{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-aminer-codegeex","slug":"codegeex-ai-coding-assistant","name":"CodeGeeX: AI Coding Assistant","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=aminer.codegeex","page_url":"https://unfragile.ai/codegeex-ai-coding-assistant","categories":["code-editors"],"tags":["agent","ai","autocomplete","c#","c++","chat","chatglm","code explanation","code generation","codegeex","glm","go","inline completion","java","javascript","keybindings","php","python","typescript","zhipu","zhipu ai","智谱","智谱AI"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-aminer-codegeex__cap_0","uri":"capability://code.generation.editing.multilingual.code.completion.with.context.aware.suggestions","name":"multilingual code completion with context-aware suggestions","description":"Generates inline code suggestions for the current or following lines by analyzing the active editor context (current file, cursor position, preceding code). The 13B parameter model processes code semantics across 20+ programming languages and outputs single or multi-line completions triggered via Tab key or autocomplete popup. Suggestions are streamed into the editor without requiring explicit function/method selection, enabling real-time pair-programming workflow integration.","intents":["I want the editor to suggest the next line of code as I type, similar to GitHub Copilot","I need quick code completion that understands my current file's context and language","I want to accept or reject suggestions with minimal friction using keyboard shortcuts"],"best_for":["individual developers using VS Code across Python, JavaScript, TypeScript, Java, C++, C#, Go, PHP and 12+ other languages","teams seeking free or low-cost code completion without GitHub Copilot subscription","developers in regions with better latency to Zhipu AI infrastructure (China-based backend)"],"limitations":["Single-file context only — cannot analyze project-wide patterns or cross-file dependencies, limiting accuracy for large codebases","Cloud-dependent inference introduces variable latency (typically 500ms–2s per suggestion) compared to local models","No offline mode — requires active internet connection to Zhipu AI backend for all completions","Suggestion quality degrades for niche languages or domain-specific code patterns outside training distribution"],"requires":["Visual Studio Code (version not specified in documentation)","Active internet connection to Zhipu AI inference backend","No explicit API key configuration documented; authentication mechanism unknown"],"input_types":["source code (current file context)","cursor position (implicit)","preceding code lines (implicit context)"],"output_types":["inline code suggestion (single or multi-line string)","suggestion confidence/ranking (unknown if exposed to user)"],"categories":["code-generation-editing","editor-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_1","uri":"capability://code.generation.editing.natural.language.to.code.generation.from.inline.comments","name":"natural language to code generation from inline comments","description":"Converts natural language comments (e.g., `// sort array in descending order`) into executable code by parsing the comment, inferring intent, and generating the corresponding implementation. The model analyzes the preceding code context (variable types, imports, function signatures) to produce syntactically correct, contextually appropriate code. Triggered via right-click menu or sidebar command palette, with output inserted at the comment location or following line.","intents":["I want to write a comment describing what I need, then have the code auto-generated below it","I need to quickly stub out a function body from a docstring without manually typing boilerplate","I want to generate code in multiple languages from the same English comment"],"best_for":["developers prototyping rapidly and willing to trade some code review overhead for speed","non-native English speakers who find it easier to describe intent in comments than write code directly","teams using CodeGeeX as a code-generation accelerator for well-specified, low-complexity functions"],"limitations":["Requires well-written, unambiguous comments — vague or incomplete descriptions produce incorrect or incomplete code","No multi-line context awareness — cannot infer intent from surrounding function signatures or class structure beyond immediate context","Generated code may require manual review and refactoring, especially for complex algorithms or edge cases","Comment-to-code mapping is lossy — model may misinterpret intent if comment uses domain-specific jargon or non-English languages"],"requires":["Visual Studio Code with CodeGeeX extension installed","Active internet connection to Zhipu AI backend","Well-formed comment in supported language (English, Chinese documented; others unknown)"],"input_types":["natural language comment (string)","preceding code context (implicit from file)","target programming language (inferred from file extension)"],"output_types":["generated code snippet (string, single or multi-line)","code inserted into editor at cursor or following line"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_10","uri":"capability://code.generation.editing.multilingual.code.translation.and.cross.language.conversion","name":"multilingual code translation and cross-language conversion","description":"Converts code from one programming language to another by analyzing the source code's logic, structure, and intent, then generating