{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-verdentai-verdent","slug":"verdent-for-vs-code-state-of-the-art-ai-coding-agent","name":"Verdent for VS Code: State-of-the-art AI Coding Agent","type":"agent","url":"https://marketplace.visualstudio.com/items?itemName=VerdentAI.verdent","page_url":"https://unfragile.ai/verdent-for-vs-code-state-of-the-art-ai-coding-agent","categories":["code-editors","deployment-infra"],"tags":["agent","ai","autonomous","coding","dev","keybindings","verdent"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-verdentai-verdent__cap_0","uri":"capability://planning.reasoning.autonomous.multi.step.code.generation.with.task.decomposition","name":"autonomous multi-step code generation with task decomposition","description":"Verdent decomposes complex coding tasks into subtasks before generating code, using an orchestrated agent architecture that breaks down requirements into actionable steps. The agent maintains context across multiple file edits and can generate code spanning multiple files within a single workspace, coordinating changes across interdependent modules. This differs from single-prompt code generation by explicitly planning the solution structure before implementation.","intents":["I need to implement a feature that requires changes across multiple files and want the agent to understand dependencies","I want the agent to break down a complex refactoring task into smaller, verifiable steps before executing","I need to generate boilerplate code that spans multiple related files (e.g., API endpoint + database schema + tests)"],"best_for":["solo developers building features in medium-to-large codebases","teams implementing multi-file refactorings with coordinated changes","developers prototyping complex systems where task decomposition reduces errors"],"limitations":["Task decomposition adds latency — each subtask requires separate AI inference","No guarantee that decomposed subtasks will be optimal or minimal","Context window limitations may prevent decomposition of very large features spanning 50+ files","Requires explicit project structure understanding — ambiguous architectures may lead to incorrect decomposition"],"requires":["VS Code 1.80+ (inferred from modern extension APIs)","Active AI model API key (model provider unknown from documentation)","Workspace with readable file structure"],"input_types":["natural language task description","existing code context from workspace","project structure metadata"],"output_types":["generated code across multiple files","task decomposition plan (structure unknown)","file modification instructions"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_1","uri":"capability://planning.reasoning.verifier.subagent.for.automated.issue.detection.and.validation","name":"verifier subagent for automated issue detection and validation","description":"Verdent includes a dedicated verifier subagent that analyzes generated code and identifies logical errors, type mismatches, architectural violations, and other issues before code is presented to the user. The verifier operates as a separate agent instance with access to the generated code, project context, and potentially linting/type-checking results. This creates a feedback loop where generated code is validated against project rules and best practices before user approval.","intents":["I want the agent to catch bugs in generated code before I review it","I need validation that generated code follows project conventions and architectural patterns","I want to reduce manual code review time by having the agent identify obvious issues"],"best_for":["teams with strict code quality standards","projects where generated code must pass linting and type checking automatically","developers who want to catch issues early without manual review"],"limitations":["Verifier operates on generated code only — cannot detect issues in existing codebase","Verification quality depends on project rules being explicitly defined in AGENTS.md or global rules","No access to runtime behavior — cannot detect logic errors that only manifest at execution time","Verification adds latency to code generation workflow (additional AI inference required)"],"requires":["Project rules defined in AGENTS.md or VS Code settings","Access to linting/type-checking tools (if verification includes tool output analysis)","AI model API key for verifier subagent inference"],"input_types":["generated code","project context and rules","linting/type-checking output (inferred)"],"output_types":["issue list with severity levels","suggested fixes or corrections","validation pass/fail status"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_10","uri":"capability://planning.reasoning.collaborative.multi.agent.task.execution.with.subagent.specialization","name":"collaborative multi-agent task execution with subagent specialization","description":"Verdent uses a subagent architecture where specialized subagents handle different aspects of tasks (e.g., planning, code generation, verification, testing). Subagents can be built-in or user-created, and the main agent orchestrates their execution. This enables task specialization where each subagent is optimized for a specific responsibility, improving overall task quality and enabling parallel execution of independent subtasks.","intents":["I want different specialized agents to handle different parts of my task (planning, coding, testing)","I need to create custom subagents for domain-specific tasks in my project","I want to parallelize task execution by having multiple subagents work independently"],"best_for":["teams with complex workflows that benefit from task specialization","organizations with domain-specific requirements that need custom subagents","developers who want to optimize performance through parallel subagent execution"],"limitations":["Subagent coordination overhead may negate benefits of parallelization for simple tasks","Custom subagent development requires understanding Verdent's subagent API (documentation unknown)","Subagent