{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ecloud-zhanlu","slug":"zhanlu-ai-coding-assistant","name":"Zhanlu - AI Coding Assistant","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ecloud.zhanlu","page_url":"https://unfragile.ai/zhanlu-ai-coding-assistant","categories":["code-editors"],"tags":["agent","ai","ai coding","AI编程","autocoding","autocomplete","c#","chat","chatgpt","code generation","codegen","dev","ecloud","go","inline completion","javascript","keybindings","mcp","python","typescript","湛卢","移动云"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ecloud-zhanlu__cap_0","uri":"capability://code.generation.editing.real.time.inline.code.completion.with.cross.file.context","name":"real-time inline code completion with cross-file context","description":"Generates single-line and multi-line code completions during active editing by analyzing the current file, cross-file project context, and compilation state. Completions are surfaced inline with Tab-key acceptance, leveraging project-level architectural understanding to predict contextually relevant code patterns. The system maintains awareness of imported modules, class hierarchies, and function signatures across the entire codebase to ensure completions align with existing code structure.","intents":["I want autocomplete suggestions that understand my project's architecture and coding patterns","I need faster code writing without breaking context or losing focus","I want completions that respect my project's dependencies and imports"],"best_for":["individual developers working on medium-to-large codebases with consistent patterns","teams using VS Code as primary editor with shared project structure"],"limitations":["Requires active authentication and cloud connectivity; cannot operate offline","Cross-file context scope is undocumented — unclear if limited to open files, workspace, or entire project","Latency characteristics unknown — may introduce perceptible delays on slower network connections","No local-only mode available; all inference appears cloud-based"],"requires":["Visual Studio Code (minimum version unknown)","Active login via China Mobile Cloud, AK/SK, or enterprise SSO","Network connectivity to Zhanlu backend servers","Project files accessible within VS Code workspace"],"input_types":["source code (current file)","project context (cross-file references)","compilation errors (if present)"],"output_types":["inline code suggestions (single-line or multi-line)","completion metadata (confidence, alternative suggestions)"],"categories":["code-generation-editing","context-aware-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_1","uri":"capability://code.generation.editing.natural.language.to.code.generation.with.inline.comments","name":"natural language to code generation with inline comments","description":"Converts natural language descriptions (provided via in-editor prompts or chat interface) into executable code with auto-generated inline comments explaining logic. The system parses the natural language requirement, decomposes it into implementation steps, generates syntactically correct code in the target language, and annotates the code with method-level and inline comments. Supports code generation within the context of the current file or as standalone snippets.","intents":["I want to describe what I need in plain English and get working code","I need generated code to be self-documenting with clear comments","I want to prototype functionality quickly without writing boilerplate"],"best_for":["developers prototyping features or exploring unfamiliar APIs","teams onboarding junior developers who benefit from commented code","rapid MVP development where speed outweighs code optimization"],"limitations":["Generated code quality depends on natural language clarity — ambiguous descriptions produce suboptimal results","No validation that generated code compiles or runs correctly; user must test output","Comment generation may be verbose or redundant for simple code","Underlying LLM model and version unknown — cannot predict consistency or capability ceiling"],"requires":["Visual Studio Code with Zhanlu extension installed","Active authentication (Cloud/AK-SK/SSO)","Network connectivity for cloud-based code generation","Target language support (Java, Python, Go, C/C++, C#, JavaScript, TypeScript, PHP, Ruby, Rust, Scala, HTML, CSS3, Swift, Dart)"],"input_types":["natural language description (text)","target programming language (enum)","optional: existing code context (for in-file generation)"],"output_types":["generated source code (language-specific)","inline comments (method-level and line-level)","optional: multiple code variants"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_10","uri":"capability://tool.use.integration.mcp.tool.configuration.and.invocation.for.external.integrations","name":"mcp tool configuration and invocation for external integrations","description":"Enables configuration and invocation of Model Context Protocol (MCP) tools to extend Zhanlu's capabilities with external integrations. Users can register custom MCP tools that interact with APIs, databases, file systems, or other services. The agent can invoke these tools as part of task execution, passing parameters and receiving results. Tool definitions include schema specifications, parameter validation, and error handling. Supports both built-in tools (file I/O, shell execution) and user-defined custom tools.","intents":["I want to extend Zhanlu with custom integrations to my internal APIs or services","I need the agent to interact with external systems (databases, APIs) as part of task execution","I want to define reusable tool definitions that the agent can invoke automatically"],"best_for":["enterprises