience","Projects requiring scalable agent deployment"],"limitations":["Docker configuration requires understanding of containerization concepts","Container images can be large (500MB+) if dependencies are not optimized","Debugging containerized agents is more complex than local debugging","Persistent storage requires external volumes or databases — no built-in persistence"],"requires":["Docker or container runtime","Docker Compose (optional, for multi-container deployments)","Cloud platform account (AWS, GCP, Azure) for deployment","Understanding of container networking and volume mounting"],"input_types":["Dockerfile configuration","deployment scripts","environment variables for deployment"],"output_types":["Docker image","container runtime","deployment logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_13","uri":"capability://code.generation.editing.local.development.workflow.with.hot.reload.and.debugging","name":"local development workflow with hot-reload and debugging","description":"Provides a local development environment with hot-reload capability that automatically restarts the agent when code changes are detected. Includes debugging support with breakpoints, step-through execution, and variable inspection. The development workflow supports running agents locally with full access to filesystem and tools, enabling rapid iteration and testing. Development mode includes verbose logging and error traces to aid debugging.","intents":["Develop and test agents locally before deployment","Iterate rapidly on agent logic with automatic reload on code changes","Debug agent behavior with breakpoints and step-through execution","Test agent interactions with tools and external systems locally"],"best_for":["Individual developers building and testing agents","Teams developing agents with frequent code changes","Debugging complex agent behavior or tool interactions"],"limitations":["Hot-reload may not work correctly with all Python modules (e.g., C extensions)","Local development has full filesystem access — no sandbox isolation","Debugging multi-agent systems is complex due to concurrent execution","Local development may not accurately simulate production environment"],"requires":["Python 3.8+","Development dependencies (pytest, debugpy, etc.)","Text editor or IDE with Python debugging support"],"input_types":["agent code","tool definitions","test cases"],"output_types":["agent execution logs","debugging output","test results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_2","uri":"capability://planning.reasoning.think.act.reflect.agent.execution.loop.with.memory.management","name":"think-act-reflect agent execution loop with memory management","description":"Implements a core cognitive cycle (Think → Act → Reflect) in agent.py that decomposes tasks into planning phases, tool execution phases, and reflection phases. The agent maintains conversation history with recursive summarization via memory.py to handle long-running sessions without token overflow. The Think phase uses chain-of-thought reasoning to decompose tasks; the Act phase executes tools and observes results; the Reflect phase evaluates outcomes and adjusts strategy. This cycle repeats until task completion or max iterations.","intents":["Execute complex multi-step tasks that require planning, tool use, and adaptive strategy adjustment","Maintain coherent agent behavior across long conversation histories without losing context","Enable agents to reason about their own actions and correct course when tools fail","Implement autonomous agents that can work on tasks for hours without human intervention"],"best_for":["Teams building autonomous agents for code generation, data analysis, or system administration","Applications requiring multi-step reasoning with tool use and error recovery","Long-running agent sessions where token budget is a constraint"],"limitations":["Recursive summarization can lose fine-grained details from early conversation history","Think-Act-Reflect cycle adds 2-5 LLM calls per task step, increasing latency and cost compared to direct tool execution","No built-in mechanism to detect and break infinite loops or circular reasoning patterns","Reflection quality depends heavily on LLM capability — weaker models may not effectively evaluate their own actions"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible provider)","API key for chosen LLM provider","Memory storage backend (file-based or external database)"],"input_types":["task description (natural language)","conversation history (messages with roles)","available tools (schemas)"],"output_types":["task completion status","final result or artifact","execution trace with reasoning steps","summarized conversation history"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_3","uri":"capability://planning.reasoning.multi.agent.swarm.orchestration.with.role.based.task.delegation","name":"multi-agent swarm orchestration with role-based task delegation","description":"Enables definition and coordination of multiple specialized agents (defined in AGENTS.md) that can delegate tasks to each other based on role and capability. The framework provides a multi-agent pipeline that routes tasks to appropriate agents, manages inter-agent communication, and aggregates results. Each agent maintains its own memory and tool set while sharing a common knowledge hub. This architecture supports hierarchical task decomposition where complex problems are broken into sub-tasks assigned to specialized agents.","intents":["Decompose complex problems into specialized sub-tasks assigned to agents with different capabilities","Coordinate multiple agents working on related tasks with shared context and knowledge","Build systems where agents can request help from other agents when encountering problems outside their expertise","Scale agent workloads across multiple specialized agents