communication and state sharing mechanisms are undocumented","No built-in monitoring or debugging for subagent execution (inferred)","Subagent failures may cascade and cause overall task failure without proper error handling"],"requires":["Understanding of subagent architecture and specialization","Custom subagent development environment (if creating custom subagents)","API keys for each subagent's AI model inference"],"input_types":["task description and requirements","subagent specialization configuration","subagent communication and coordination rules"],"output_types":["subagent execution logs and results","coordinated task output from multiple subagents","error messages from subagent failures"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_11","uri":"capability://code.generation.editing.incremental.code.modification.with.change.tracking.and.rollback","name":"incremental code modification with change tracking and rollback","description":"Verdent applies code changes incrementally to the workspace, tracking modifications and enabling rollback if needed. The agent can modify files in place, and users can review changes before they are committed. This differs from generating entire files by allowing the agent to make surgical edits to existing code while preserving context and enabling easy reversal of changes.","intents":["I want the agent to modify my existing code without replacing entire files","I need to review changes before they are applied to my codebase","I want to be able to undo changes if they don't work out"],"best_for":["developers working with large existing files who want surgical edits","teams with strict code review requirements","projects where preserving file history and context is important"],"limitations":["Incremental modification is more complex than full file replacement — higher risk of syntax errors","Change tracking mechanism is undocumented — unclear how changes are tracked and displayed","Rollback capability is undocumented — may require manual undo or git revert","No integration with version control (inferred) — changes may not be tracked in git history","Concurrent modifications from multiple sources may cause conflicts"],"requires":["Existing code files in workspace","Change tracking mechanism (built-in or external)","Rollback capability (git, undo history, or other)"],"input_types":["existing code to be modified","modification instructions or diffs","change context and rationale"],"output_types":["modified code with changes applied","change tracking information (diffs, line numbers)","rollback instructions or git commits"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_12","uri":"capability://planning.reasoning.performance.benchmarking.and.swe.bench.verification","name":"performance benchmarking and swe-bench verification","description":"Verdent claims to achieve the highest SWE-bench Verified results among production-level agents, indicating strong performance on standardized software engineering benchmarks. The agent is optimized for real-world coding tasks and has been evaluated against established benchmarks. However, specific benchmark methodology, results, and comparison data are not provided in the documentation.","intents":["I want to use an agent that has been proven to perform well on real-world coding tasks","I need to evaluate agent quality before adopting it for my team","I want to compare Verdent's performance against other agents like Claude Code and Copilot"],"best_for":["organizations evaluating multiple agents and needing objective performance data","teams who want to use an agent with proven real-world performance","developers who care about agent quality and reliability"],"limitations":["Benchmark results are claimed but not documented with links or detailed methodology","No breakdown of performance by task type or difficulty level","Benchmark date and version are unknown — results may be outdated","No comparison of cost-per-task or latency metrics — only success rate is implied","Benchmark performance may not correlate with real-world performance on your specific codebase"],"requires":["Access to SWE-bench Verified benchmark results (external resource)","Understanding of benchmark methodology and limitations"],"input_types":["benchmark task descriptions","evaluation criteria and metrics"],"output_types":["benchmark performance results","comparison data vs other agents","performance metrics and success rates"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_2","uri":"capability://tool.use.integration.browser.automation.and.web.interaction.orchestration","name":"browser automation and web interaction orchestration","description":"Verdent includes a browser action tool that enables the agent to automate web interactions, capture screenshots, extract page content, and collect logs from browser sessions. The agent can navigate websites, fill forms, click elements, and analyze page state as part of task execution. This allows the agent to interact with web-based tools, APIs, and services directly within the coding workflow, capturing evidence of successful interactions.","intents":["I need the agent to test my web application by automating user interactions and capturing screenshots","I want the agent to interact with web-based APIs or services as part of code generation","I need to verify that generated code works by having the agent test it in a browser"],"best_for":["full-stack developers building web applications","teams testing web UIs as part of CI/CD workflows","developers integrating with web-based APIs or third-party services"],"limitations":["Browser automation requires a running browser instance — adds overhead and latency","Cannot interact with authenticated sessions without credential management (security risk)","Screenshot capture and analysis depends on page rendering — may fail with dynamic or JavaScript-heavy content","No access to browser DevTools or network inspection (inferred) — limited debugging capability","Browser automation is inherently flaky — timing issues and race