integrating Zhanlu with proprietary systems and APIs","teams building custom agents with domain-specific tool requirements","developers extending Zhanlu's capabilities beyond built-in functions"],"limitations":["MCP tool configuration documentation is minimal — unclear how to define custom tools or what schema format is required","No built-in tool library documented — unclear which tools are available out-of-the-box","Tool invocation is sandboxed — cannot directly access sensitive resources or production systems without explicit configuration","Error handling and timeout behavior for tool invocation is undocumented"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","MCP tool schema definition (format unknown)","External service credentials or API keys (if integrating with external systems)"],"input_types":["tool schema definition (JSON or YAML format, unspecified)","tool parameters (variable based on tool definition)","optional: authentication credentials"],"output_types":["tool invocation results (variable based on tool definition)","error messages and status codes","execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_11","uri":"capability://safety.moderation.enterprise.authentication.with.sso.and.role.based.access.control","name":"enterprise authentication with sso and role-based access control","description":"Provides enterprise-grade authentication supporting multiple identity providers (China Mobile Cloud, AK/SK credentials, SAML/SSO) and role-based access control (RBAC) for team environments. Users authenticate once and receive a session token valid across VS Code and web interfaces. RBAC controls which features and projects each user can access, with granular permissions for code review, test generation, and agent execution. Audit logging tracks all user actions for compliance and security monitoring.","intents":["I need to authenticate my team with our corporate identity provider","I want to control which developers can use which Zhanlu features","I need audit logs of all code generation and review activities for compliance"],"best_for":["enterprise organizations with existing SSO infrastructure","regulated industries requiring audit trails and access control","teams managing sensitive codebases with strict security requirements"],"limitations":["SSO configuration is undocumented — unclear how to set up SAML or other identity providers","RBAC granularity is unknown — unclear which permissions can be assigned at which levels","Audit logging scope is undocumented — unclear what events are logged and retention policies","No offline authentication — requires network connectivity to identity provider"],"requires":["Visual Studio Code with Zhanlu extension","Enterprise account with Zhanlu (not available in free tier)","SSO provider configuration (SAML, OAuth2, or proprietary)","Network connectivity to identity provider"],"input_types":["user credentials (SSO provider-specific)","optional: RBAC role assignment (admin-only)"],"output_types":["session token (valid for duration of VS Code session)","user profile and permissions (cached locally)","audit log entries (server-side)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_2","uri":"capability://code.generation.editing.project.level.code.review.with.auto.optimization.recommendations","name":"project-level code review with auto-optimization recommendations","description":"Analyzes entire project codebase to identify code quality issues, performance bottlenecks, and optimization opportunities. Generates a comprehensive review report with specific recommendations for refactoring, performance improvement, and best-practice alignment. The system scans multiple files in parallel, builds a project-wide dependency graph, and surfaces issues ranked by severity and impact. Recommendations include before/after code examples and rationale for each suggested change.","intents":["I want an automated code review of my entire project without waiting for human reviewers","I need to identify performance bottlenecks and optimization opportunities across my codebase","I want to enforce coding standards and best practices at scale"],"best_for":["teams conducting pre-release code quality audits","solo developers seeking objective feedback on project architecture","organizations migrating legacy codebases and needing systematic refactoring guidance"],"limitations":["Review quality depends on project size and complexity — very large projects may timeout or produce incomplete analysis","Recommendations are suggestions only; no automatic code modification without explicit user approval","Cannot detect domain-specific or business logic issues; focuses on syntax, performance, and style","No integration with version control or CI/CD pipelines documented — requires manual review initiation"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Complete project accessible within VS Code workspace","Compilation/build artifacts available (for error detection)"],"input_types":["entire project codebase (multi-file)","compilation errors and warnings (if present)","project configuration (implicit)"],"output_types":["structured review report (JSON or formatted text)","issue list with severity levels","code snippets (before/after examples)","optimization recommendations with rationale"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_3","uri":"capability://code.generation.editing.stack.trace.analysis.and.error.repair.suggestion","name":"stack trace analysis and error repair suggestion","description":"Analyzes runtime exceptions and compilation errors (including stack traces) to diagnose root causes and suggest targeted repairs. The system parses error messages, traces execution paths through the