equivalent code in the target language. The model preserves semantics and idioms while adapting to target language conventions (e.g., Python list comprehensions vs. Java streams). Triggered via right-click menu or command palette (exact trigger unknown), with output displayed inline or in sidebar. Supported languages include Python, JavaScript, TypeScript, Java, C++, C#, Go, PHP, and 12+ others.","intents":["I want to convert a Python script to JavaScript for a web project","I need to port legacy code from one language to another without rewriting from scratch","I want to understand how an algorithm is implemented in different languages"],"best_for":["polyglot teams migrating code between tech stacks","developers learning new languages by comparing implementations","teams maintaining code in multiple languages and needing synchronization"],"limitations":["Language-specific idioms and best practices may not be preserved — generated code may be non-idiomatic","Semantic differences between languages (e.g., memory management, type systems) may cause incorrect translations","External dependencies and library calls may not have direct equivalents in target language","Generated code requires manual review and testing — not production-ready without validation","No support for partial translation or selective language features (unknown if supported)"],"requires":["Visual Studio Code with CodeGeeX extension","Source code in supported language (20+ languages documented)","Target language selection (mechanism unknown)","Active internet connection to Zhipu AI backend"],"input_types":["source code (string, selected or full file)","source language (inferred from file extension)","target language (user-specified or inferred)"],"output_types":["translated code (string, in target language)","translation notes or warnings (unknown if provided)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_11","uri":"capability://code.generation.editing.code.review.and.quality.analysis","name":"code review and quality analysis","description":"Analyzes code for quality issues, design patterns, best practices, and potential improvements. The model performs static analysis on selected code or entire files, identifying violations of coding standards, inefficient patterns, and architectural concerns. Output includes a list of issues with explanations and suggested improvements. Triggered via right-click menu or command palette (exact trigger unknown); full feature details are undocumented.","intents":["I want an automated code review before submitting a pull request","I need to identify code quality issues and get suggestions for improvement","I want to ensure my code follows best practices and coding standards"],"best_for":["teams without dedicated code review resources or static analysis tools","developers seeking quick feedback on code quality before peer review","projects adopting coding standards and needing automated enforcement"],"limitations":["Feature is mentioned in description but not documented in UI or command list — full capabilities unknown","Quality analysis is pattern-based and may miss domain-specific or architectural issues","No support for custom coding standards or rule configuration (unknown if supported)","Suggestions are generic and may not align with team-specific conventions","No integration with linters or static analysis tools (unknown if supported)"],"requires":["Visual Studio Code with CodeGeeX extension","Selected code or entire file","Active internet connection to Zhipu AI backend"],"input_types":["source code (string, selected or full file)","target language (inferred from file extension)"],"output_types":["list of quality issues (text with explanations)","suggested improvements (text with code examples)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_2","uri":"capability://code.generation.editing.automated.inline.comment.and.docstring.generation","name":"automated inline comment and docstring generation","description":"Analyzes selected code (function, method, code block, or entire file) and generates inline comments or docstrings explaining the logic, parameters, and return values. The model infers intent from code structure (variable names, control flow, API calls) and produces comments in the user's preferred language (English or Chinese documented). Output is inserted inline or as a separate docstring block, with formatting adapted to the language (Python docstrings, JSDoc, etc.).","intents":["I want to auto-generate comments for legacy code that lacks documentation","I need to add docstrings to functions before committing to a shared codebase","I want line-by-line comments explaining complex algorithms without manually writing them"],"best_for":["teams improving code documentation quality without manual effort","developers documenting legacy codebases with minimal context loss","open-source maintainers preparing code for public release with comprehensive comments"],"limitations":["Generated comments may be verbose, redundant, or state the obvious (e.g., `x = x + 1 // increment x`)","Cannot infer business logic or domain-specific intent from code alone — comments reflect syntactic structure, not semantic purpose","Language-specific docstring formats (JSDoc, Sphinx, etc.) may require post-generation cleanup","Comments generated for obfuscated or non-idiomatic code may be inaccurate or misleading"],"requires":["Visual Studio Code with CodeGeeX extension","Selected code block or entire file to analyze","Active internet connection to Zhipu AI backend"],"input_types":["source