conditions may cause failures"],"requires":["Browser runtime (Chrome, Firefox, or Chromium-based) installed locally","Network access to target websites","Proper credential management for authenticated interactions (mechanism unknown)"],"input_types":["URL or web page reference","interaction instructions (click, fill, navigate)","screenshot or page content analysis requests"],"output_types":["screenshot images","page content and DOM structure","interaction logs and results","error messages from failed interactions"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_3","uri":"capability://automation.workflow.collaborative.approval.workflow.with.three.execution.modes","name":"collaborative approval workflow with three execution modes","description":"Verdent supports three distinct collaboration modes that control how the agent interacts with the user: Manual Accept (user approves each action before execution), Auto Run (agent executes without approval), and Skip Permission (agent bypasses permission checks entirely). The user can configure which mode applies globally or per-subagent, enabling fine-grained control over agent autonomy. This architecture allows teams to balance automation speed with safety and oversight.","intents":["I want to review and approve each change the agent makes before it's applied","I want the agent to run autonomously on trusted tasks without interruption","I need to configure different approval workflows for different types of tasks (e.g., strict review for production, auto-run for tests)"],"best_for":["teams with strict code review requirements","developers who want to gradually increase agent autonomy as trust builds","organizations balancing automation speed with governance and oversight"],"limitations":["Manual Accept mode creates bottlenecks — agent must wait for user approval between steps","Auto Run mode removes safety guardrails — requires high confidence in agent behavior","Skip Permission mode is dangerous for production code — should only be used in isolated environments","No audit trail or rollback mechanism documented — changes are applied immediately without version control integration","Mode configuration is global or per-subagent, not per-file or per-change-type (coarse-grained control)"],"requires":["VS Code extension active and running","User interaction capability (keyboard/mouse for approval UI)","Clear understanding of mode implications and risks"],"input_types":["mode selection (Manual Accept, Auto Run, Skip Permission)","per-subagent mode configuration"],"output_types":["approval prompts (in Manual Accept mode)","execution logs showing which mode was used","file modifications applied according to selected mode"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_4","uri":"capability://planning.reasoning.rule.based.agent.behavior.configuration.via.agents.md","name":"rule-based agent behavior configuration via agents.md","description":"Verdent allows users to define global rules and AGENTS.md entries that guide agent and subagent behavior consistently across tasks. Rules are stored in a project-level configuration file (format unknown) and are injected into agent prompts to enforce architectural patterns, coding standards, and project-specific constraints. This enables teams to customize agent behavior without modifying the extension itself, creating a form of lightweight agent fine-tuning through configuration.","intents":["I want to enforce project-specific coding standards and architectural patterns without manually reviewing every generated change","I need to guide the agent toward specific implementation approaches (e.g., 'always use dependency injection')","I want to prevent the agent from making certain types of changes (e.g., 'never modify database schemas without approval')"],"best_for":["teams with established coding standards and architectural patterns","projects where consistency is critical (e.g., financial systems, healthcare)","organizations wanting to enforce governance policies through agent configuration"],"limitations":["Rule format and syntax are undocumented — users must reverse-engineer from examples or trial-and-error","No validation or linting for rule definitions — invalid rules may silently fail or cause unexpected behavior","Rules are injected into prompts, not enforced at runtime — agent can still violate rules if it chooses to ignore them","No rule versioning or change tracking — difficult to audit what rules were active when code was generated","Rule complexity is limited by prompt context window — very large rule sets may be truncated or ignored"],"requires":["AGENTS.md file in project root (or configured location)","Understanding of rule syntax and format (documentation unknown)","VS Code extension with rule parsing capability"],"input_types":["AGENTS.md configuration file","global rules from VS Code settings","project context and existing code patterns"],"output_types":["agent behavior modifications","generated code that adheres to defined rules","validation feedback if rules are violated"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_5","uri":"capability://tool.use.integration.mcp.tool.integration.and.custom.plugin.orchestration","name":"mcp tool integration and custom plugin orchestration","description":"Verdent supports interoperability with MCP (Model Context Protocol) tools and extended functionality through custom toolchains and plugins. The agent can invoke MCP-compatible tools as part of task execution, enabling integration with external services, APIs, and specialized tools. This architecture allows the agent to extend its capabilities beyond built-in functions by composing MCP tools into workflows.","intents":["I want the agent to use specialized tools (e.g., database query tools, API clients) as part of code generation","I need to integrate the agent with our internal tools and services via MCP","I want to extend the agent's capabilities with custom plugins for domain-specific tasks"],"best_for":["organizations with custom internal tools and services","teams using