codebase, identifies the problematic code section, and generates corrected code with explanation of the fix. Integrates with VS Code's error diagnostics to surface suggestions inline at error locations. Supports multi-step debugging by analyzing error chains and suggesting fixes that address root causes rather than symptoms.","intents":["I want to understand why my code is crashing and get a fix suggestion immediately","I need to debug complex error chains without manually tracing execution paths","I want to learn from errors by seeing explanations of what went wrong and why"],"best_for":["developers debugging unfamiliar codebases or frameworks","teams reducing time-to-resolution for production errors","learning environments where error explanations accelerate skill development"],"limitations":["Accuracy depends on error message clarity — obfuscated or truncated stack traces reduce diagnostic quality","Cannot fix errors requiring external service changes or infrastructure modifications","Suggested fixes may not address all edge cases or may introduce new issues — user must validate","No integration with debugger or runtime state — analysis is static, not dynamic"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Compilation errors or runtime exceptions visible in VS Code diagnostics","Source code accessible for analysis"],"input_types":["error message (text)","stack trace (multi-line formatted text)","source code context (surrounding the error location)","compilation/runtime diagnostics"],"output_types":["root cause explanation (natural language)","corrected code snippet","fix rationale and alternative approaches","inline suggestions in editor"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_4","uri":"capability://code.generation.editing.unit.test.generation.with.framework.specific.templates","name":"unit test generation with framework-specific templates","description":"Generates unit tests for specified functions or classes using framework-specific patterns and conventions. Supports batch test generation across multiple files, automatically selecting appropriate test frameworks (JUnit, Mockito, Spring Test for Java; pytest, unittest for Python) based on project configuration. Generated tests include setup/teardown logic, mock object creation, assertion statements, and edge case coverage. Tests are generated with proper naming conventions and documentation matching the target framework's idioms.","intents":["I want to generate comprehensive unit tests without writing boilerplate setup and assertions","I need to increase test coverage quickly across a large codebase","I want tests that follow my project's framework conventions and best practices"],"best_for":["teams under time pressure to increase test coverage before release","projects adopting test-driven development and needing rapid test scaffolding","developers unfamiliar with specific test frameworks seeking to learn conventions"],"limitations":["Generated tests may not cover domain-specific business logic or edge cases — user must review and enhance","Mock object creation assumes standard patterns; complex dependency injection scenarios may require manual adjustment","Test quality depends on code clarity — poorly documented functions produce less effective tests","Batch generation can be slow for large projects; no progress indication or cancellation mechanism documented"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Target language support (Java, Python, Go, C/C++, C#, JavaScript, TypeScript, PHP, Ruby, Rust, Scala, HTML, CSS3, Swift, Dart)","Test framework installed in project (JUnit, Mockito, Spring Test, pytest, unittest, etc.)"],"input_types":["source code (function or class to test)","test framework selection (enum or auto-detect)","optional: existing test examples (for style matching)"],"output_types":["generated test code (framework-specific)","test fixtures and mocks","setup/teardown methods","assertion statements with expected values"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_5","uri":"capability://code.generation.editing.cross.language.code.translation.with.semantic.preservation","name":"cross-language code translation with semantic preservation","description":"Translates source code from one programming language to another while preserving semantic meaning and adapting to target language idioms. Supports bidirectional translation between Java, Python, Go, JavaScript, TypeScript, C/C++, and C#. The system analyzes the source code's control flow, data structures, and algorithms, then reconstructs equivalent logic in the target language using idiomatic patterns (e.g., list comprehensions in Python, goroutines in Go). Maintains function signatures and class hierarchies where applicable, and generates comments explaining language-specific adaptations.","intents":["I need to migrate code from one language to another without rewriting from scratch","I want to understand how an algorithm is implemented in different languages","I need to port a library or module to a new language while preserving behavior"],"best_for":["teams migrating between technology stacks (e.g., Java to Go for microservices)","polyglot organizations sharing code across language boundaries","developers learning new languages by translating familiar code"],"limitations":["Translation quality degrades for language-specific features without direct equivalents (e.g., C++ templates to Python)","Standard library differences may require manual adjustment of translated code","Performance characteristics may differ significantly between languages — translated code may need optimization","No guarantee of functional equivalence — user must test translated code thoroughly"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Source