{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-blinky","slug":"blinky","name":"Blinky","type":"repo","url":"https://github.com/seahyinghang8/blinky","page_url":"https://unfragile.ai/blinky","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-blinky__cap_0","uri":"capability://code.generation.editing.vscode.integrated.real.time.error.detection.and.diagnosis","name":"vscode-integrated real-time error detection and diagnosis","description":"Monitors VSCode editor for runtime errors, compilation failures, and linting issues in real-time by hooking into the editor's diagnostic system and language server protocol (LSP) outputs. Captures error context including stack traces, file locations, and error messages, then feeds them into an LLM reasoning loop for root-cause analysis without requiring manual error reporting.","intents":["I want the debugger to automatically catch and explain errors as they occur in my code","I need quick diagnosis of why my code is failing without manually inspecting stack traces","I want to understand the root cause of compilation or runtime errors in context"],"best_for":["solo developers iterating rapidly on code","teams using VSCode as primary IDE","developers working with dynamically-typed languages where errors surface at runtime"],"limitations":["Requires VSCode extension runtime — cannot debug code outside the editor context","Depends on LSP availability for the target language — unsupported languages will have limited error detection","Real-time monitoring adds overhead to editor responsiveness if error frequency is very high (>100 errors/minute)","Cannot intercept runtime errors from external processes or headless execution environments"],"requires":["VSCode 1.80 or later","Node.js 16+ for extension runtime","Language server or linter configured for the target language","API key for LLM provider (OpenAI, Anthropic, or local Ollama instance)"],"input_types":["diagnostic messages from VSCode LSP","error stack traces","source code context from editor buffer","file paths and line numbers"],"output_types":["natural language error explanation","root cause analysis","suggested fixes or code patches","links to relevant documentation"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_1","uri":"capability://planning.reasoning.llm.powered.root.cause.analysis.with.code.context","name":"llm-powered root-cause analysis with code context","description":"Takes captured error information and surrounding source code, constructs a multi-turn reasoning prompt that includes the error message, stack trace, relevant code snippets, and file context, then uses an LLM (via OpenAI, Anthropic, or local Ollama) to perform chain-of-thought reasoning to identify root causes. 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Exposes metrics via a dashboard or sidebar panel, allowing users to identify performance bottlenecks. Logs detailed timing information for each step of the debugging pipeline (error detection → context extraction → LLM inference → fix suggestion).","intents":["I want to understand why the debugger is slow for certain errors","I need to track how much the debugger is costing me in LLM API calls","I want to optimize the debugger's performance for my workflow"],"best_for":["developers who want to optimize debugger performance","teams that want to track LLM API costs and usage","developers debugging the debugger itself"],"limitations":["Metrics collection adds overhead — timing instrumentation may skew results","Dashboard rendering may impact editor performance if metrics are updated frequently","No built-in alerting — users must manually check metrics to identify issues","Metrics are local to the editor instance — no cross-device or team-wide aggregation","Privacy concern — detailed logs may contain sensitive code or error information"],"requires":["VSCode 1.80 or later with webview API support","Local storage for metrics (VSCode workspace storage)"],"input_types":["timing data from each debugging operation","LLM API responses (latency, token count, cost)","cache hit/miss events"],"output_types":["performance metrics (latency, cache hit rate, cost)","dashboard visualization","detailed logs for troubleshooting"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_11","uri":"capability://planning.reasoning.incremental.error.analysis.with.progressive.disclosure","name":"incremental error analysis with progressive disclosure","description":"Analyzes errors in stages, starting with a quick explanation of the error message, then progressively revealing deeper analysis (root cause, related code patterns, suggested fixes) as the user requests more detail. 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Uses AST-aware or line-based patching to ensure fixes are applied to the correct location even if the file has been modified since error detection.","intents":["I want the debugger to suggest and apply code fixes automatically","I need to see the diff of a proposed fix before applying it","I want to quickly iterate on fixes without manually editing code"],"best_for":["developers working on simple, well-defined bugs (off-by-one errors, missing imports, type mismatches)","teams with high code churn where manual fixes are time-consuming","developers who trust LLM-generated code and want to move fast"],"limitations":["Fix quality varies — LLM may generate syntactically correct but semantically incorrect patches","Cannot apply fixes that require architectural changes or multi-file refactoring","One-click patching bypasses code review — risky in production codebases without additional safeguards","File offset tracking may fail if the file is heavily modified between error detection and fix application","No rollback mechanism — applied fixes are permanent unless manually reverted"],"requires":["VSCode 1.80 or later with extension API support","Write permissions on the source file","LLM provider with code generation capability (GPT-4, Claude, or equivalent)"],"input_types":["LLM-generated fix suggestions","source code file content","error context and location"],"output_types":["unified diff format","inline code block with highlighted changes","patched source file (after user confirmation)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_3","uri":"capability://code.generation.editing.multi.language.error.detection.with.lsp.fallback","name":"multi-language