specialized tools (e.g., Terraform, Kubernetes, Docker) that need agent integration","developers building domain-specific coding agents on top of Verdent"],"limitations":["MCP tool catalog and available integrations are undocumented — users must discover tools independently","Custom plugin development requires understanding MCP protocol and Verdent's plugin API (documentation unknown)","Tool invocation adds latency and potential failure points — agent must wait for tool responses","No built-in error handling or retry logic for tool failures (inferred) — agent may fail if tools are unavailable","Tool output format and parsing are agent-dependent — inconsistent tool responses may confuse the agent"],"requires":["MCP-compatible tools installed and configured","API keys or credentials for external tools","Understanding of MCP protocol and tool schemas","Custom plugin development environment (if building plugins)"],"input_types":["MCP tool schemas and capabilities","tool invocation requests from agent","tool output and results"],"output_types":["tool execution results","integrated code generation using tool outputs","error messages from tool failures"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_6","uri":"capability://code.generation.editing.context.aware.code.generation.with.workspace.understanding","name":"context-aware code generation with workspace understanding","description":"Verdent analyzes the current workspace structure, existing code patterns, and project metadata to generate code that is contextually appropriate and consistent with the codebase. The agent has access to the current file, related files, project configuration, and inferred architectural patterns. This enables the agent to generate code that follows existing conventions and integrates seamlessly with the codebase, rather than generating generic or isolated code snippets.","intents":["I want the agent to generate code that follows the patterns and conventions in my existing codebase","I need the agent to understand my project structure and generate code that integrates properly","I want the agent to be aware of my tech stack and generate code using the right libraries and frameworks"],"best_for":["developers working in established codebases with strong conventions","teams with specific tech stacks and architectural patterns","projects where consistency and integration are critical"],"limitations":["Context analysis is limited by file system access scope — agent may not see all relevant files","Inferring architectural patterns from code is error-prone — agent may misunderstand project structure","Context window limitations prevent analyzing very large codebases — agent may miss important patterns","No explicit dependency graph analysis (inferred) — agent may generate code that violates dependency constraints","Context freshness is unknown — agent may use stale information if files are modified during execution"],"requires":["Readable workspace with clear file structure","Project configuration files (package.json, tsconfig.json, etc.) for tech stack detection","Sufficient file system access to analyze codebase patterns"],"input_types":["current file content","related file references","project configuration and metadata","existing code patterns and conventions"],"output_types":["contextually appropriate generated code","code that follows existing patterns and conventions","integration-ready code snippets"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_7","uri":"capability://tool.use.integration.multi.model.ai.orchestration.with.configurable.model.selection","name":"multi-model ai orchestration with configurable model selection","description":"Verdent orchestrates multiple AI models (specific models unknown from documentation) and allows users to configure which model is used for different tasks. The agent can route different subtasks to different models based on their strengths (e.g., using a fast model for simple tasks and a powerful model for complex reasoning). This enables cost optimization and performance tuning by matching model capabilities to task requirements.","intents":["I want to use different AI models for different types of tasks (e.g., fast model for simple completions, powerful model for complex reasoning)","I need to optimize costs by using cheaper models for simple tasks and expensive models only when necessary","I want to switch between AI providers (OpenAI, Anthropic, etc.) without changing my workflow"],"best_for":["teams with budget constraints who want to optimize AI API costs","organizations using multiple AI providers and wanting unified orchestration","developers who want to experiment with different models for performance tuning"],"limitations":["Supported models are undocumented — users must discover available options through trial or documentation","Model configuration mechanism is unknown — may require manual settings or code changes","No automatic model selection based on task complexity (inferred) — users must configure routing manually","Model availability and pricing are subject to provider changes — configuration may become outdated","No cost tracking or optimization recommendations (inferred) — users must manually monitor API usage"],"requires":["API keys for configured AI models (providers unknown)","Understanding of available models and their capabilities","Model configuration in VS Code settings or AGENTS.md (format unknown)"],"input_types":["model selection configuration","task type or complexity indicators","API keys for multiple providers"],"output_types":["model routing decisions","generated code from selected model","API usage logs (inferred)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_8","uri":"capability://code.generation.editing.autonomous.debugging.with.error.analysis.and.fix.generation","name":"autonomous debugging with error analysis and fix generation","description":"Verdent can autonomously identify bugs in code, analyze error messages and logs, and generate fixes without explicit user direction. The agent can access error