code selection (string)","target language (inferred from file extension)","comment style preference (unknown if configurable)"],"output_types":["inline comments (string, inserted into editor)","docstring block (language-specific format)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_3","uri":"capability://code.generation.editing.unit.test.generation.from.function.signatures.and.implementations","name":"unit test generation from function signatures and implementations","description":"Analyzes a selected function or method and generates unit test code covering common cases, edge cases, and error conditions. The model infers input types, return types, and expected behaviors from the function signature and implementation, then produces test code in the appropriate testing framework (Jest for JavaScript, pytest for Python, JUnit for Java, etc.). Tests are generated with assertions and can be inserted into a test file or displayed in the sidebar for review.","intents":["I want to generate unit tests for a function without manually writing test cases","I need to create test coverage for legacy functions that lack tests","I want to generate edge case tests that I might have missed in manual testing"],"best_for":["developers working under time pressure who need quick test coverage without sacrificing quality","teams adopting test-driven development and needing scaffolding for test cases","QA engineers generating test cases from function specifications"],"limitations":["Generated tests may miss domain-specific edge cases or business logic constraints not evident from code","Test assertions are based on inferred expected behavior — may be incorrect if function has undocumented side effects","Framework detection is automatic but may fail for custom testing frameworks or polyglot projects","Tests require manual review and refinement; generated tests are starting points, not production-ready","No support for mocking external dependencies or async/await patterns (unknown if supported)"],"requires":["Visual Studio Code with CodeGeeX extension","Selected function or method to test","Active internet connection to Zhipu AI backend","Testing framework installed in project (Jest, pytest, JUnit, etc.)"],"input_types":["function signature and implementation (code string)","target language (inferred from file extension)","testing framework (auto-detected or user-specified, unknown)"],"output_types":["test code (string, framework-specific syntax)","test cases with assertions (multiple test functions)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_4","uri":"capability://text.generation.language.code.explanation.and.semantic.analysis","name":"code explanation and semantic analysis","description":"Analyzes selected code or entire file and generates a natural language explanation of what the code does, how it works, and why it's structured that way. The model performs semantic analysis of control flow, function calls, variable usage, and algorithmic patterns, then produces a human-readable explanation in English or Chinese. Triggered via `/explain` command in sidebar, with output displayed in the chat panel.","intents":["I need to understand what a complex function does without reading every line","I want to explain legacy code to a junior developer or new team member","I need to document the purpose and logic of a code snippet for a code review"],"best_for":["developers onboarding to unfamiliar codebases and needing quick understanding","technical leads explaining code decisions to non-technical stakeholders","educators teaching programming concepts using real code examples"],"limitations":["Explanations are based on code structure alone — cannot infer business context, requirements, or design decisions","Complex algorithms may be oversimplified or explained incorrectly if the model misinterprets intent","Explanations are verbose and may include obvious statements (e.g., `this loop iterates over an array`)","No support for explaining code comments or docstrings — only code logic","Language-specific idioms or framework-specific patterns may be misexplained"],"requires":["Visual Studio Code with CodeGeeX extension","Selected code block or entire file","Active internet connection to Zhipu AI backend"],"input_types":["source code (string, selected or full file)","target language (inferred from file extension)"],"output_types":["natural language explanation (text, displayed in sidebar chat panel)","structured explanation (unknown if available)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_5","uri":"capability://code.generation.editing.bug.detection.and.automated.code.fixing","name":"bug detection and automated code fixing","description":"Analyzes selected code or entire file to identify potential bugs (null pointer dereferences, off-by-one errors, type mismatches, logic errors) and generates corrected code with fixes applied. The model uses pattern matching and semantic analysis to detect common bug categories, then produces a patched version of the code with explanations of what was fixed. Triggered via `/fixbug` command in sidebar, with output displayed as a diff or replacement code.","intents":["I want to identify and fix bugs in my code without running a debugger","I need to review code for common error patterns before committing","I want to understand what bugs exist in a function and how to fix them"],"best_for":["developers doing code review and needing quick bug detection","teams without access to static analysis tools (linters, type checkers)","developers