language support (Java, Python, Go, C/C++, C#, JavaScript, TypeScript, PHP, Ruby, Rust, Scala, HTML, CSS3, Swift, Dart)","Target language support (same set)"],"input_types":["source code (any supported language)","target language selection (enum)","optional: translation constraints or style preferences"],"output_types":["translated source code (target language)","adaptation notes (explaining language-specific changes)","equivalent function signatures and class definitions"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_6","uri":"capability://planning.reasoning.full.stack.programming.agent.with.task.decomposition.and.execution","name":"full-stack programming agent with task decomposition and execution","description":"Operates as an autonomous agent that accepts high-level programming requirements, decomposes them into subtasks, generates implementation code, executes the code, detects failures, and iteratively fixes bugs until the task completes successfully. The agent maintains state across multiple steps, tracks completed subtasks, and can invoke MCP (Model Context Protocol) tools to interact with external systems (file I/O, API calls, database operations). Includes self-reflection capability to evaluate generated code quality and suggest improvements. Operates in a sandboxed environment with explicit user approval for each execution step.","intents":["I want to describe a complete feature and have the AI implement it end-to-end","I need an agent that can write code, test it, fix bugs, and iterate until it works","I want to automate repetitive programming tasks like data migration or batch processing"],"best_for":["solo developers or small teams building MVPs with tight timelines","organizations automating routine code generation tasks (migrations, refactoring)","exploratory development where rapid iteration and feedback loops are valuable"],"limitations":["Agent behavior is non-deterministic — same input may produce different outputs across runs","No guaranteed convergence — agent may fail to complete complex tasks or enter infinite loops","MCP tool configuration is undocumented — unclear which tools are available and how to configure custom tools","Execution environment is sandboxed — cannot directly modify production systems or access sensitive resources","Cost of multi-step execution unknown — may incur significant API charges for complex tasks"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","MCP tool configuration (if using external integrations)","Explicit user approval for each execution step"],"input_types":["high-level requirement description (natural language)","optional: existing code context or constraints","optional: MCP tool specifications"],"output_types":["generated implementation code","execution logs and step-by-step trace","error messages and fix attempts","final working code (if successful)"],"categories":["planning-reasoning","code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_7","uri":"capability://text.generation.language.multi.turn.conversational.q.a.with.code.context","name":"multi-turn conversational q&a with code context","description":"Provides a chat interface for multi-turn dialogue about coding problems, technical questions, and implementation strategies. The system maintains conversation history and project context across turns, enabling follow-up questions and iterative refinement of solutions. Responses include code examples, explanations, and links to relevant project files. The chat interface integrates with the editor, allowing users to reference selected code snippets or error messages directly in questions.","intents":["I want to ask follow-up questions about generated code without losing context","I need to discuss design decisions and get recommendations for different approaches","I want to learn how to solve a problem by discussing it conversationally"],"best_for":["developers learning new frameworks or languages through interactive dialogue","teams discussing architecture decisions and getting AI-assisted recommendations","solo developers seeking rubber-duck debugging with an intelligent partner"],"limitations":["Conversation history is session-scoped — no persistence across VS Code restarts (unless explicitly saved)","Context window limitations may cause loss of earlier conversation turns in very long dialogues","Responses are generated by LLM — may contain inaccuracies or outdated information","No integration with external documentation or web search — responses based only on training data and project context"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Chat interface accessible (sidebar or panel)"],"input_types":["natural language question (text)","optional: selected code snippet (from editor)","optional: error message or diagnostic"],"output_types":["natural language response (text)","code examples (language-specific)","file references (links to relevant project files)","follow-up suggestions"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_8","uri":"capability://text.generation.language.commit.message.and.readme.generation.from.code.changes","name":"commit message and readme generation from code changes","description":"Automatically generates descriptive commit messages and README documentation based on code changes detected in the working directory. Analyzes diffs to understand what was added, modified, or removed, then synthesizes natural language descriptions following conventional commit format (feat:, fix:, refactor:, etc.). For README generation, scans project structure, identifies key modules and functions, and produces documentation with usage examples and API descriptions. Integrates with version control workflows to suggest messages before