output from the IDE, test results, and runtime logs to understand what went wrong and propose corrections. This creates a feedback loop where the agent learns from failures and iteratively improves generated code.","intents":["I want the agent to automatically fix bugs it finds in generated code","I need the agent to analyze error messages and logs to understand what went wrong","I want the agent to run tests and fix failures autonomously"],"best_for":["developers who want to reduce manual debugging time","teams with comprehensive test suites that can validate fixes","projects where rapid iteration and bug fixing are priorities"],"limitations":["Debugging capability depends on error message quality — cryptic errors may confuse the agent","Agent cannot access runtime state or debugger information (inferred) — limited to static analysis and logs","No guarantee that generated fixes are correct — agent may introduce new bugs while fixing old ones","Debugging latency is high — each iteration requires new AI inference and potentially test execution","No rollback mechanism if fixes introduce regressions (inferred) — users must manually revert bad fixes"],"requires":["Error output accessible to agent (IDE console, test results, logs)","Test suite or validation mechanism to verify fixes","AI model API key for fix generation inference"],"input_types":["error messages and stack traces","test failure output","runtime logs and diagnostics","generated code that failed"],"output_types":["bug analysis and root cause identification","proposed fixes and corrections","updated code with fixes applied","validation results from re-running tests"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-verdentai-verdent__cap_9","uri":"capability://planning.reasoning.system.design.and.architectural.reasoning","name":"system design and architectural reasoning","description":"Verdent can assist with system design and technical brainstorming by reasoning about architectural patterns, trade-offs, and design decisions. The agent can analyze requirements, propose architectural approaches, and explain design rationale. This capability extends beyond code generation to help developers think through complex system design problems before implementation.","intents":["I want the agent to help me design the architecture for a new system","I need to brainstorm design approaches and understand trade-offs","I want the agent to explain architectural patterns and when to use them"],"best_for":["architects and senior developers designing new systems","teams making high-level design decisions","developers learning about architectural patterns and best practices"],"limitations":["System design reasoning is subjective — agent may propose designs that don't match team preferences","No access to domain-specific knowledge or business constraints (inferred) — designs may not align with requirements","Design proposals are text-based — no visual diagrams or architecture visualization (inferred)","Agent cannot validate designs against real-world constraints (scalability, cost, etc.)","Design reasoning quality depends on prompt quality — vague requirements lead to vague designs"],"requires":["Clear requirements and constraints for the system","AI model API key for reasoning inference","Understanding of architectural patterns and trade-offs"],"input_types":["system requirements and constraints","existing architecture or codebase context","design questions or brainstorming prompts"],"output_types":["architectural proposals and approaches","design trade-off analysis","rationale and justification for design decisions","implementation guidance"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.80+ (inferred from modern extension APIs)","Active AI model API key (model provider unknown from documentation)","Workspace with readable file structure","Project rules defined in AGENTS.md or VS Code settings","Access to linting/type-checking tools (if verification includes tool output analysis)","AI model API key for verifier subagent inference","Understanding of subagent architecture and specialization","Custom subagent development environment (if creating custom subagents)","API keys for each subagent's AI model inference","Existing code files in workspace"],"failure_modes":["Task decomposition adds latency — each subtask requires separate AI inference","No guarantee that decomposed subtasks will be optimal or minimal","Context window limitations may prevent decomposition of very large features spanning 50+ files","Requires explicit project structure understanding — ambiguous architectures may lead to incorrect decomposition","Verifier operates on generated code only — cannot detect issues in existing codebase","Verification quality depends on project rules being explicitly defined in AGENTS.md or global rules","No access to runtime behavior — cannot detect logic errors that only manifest at execution time","Verification adds latency to code generation workflow (additional AI inference required)","Subagent coordination overhead may negate benefits of parallelization for simple tasks","Custom subagent development requires understanding Verdent's subagent API (documentation unknown)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.49,"quality":0.5,"ecosystem":0.45,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:40.998Z","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=verdent-for-vs-code-state-of-the-art-ai-coding-agent","compare_url":"https://unfragile.ai/compare?artifact=verdent-for-vs-code-state-of-the-art-ai-coding-agent"}},"signature":"mV8V9qPfVxEIQRboEXyR8IVxyw9+XKk9B5ONwoMMXFuzkiFvyGLJ8R8dSL0gG+rgkrLsYgSn0NPBhLfd3dLHCQ==","signedAt":"2026-06-20T11:05:19.703Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/verdent-for-vs-code-state-of-the-art-ai-coding-agent","artifact":"https://unfragile.ai/verdent-for-vs-code-state-of-the-art-ai-coding-agent","verify":"https://unfragile.ai/api/v1/verify?slug=verdent-for-vs-code-state-of-the-art-ai-coding-agent","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"}}