rather than overloading a single agent"],"best_for":["Large-scale automation projects requiring multiple specialized agents (e.g., code review agent, testing agent, deployment agent)","Teams building agent systems for complex domains (e.g., software development, data analysis, system administration)","Organizations needing to partition agent responsibilities by domain expertise"],"limitations":["Inter-agent communication adds latency — each delegation requires LLM calls to route and coordinate","No built-in load balancing or queue management for high-concurrency scenarios","Debugging multi-agent systems is significantly more complex than single-agent systems","Agent coordination logic must be explicitly defined in AGENTS.md — no automatic role inference"],"requires":["Python 3.8+","AGENTS.md manifest defining agent roles and capabilities","Multiple LLM API keys if agents use different providers","Shared knowledge hub or context store for inter-agent communication"],"input_types":["task description","AGENTS.md manifest with agent definitions","agent capability descriptions"],"output_types":["task completion status","results from multiple agents","execution trace showing task delegation","aggregated knowledge from all agents"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_4","uri":"capability://memory.knowledge.infinite.memory.engine.with.recursive.conversation.summarization","name":"infinite memory engine with recursive conversation summarization","description":"Manages long-running conversation history using recursive summarization (implemented in memory.py) that compresses old messages into progressively higher-level summaries while preserving recent context in full detail. The system maintains a conversation tree where leaf nodes are recent messages and parent nodes are summaries of their children. When conversation length exceeds a token budget threshold, the oldest messages are recursively summarized and replaced with their summaries. This enables agents to maintain coherent context across conversations spanning thousands of messages without token overflow.","intents":["Enable agents to work on long-running tasks (hours or days) without losing context or hitting token limits","Preserve important details from early conversation history while compressing less critical information","Maintain conversation coherence across multiple summarization cycles","Support agents that need to reference decisions or context from hours earlier in a session"],"best_for":["Long-running autonomous agents (code generation, data analysis, system administration)","Applications where conversation history is valuable for debugging or audit trails","Teams building agents that need to work on projects spanning multiple days"],"limitations":["Recursive summarization can lose fine-grained details or nuance from early conversation history","Summarization quality depends on LLM capability — weaker models may produce lossy summaries","Each summarization cycle requires LLM API calls, adding cost and latency","No mechanism to selectively preserve important messages from being summarized","Summarized messages cannot be edited or corrected — only new messages can be added"],"requires":["Python 3.8+","LLM API access for summarization (OpenAI, Anthropic, or compatible)","Memory storage backend (file-based or database)","Token counting library (tiktoken or equivalent) to track conversation length"],"input_types":["conversation messages with roles and content","token budget threshold","summarization parameters (compression ratio, summary length)"],"output_types":["compressed conversation history","summary nodes with references to original messages","token count estimates"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_5","uri":"capability://tool.use.integration.universal.tool.protocol.with.mcp.server.integration","name":"universal tool protocol with mcp server integration","description":"Provides a unified interface for integrating tools from multiple sources: local Python functions (via zero-config discovery), Model Context Protocol (MCP) servers, and external APIs. The framework translates between different tool schema formats (Python type hints, MCP schemas, OpenAI function calling) and provides a common execution interface. MCP servers are configured in .antigravity/mcp.json and automatically discovered and registered. This enables agents to use tools from Claude's ecosystem, custom MCP servers, and local Python functions through a single unified API.","intents":["Use Claude's built-in MCP tools (filesystem, web search, etc.) alongside custom local tools","Integrate third-party MCP servers (e.g., GitHub, Slack, databases) without custom adapter code","Build agents that can seamlessly use tools from multiple sources without knowing their origin","Enable tool composition where one tool's output can be piped to another tool's input"],"best_for":["Teams building agents that need to integrate with multiple external systems (GitHub, Slack, databases, APIs)","Organizations adopting MCP as a standard tool integration protocol","Developers building tool ecosystems that need to support multiple schema formats"],"limitations":["MCP server configuration requires manual setup in .antigravity/mcp.json — no auto-discovery of MCP servers","Tool schema translation between formats can introduce incompatibilities or lose metadata","MCP server failures are not automatically handled — requires explicit error handling in agent logic","No built-in tool composition or chaining — agents must explicitly call tools in sequence"],"requires":["Python 3.8+","MCP server binaries or Docker containers for external tools",".antigravity/mcp.json configuration file","Network access to MCP servers (if remote)"],"input_types":["MCP server configuration (JSON)","tool schema definitions (Python type hints or MCP schemas)","tool invocation requests