debugging production issues and needing quick fixes"],"limitations":["Bug detection is pattern-based and may miss domain-specific bugs or logic errors not in training data","Generated fixes may introduce new bugs or change intended behavior if the model misunderstands intent","Cannot detect bugs that require runtime context (e.g., race conditions, memory leaks in specific scenarios)","No support for detecting security vulnerabilities or performance issues (unknown if supported)","Fixes are suggestions only — require manual testing and validation before deployment"],"requires":["Visual Studio Code with CodeGeeX extension","Selected code block or entire file","Active internet connection to Zhipu AI backend"],"input_types":["source code (string, selected or full file)","target language (inferred from file extension)"],"output_types":["fixed code (string, with bugs corrected)","explanation of bugs found and fixes applied (text)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_6","uri":"capability://text.generation.language.conversational.code.q.a.with.model.selection","name":"conversational code q&a with model selection","description":"Provides an interactive chat interface (sidebar 'Ask CodeGeeX' panel) where users can ask natural language questions about code, request explanations, or seek coding advice. The model processes user queries and optional selected code context, then generates responses. Users can select between Pro and Lite model variants, with Pro offering more detailed/accurate responses at higher latency or cost (pricing model unknown). Responses are streamed into the chat panel with full conversation history.","intents":["I want to ask a coding question and get an AI-generated answer with code examples","I need to understand how to implement a specific algorithm or design pattern","I want to get advice on code structure or best practices for a problem"],"best_for":["developers seeking quick coding advice without leaving VS Code","learners studying programming concepts with interactive Q&A","teams using CodeGeeX as an internal coding knowledge base"],"limitations":["Responses are generated without access to external documentation or web search — may be outdated or incomplete","No multi-turn context awareness documented — each question may be treated independently","Pro vs. Lite model selection mechanism is undocumented; unclear how to switch models or what the quality difference is","No support for follow-up questions or clarifications (unknown if supported)","Responses may hallucinate or provide incorrect information if question is outside training distribution"],"requires":["Visual Studio Code with CodeGeeX extension","Active internet connection to Zhipu AI backend","Optional: selected code context to include in query"],"input_types":["natural language question (text)","optional selected code (string)","model variant selection (Pro or Lite, unknown if persisted)"],"output_types":["natural language response (text, streamed into chat panel)","code examples (optional, embedded in response)","conversation history (maintained in sidebar)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_7","uri":"capability://text.generation.language.inline.chat.with.code.context.and.editing","name":"inline chat with code context and editing","description":"Enables real-time conversational interaction directly within the editor via `Ctrl+I` (Windows) or `Command+I` (Mac) keybinding. Users can ask questions, request code modifications, or seek explanations while the cursor is positioned in code. The model receives the current file context, cursor position, and selected code, then generates responses or code edits that can be applied directly to the editor. Full feature details are truncated in documentation; complete capabilities unknown.","intents":["I want to ask a quick question about the code I'm currently editing without switching to a sidebar","I need to refactor a code block and want inline suggestions without leaving the editor","I want to get an explanation of the code at my cursor position"],"best_for":["developers who prefer keyboard-driven workflows and minimal context switching","teams using inline chat for rapid code iteration and pair-programming simulation"],"limitations":["Feature documentation is truncated — full capabilities, response types, and limitations unknown","Unclear if inline chat supports code edits or only read-only explanations","No documented support for multi-file context or project-wide analysis","Keybinding conflicts with other VS Code extensions possible but undocumented"],"requires":["Visual Studio Code with CodeGeeX extension","Keybinding enabled: `Ctrl+I` (Windows) or `Command+I` (Mac)","Active internet connection to Zhipu AI backend"],"input_types":["natural language query (text, typed into inline chat box)","current file context (implicit)","cursor position (implicit)","selected code (optional)"],"output_types":["natural language response (text, displayed inline or in popup)","code suggestions or edits (unknown if supported)","explanation or analysis (text)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_8","uri":"capability://tool.use.integration.command.palette.integration.with.preset.coding.tasks","name":"command palette integration with preset coding tasks","description":"Provides a command palette interface (triggered via `/` prefix in sidebar chat) with preset commands for common coding tasks: `/explain` (code explanation), `/comment` (comment generation), `/fixbug` (bug fixing), `/tests` (unit test generation), and