commit.","intents":["I want to generate clear commit messages without manually writing descriptions","I need to create or update README documentation to match my code changes","I want to enforce consistent documentation standards across commits"],"best_for":["teams enforcing conventional commit standards and reducing review friction","open-source projects maintaining high documentation quality","developers working in non-English environments seeking to standardize documentation language"],"limitations":["Generated messages may be generic or miss domain-specific context — user should review and customize","README generation assumes standard project structure — non-standard layouts may produce incomplete documentation","No integration with commit hooks or pre-commit frameworks documented","Cannot access commit history or related issues — generates messages based only on current diff"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Git repository initialized in workspace","Staged changes or working directory diff available"],"input_types":["code diff (git diff format)","project structure (implicit from workspace)","optional: existing README (for style matching)"],"output_types":["commit message (conventional commit format)","README content (markdown)","changelog entries (optional)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ecloud-zhanlu__cap_9","uri":"capability://planning.reasoning.practice.mode.with.auto.generated.coding.exercises","name":"practice mode with auto-generated coding exercises","description":"Generates coding exercises and challenges organized by programming language and difficulty level. The system creates problem statements, test cases, and solution templates, then evaluates user submissions against the test cases. Provides hints and explanations when users request help, and tracks progress across multiple exercises. Exercises are generated dynamically based on selected language and difficulty, covering common algorithms, data structures, and language-specific idioms.","intents":["I want to practice coding in a specific language with auto-graded exercises","I need to learn language idioms and best practices through hands-on practice","I want to track my progress and identify areas for improvement"],"best_for":["developers learning new programming languages","teams conducting technical interviews or coding assessments","educational institutions using Zhanlu as a practice platform"],"limitations":["Exercise quality depends on LLM generation — some exercises may be trivial or overly complex","Test case coverage may be incomplete — user solutions might pass tests but fail in edge cases","No integration with external coding platforms (LeetCode, HackerRank) — exercises are Zhanlu-specific","Progress tracking is local to VS Code — no cloud sync or cross-device access"],"requires":["Visual Studio Code with Zhanlu extension","Active authentication and network connectivity","Target language support (Java, Python, Go, C/C++, C#, JavaScript, TypeScript, PHP, Ruby, Rust, Scala, HTML, CSS3, Swift, Dart)"],"input_types":["language selection (enum)","difficulty level (easy/medium/hard)","optional: topic or algorithm focus"],"output_types":["problem statement (natural language)","test cases (input/output pairs)","solution template (language-specific)","hints and explanations (on request)","evaluation results (pass/fail with feedback)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version unknown)","Active login via China Mobile Cloud, AK/SK, or enterprise SSO","Network connectivity to Zhanlu backend servers","Project files accessible within VS Code workspace","Visual Studio Code with Zhanlu extension installed","Active authentication (Cloud/AK-SK/SSO)","Network connectivity for cloud-based code generation","Target language support (Java, Python, Go, C/C++, C#, JavaScript, TypeScript, PHP, Ruby, Rust, Scala, HTML, CSS3, Swift, Dart)","Visual Studio Code with Zhanlu extension","Active authentication and network connectivity"],"failure_modes":["Requires active authentication and cloud connectivity; cannot operate offline","Cross-file context scope is undocumented — unclear if limited to open files, workspace, or entire project","Latency characteristics unknown — may introduce perceptible delays on slower network connections","No local-only mode available; all inference appears cloud-based","Generated code quality depends on natural language clarity — ambiguous descriptions produce suboptimal results","No validation that generated code compiles or runs correctly; user must test output","Comment generation may be verbose or redundant for simple code","Underlying LLM model and version unknown — cannot predict consistency or capability ceiling","MCP tool configuration documentation is minimal — unclear how to define custom tools or what schema format is required","No built-in tool library documented — unclear which tools are available out-of-the-box","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5,"quality":0.34,"ecosystem":0.35000000000000003,"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":"2026-05-24T12:16:34.118Z","last_scraped_at":"2026-05-03T15:20:42.146Z","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=zhanlu-ai-coding-assistant","compare_url":"https://unfragile.ai/compare?artifact=zhanlu-ai-coding-assistant"}},"signature":"S6F7L8rVEetM2N0kTmzN6i3lTjOD3/Q1cw0ffYwL95oYk0pe/3c847n05qWnA87AYpJlWLA5tLvZ0oP+9nEhBg==","signedAt":"2026-06-21T22:13:39.127Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zhanlu-ai-coding-assistant","artifact":"https://unfragile.ai/zhanlu-ai-coding-assistant","verify":"https://unfragile.ai/api/v1/verify?slug=zhanlu-ai-coding-assistant","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"}}