with parameters"],"output_types":["unified tool schemas","tool execution results","error messages with fallback suggestions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_6","uri":"capability://safety.moderation.sandbox.execution.environment.for.untrusted.tools","name":"sandbox execution environment for untrusted tools","description":"Executes tools in isolated sandbox environments (using containerization or process isolation) to prevent untrusted or experimental tools from compromising the host system. The sandbox provides a restricted filesystem, limited network access, and resource quotas (CPU, memory, disk). Tool execution is monitored for policy violations (e.g., attempts to access /etc/passwd) and can be terminated if limits are exceeded. This enables agents to safely execute user-provided tools or experimental code without risk to the host system.","intents":["Execute user-provided or untrusted tools without risking host system compromise","Run experimental code in isolation to test behavior before deploying to production","Enforce resource limits on tool execution to prevent denial-of-service attacks","Audit tool execution for security violations and policy breaches"],"best_for":["Multi-tenant SaaS platforms where users can provide custom tools","Security-conscious teams running agents in production environments","Organizations with strict compliance requirements (SOC 2, HIPAA, etc.)"],"limitations":["Sandbox execution adds 50-200ms latency per tool invocation compared to in-process execution","Sandbox configuration is complex and requires deep understanding of container/process isolation","Some tools may not work in sandboxed environments due to missing dependencies or restricted system calls","Sandbox escape vulnerabilities are possible — sandbox is not a guarantee of security","Monitoring and logging sandbox execution can generate large volumes of data"],"requires":["Python 3.8+","Docker or container runtime (for container-based sandboxing)","Linux kernel with cgroup support (for process-based sandboxing)","Sandbox configuration file defining resource limits and policies"],"input_types":["tool code (Python, shell script, or binary)","tool input parameters","sandbox policy configuration (resource limits, filesystem restrictions)"],"output_types":["tool execution results","sandbox execution logs","policy violation alerts","resource usage metrics"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_7","uri":"capability://data.processing.analysis.knowledge.hub.with.codebase.scanning.and.convention.extraction","name":"knowledge hub with codebase scanning and convention extraction","description":"Implements a multi-agent pipeline (ag-refresh command) that scans project codebases to automatically generate conventions.md and structure.md files. The pipeline uses specialized agents to analyze code patterns, extract architectural conventions, identify project structure, and generate documentation. The knowledge hub serves as a shared context store for all agents in the system, enabling them to understand project conventions without explicit configuration. Generated files are stored in .context/ directory and automatically loaded as context for all agent interactions.","intents":["Automatically generate project documentation from codebase analysis without manual effort","Extract and enforce architectural conventions across a codebase","Provide agents with project context (structure, conventions, patterns) without manual configuration","Keep project documentation in sync with actual codebase as it evolves"],"best_for":["Teams with large codebases that need to maintain up-to-date documentation","Organizations adopting agents for code generation and need to enforce project conventions","Projects where architectural conventions are implicit in code and need to be made explicit"],"limitations":["Codebase scanning can be slow for very large projects (1M+ lines of code)","Extracted conventions may be incomplete or inaccurate if code patterns are inconsistent","Generated documentation requires manual review and editing to ensure accuracy","No built-in mechanism to detect and resolve conflicting conventions across different parts of codebase","Scanning requires LLM API calls for each analysis phase, adding cost"],"requires":["Python 3.8+","LLM API access for codebase analysis","Git repository with project code","Write permissions to .context/ directory"],"input_types":["project codebase (source files)","project structure (directory layout)","optional seed conventions or patterns"],"output_types":["conventions.md file with extracted conventions","structure.md file with project structure documentation","analysis logs showing what was discovered"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_8","uri":"capability://automation.workflow.ide.integration.via.cursorrules.and.claude.md.entry.points","name":"ide integration via .cursorrules and claude.md entry points","description":"Provides IDE-specific integration points (.cursorrules for Cursor, CLAUDE.md for Claude Code) that inject agent configuration and system prompts into IDE-native agent interfaces. The .cursorrules file contains Cursor-specific rules and context that guide Cursor's native agents. The CLAUDE.md file serves as the entry point for Claude Code, containing system prompt, task description, and links to available tools and context. These files are generated by the CLI and can be manually edited to customize agent behavior. This approach enables the same agent configuration to work across different IDEs without modification.","intents":["Configure Cursor's native agents with custom rules and context without IDE settings","Provide Claude Code with system prompts and task descriptions via CLAUDE.md","Enable IDE-specific optimizations while maintaining cross-IDE compatibility","Allow developers to customize agent behavior through