others (full list unknown). Each command is a shortcut to a specific AI action, with parameters inferred from selected code or user input. Commands can be chained or combined, and custom commands may be supported (unknown).","intents":["I want quick access to common AI coding tasks without navigating menus","I need to run a specific task (e.g., generate tests) with a single command","I want to discover available AI capabilities through a command palette"],"best_for":["power users comfortable with command-line interfaces and keyboard shortcuts","teams standardizing on preset tasks and wanting discoverable, consistent workflows"],"limitations":["Command list is incomplete in documentation — full set of available commands unknown","No support for custom commands or task composition (unknown if supported)","Command parameters are implicit (inferred from context) — no explicit parameter passing documented","No command history or favorites feature documented"],"requires":["Visual Studio Code with CodeGeeX extension","Sidebar chat panel open","Selected code or file context (for most commands)"],"input_types":["command name (string, prefixed with `/`)","optional parameters (inferred from context)"],"output_types":["task execution result (varies by command: code, comments, tests, explanations, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-aminer-codegeex__cap_9","uri":"capability://tool.use.integration.right.click.context.menu.integration.for.code.actions","name":"right-click context menu integration for code actions","description":"Provides context-sensitive menu options accessible via right-click on selected code, including: 'Add Comment' (comment generation), 'Generate Unit Tests' (test generation), and others (full list unknown). Each menu option triggers a specific AI action on the selected code block, with results displayed inline or in the sidebar. Menu items are language-aware and adapt to the file type being edited.","intents":["I want to quickly generate comments or tests for selected code without typing commands","I need a discoverable way to access AI coding tasks without memorizing keyboard shortcuts","I want context-specific menu options that adapt to the code I'm working with"],"best_for":["developers preferring mouse-driven workflows or discovering features through UI","teams with less keyboard-shortcut familiarity or accessibility needs"],"limitations":["Menu options are limited compared to command palette — full list unknown","Right-click menu can become cluttered if many extensions add context menu items","No support for custom menu items or task composition (unknown if supported)","Menu structure and organization are undocumented"],"requires":["Visual Studio Code with CodeGeeX extension","Selected code block in editor","Right-click context menu enabled (default)"],"input_types":["selected code (string)","menu option selection (user click)"],"output_types":["task execution result (comments, tests, explanations, etc.)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":53,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (version not specified in documentation)","Active internet connection to Zhipu AI inference backend","No explicit API key configuration documented; authentication mechanism unknown","Visual Studio Code with CodeGeeX extension installed","Active internet connection to Zhipu AI backend","Well-formed comment in supported language (English, Chinese documented; others unknown)","Visual Studio Code with CodeGeeX extension","Source code in supported language (20+ languages documented)","Target language selection (mechanism unknown)","Selected code or entire file"],"failure_modes":["Single-file context only — cannot analyze project-wide patterns or cross-file dependencies, limiting accuracy for large codebases","Cloud-dependent inference introduces variable latency (typically 500ms–2s per suggestion) compared to local models","No offline mode — requires active internet connection to Zhipu AI backend for all completions","Suggestion quality degrades for niche languages or domain-specific code patterns outside training distribution","Requires well-written, unambiguous comments — vague or incomplete descriptions produce incorrect or incomplete code","No multi-line context awareness — cannot infer intent from surrounding function signatures or class structure beyond immediate context","Generated code may require manual review and refactoring, especially for complex algorithms or edge cases","Comment-to-code mapping is lossy — model may misinterpret intent if comment uses domain-specific jargon or non-English languages","Language-specific idioms and best practices may not be preserved — generated code may be non-idiomatic","Semantic differences between languages (e.g., memory management, type systems) may cause incorrect translations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.81,"quality":0.49,"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":"2026-05-03T15:20:29.937Z","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=codegeex-ai-coding-assistant","compare_url":"https://unfragile.ai/compare?artifact=codegeex-ai-coding-assistant"}},"signature":"iPG85oyf9mhWYPA68eEazBK0+jGm/7Kb98AXXnPxT5nOL+IcvieJ4/E9PsmwPz9I1Gw4BtVDiyXiAKo6ir92Dg==","signedAt":"2026-06-20T20:14:36.900Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codegeex-ai-coding-assistant","artifact":"https://unfragile.ai/codegeex-ai-coding-assistant","verify":"https://unfragile.ai/api/v1/verify?slug=codegeex-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"}}