error detection with lsp fallback","description":"Detects errors across multiple programming languages (JavaScript, TypeScript, Python, Go, Rust, etc.) by querying VSCode's language server protocol (LSP) implementations for each language. Falls back to regex-based or heuristic error detection for languages without LSP support, ensuring broad language coverage. Normalizes error messages across different language servers into a consistent format for LLM processing.","intents":["I want debugging to work across all the languages in my polyglot project","I need error detection even for languages without mature tooling","I want consistent error explanations regardless of which language I'm debugging"],"best_for":["polyglot teams using multiple languages in a single project","developers working with less common languages (Rust, Go, Elixir)","teams with heterogeneous tech stacks"],"limitations":["Error detection quality depends on LSP maturity — some languages have incomplete or slow language servers","Regex-based fallback for unsupported languages is brittle and may miss errors or produce false positives","Normalizing error messages across different language servers may lose language-specific context","Performance degrades if multiple language servers are active simultaneously (>5 concurrent servers)","Some languages (e.g., dynamically-typed languages) have limited static error detection"],"requires":["VSCode 1.80 or later","Language server extensions installed for target languages (e.g., Pylance for Python, rust-analyzer for Rust)","Node.js 16+ for extension runtime"],"input_types":["LSP diagnostic messages","source code in various languages","file extensions and language identifiers"],"output_types":["normalized error messages","language-agnostic error metadata (file, line, column, severity)","language-specific context when available"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_4","uri":"capability://code.generation.editing.contextual.code.snippet.extraction.and.summarization","name":"contextual code snippet extraction and summarization","description":"Automatically extracts relevant code snippets surrounding an error (function definition, class context, import statements, related function calls) using AST parsing or line-based heuristics. Summarizes large code blocks to fit within LLM context windows while preserving semantic meaning. Includes file structure metadata (imports, dependencies, function signatures) to give the LLM a complete picture of the code context.","intents":["I want the debugger to understand the full context of my error, not just the error line","I need the LLM to see related functions and imports to diagnose the bug correctly","I want to avoid sending my entire codebase to the LLM for privacy and cost reasons"],"best_for":["developers working with large codebases where full-file context is impractical","teams with privacy concerns about sending code to cloud LLM providers","developers using local LLM instances with limited context windows"],"limitations":["AST parsing is language-specific — requires parsers for each supported language","Snippet extraction may miss non-obvious dependencies (e.g., global state, monkey-patching)","Summarization may lose important context if the code is highly interdependent","Context window limits still apply — very large functions or complex call chains may be truncated","No semantic understanding of code flow — may include irrelevant snippets or miss relevant ones"],"requires":["Tree-sitter or language-specific AST parser for target languages","VSCode extension API for accessing editor buffer and file system","Sufficient memory to parse and analyze large files"],"input_types":["error location (file, line, column)","source code file content","language identifier"],"output_types":["extracted code snippets with line numbers","summarized code blocks","file structure metadata (imports, function signatures)","relevance scores for each snippet"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_5","uri":"capability://memory.knowledge.persistent.debugging.session.state.and.conversation.history","name":"persistent debugging session state and conversation history","description":"Maintains a stateful debugging session that persists error context, LLM conversation history, applied fixes, and user feedback across multiple interactions. Stores session metadata (timestamps, error counts, fix success rates) and allows users to resume debugging sessions or review past error analyses. Uses local file storage or optional cloud sync to preserve session state across editor restarts.","intents":["I want to continue debugging from where I left off after closing the editor","I need to review past error analyses and fixes I've applied","I want to track which errors I've encountered and how I fixed them"],"best_for":["developers working on long-running debugging sessions","teams that want to build institutional knowledge of common errors","developers who need audit trails of fixes applied to code"],"limitations":["Session storage adds disk I/O overhead — may impact editor responsiveness if sessions are very large (>100MB)","Cloud sync requires additional infrastructure and introduces privacy/security considerations","Session state may become stale if code is modified externally (e.g., git pull, merge)","No built-in garbage collection — old sessions accumulate and consume disk space","Conversation history is not encrypted by default — sensitive code may be exposed if storage is compromised"],"requires":["VSCode extension storage API (local workspace storage)","Optional: cloud storage service (e.g., GitHub, AWS S3) for session sync","Sufficient disk space for session history"],"input_types":["error context and LLM responses","user feedback and fix confirmations","session metadata (timestamps, file paths)"],"output_types":["session history (JSON or structured format)","conversation transcripts","fix application logs","session statistics (error count, fix success rate)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_6","uri":"capability://automation.workflow.interactive.debugging.ui.with.inline.error.annotations","name":"interactive debugging ui with inline error annotations","description":"Renders error explanations, root-cause analyses, and fix suggestions directly in the VSCode editor using inline code lenses, hover tooltips, and