familiar IDE configuration files"],"best_for":["Teams using Cursor or Claude Code as their primary development environment","Developers who want to customize agent behavior without learning framework APIs","Organizations standardizing on specific IDE agents across teams"],"limitations":[".cursorrules syntax is Cursor-specific and not portable to other IDEs","CLAUDE.md is optimized for Claude Code and may not work well with other AI IDEs","IDE agents may not support all features defined in rules files (e.g., custom tool execution)","Changes to IDE agent behavior require updating rules files manually — no automatic sync"],"requires":["Cursor IDE (for .cursorrules support) or Claude Code (for CLAUDE.md support)","Text editor to modify rules files","Understanding of Cursor rules syntax or Claude Code conventions"],"input_types":["agent configuration (from CLI generation)","custom rules and prompts","context files and tool definitions"],"output_types":[".cursorrules file (Cursor-specific)","CLAUDE.md file (Claude Code entry point)","IDE-integrated agent behavior"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-study8677--antigravity-workspace-template__cap_9","uri":"capability://memory.knowledge.context.file.management.with.automatic.loading.and.prioritization","name":"context file management with automatic loading and prioritization","description":"Automatically discovers and loads context files (Markdown files in .context/ directory) that provide background information, conventions, and project knowledge to agents. Context files are prioritized by relevance to the current task using semantic matching or explicit priority declarations. The system maintains a context budget (token limit) and selects the most relevant context files to include in agent prompts. Context files can include project conventions, architecture documentation, API references, code examples, and other background knowledge. This enables agents to make informed decisions without requiring explicit context injection in every prompt.","intents":["Provide agents with project context (conventions, architecture, patterns) automatically without manual injection","Ensure agents follow project conventions and architectural patterns without explicit instructions","Maintain a single source of truth for project knowledge that all agents can access","Enable context to be updated once and automatically used by all agents"],"best_for":["Large projects with complex conventions that agents need to follow","Teams wanting to enforce architectural patterns across agent-generated code","Organizations maintaining shared knowledge bases for multiple projects"],"limitations":["Context file discovery requires files to be in .context/ directory — no support for arbitrary locations","Semantic matching for context relevance requires LLM API calls or embedding models, adding latency","Context budget limits may cause important context to be excluded if too many files exist","No built-in mechanism to detect and resolve conflicting context from multiple files","Context files must be manually maintained — no automatic update when code changes"],"requires":["Python 3.8+",".context/ directory with Markdown files","Optional: embedding model or LLM API for semantic matching"],"input_types":["Markdown context files","task description or current agent prompt","context budget (token limit)"],"output_types":["selected context files","context relevance scores","merged context for agent prompt"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Git repository or any project directory","Write permissions to target directory","Tools must be Python functions in src/tools/ directory","Type hints on function parameters (recommended for schema generation)","Docstrings for tool descriptions (recommended)",".antigravity/config.json or environment variables","Optional: JSON schema for configuration validation","SKILLS.md file or skill modules defining available skills","Tool definitions for each skill"],"failure_modes":["Requires manual file editing to customize cognitive architecture — no GUI configuration builder","IDE-specific features (e.g., Cursor's native agent capabilities) may not fully integrate with declarative rules","File-based configuration can become unwieldy for complex multi-agent systems with 50+ rules","Tool discovery only works for Python modules — no support for shell scripts, JavaScript, or compiled binaries without wrapper functions","Sandbox execution adds ~50-200ms latency per tool invocation compared to direct function calls","Complex type hints (e.g., Union types, generics) may not translate cleanly to LLM function schemas","No built-in rate limiting or quota management for tool execution","Configuration schema must be manually defined — no auto-generation from code","Environment variable substitution only works for string values — complex types require custom parsing","No built-in configuration versioning or rollback mechanism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.46544891287959855,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"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":"2026-05-03T13:58:34.540Z","last_commit":"2026-04-29T15:38:06Z"},"community":{"stars":1205,"forks":245,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=study8677--antigravity-workspace-template","compare_url":"https://unfragile.ai/compare?artifact=study8677--antigravity-workspace-template"}},"signature":"ASSo1RKke3LGrj1hdhnYroEDpTiJNi8A5dSyoe0/4QJnt/XAovLWtHs2UzoOwZahpgyRtHSQ8LikQaDySmqTBA==","signedAt":"2026-06-22T03:56:47.915Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/study8677--antigravity-workspace-template","artifact":"https://unfragile.ai/study8677--antigravity-workspace-template","verify":"https://unfragile.ai/api/v1/verify?slug=study8677--antigravity-workspace-template","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"}}