sidebar panels. Provides interactive UI elements (buttons, dropdowns, text inputs) for users to ask follow-up questions, apply fixes, or dismiss errors without leaving the editor. Uses VSCode's decoration and webview APIs to create a rich, integrated debugging experience.","intents":["I want to see error explanations without switching to a separate tool or terminal","I need to interact with the debugger (ask questions, apply fixes) without leaving the editor","I want visual indicators of which errors have been analyzed or fixed"],"best_for":["developers who prefer visual, integrated debugging experiences","teams using VSCode as their primary IDE","developers who want to minimize context switching during debugging"],"limitations":["VSCode UI APIs have limited customization — complex interactions may not be possible","Rendering many inline annotations (>50 errors) can degrade editor performance","Webview-based panels have sandbox restrictions — cannot directly access editor state without message passing","UI state is not persisted across editor restarts — users must re-open panels","Accessibility may be limited for screen reader users if UI is not properly labeled"],"requires":["VSCode 1.80 or later with webview and decoration API support","Node.js 16+ for extension runtime"],"input_types":["error context and LLM responses","user interactions (clicks, text input)","editor state (cursor position, selection)"],"output_types":["rendered inline annotations","interactive UI elements","user actions (apply fix, ask question, dismiss error)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_7","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.fallback.routing","name":"multi-provider llm abstraction with fallback routing","description":"Abstracts LLM provider differences (OpenAI, Anthropic, Ollama, etc.) behind a unified interface, allowing users to switch providers without code changes. Implements automatic fallback routing: if the primary provider fails or times out, requests are automatically routed to a secondary provider. Supports both cloud-based and local LLM instances, with configurable model selection and inference parameters (temperature, max tokens, etc.).","intents":["I want to use different LLM providers (OpenAI, Claude, local Ollama) without reconfiguring the extension","I need the debugger to continue working if my primary LLM provider is down or rate-limited","I want to use a local LLM for privacy but fall back to cloud providers if needed"],"best_for":["developers who want flexibility in LLM provider choice","teams with privacy requirements who want local-first LLM support","developers who want to minimize LLM API costs by using cheaper providers"],"limitations":["Abstraction adds latency — provider switching and fallback logic add ~50-100ms per request","Different providers have different capabilities and quality — fallback provider may produce lower-quality results","Local LLM instances (Ollama) require significant compute resources and may be slow on consumer hardware","Configuration complexity increases with multiple providers — users must manage API keys and model names for each","Fallback routing may mask provider issues — users may not realize their primary provider is failing"],"requires":["API key for at least one LLM provider (OpenAI, Anthropic, or local Ollama instance)","Network connectivity for cloud providers","VSCode 1.80 or later"],"input_types":["error context and code snippets","user configuration (provider selection, model names, API keys)","inference parameters (temperature, max tokens)"],"output_types":["LLM responses (error explanations, fix suggestions)","provider metadata (which provider was used, latency, cost)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_8","uri":"capability://memory.knowledge.error.pattern.recognition.and.deduplication","name":"error pattern recognition and deduplication","description":"Analyzes error messages and stack traces to identify recurring error patterns (e.g., 'undefined is not a function', 'type mismatch', 'import not found'). Deduplicates similar errors to avoid redundant LLM analysis, instead retrieving cached explanations from previous analyses. Uses fuzzy matching on error messages and stack trace signatures to identify similar errors even if they occur in different files or with slightly different messages.","intents":["I want to avoid re-analyzing the same error multiple times","I need to see if I've encountered this error before and how I fixed it","I want the debugger to learn from past errors and provide faster explanations"],"best_for":["developers working on long-running projects with recurring bugs","teams that want to reduce LLM API costs by caching analyses","developers who want to build a personal knowledge base of common errors"],"limitations":["Fuzzy matching may incorrectly deduplicate different errors with similar messages","Cache invalidation is non-trivial — cached explanations may become stale if code changes significantly","Storage overhead — maintaining a cache of error patterns and explanations adds disk I/O","Privacy concern — cached errors may contain sensitive code or stack traces","No built-in mechanism to update cached explanations if they become incorrect"],"requires":["Local storage for error pattern cache (VSCode workspace storage or file system)","Fuzzy matching algorithm (e.g., Levenshtein distance, semantic similarity)"],"input_types":["error messages and stack traces","source code context","previous error analyses and fixes"],"output_types":["deduplicated error patterns","cached explanations and fixes","similarity scores for matched errors"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-blinky__cap_9","uri":"capability://automation.workflow.configuration.management.and.user.preferences","name":"configuration management and user preferences","description":"Provides a settings UI for users to configure LLM provider, model selection, inference parameters, error detection sensitivity, and UI preferences. 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Includes sensible defaults for each LLM provider and language, reducing configuration burden for new users.","intents":["I want to configure which LLM provider and model the debugger uses","I need to adjust error detection sensitivity to reduce false positives","I want to share debugging configuration with my team"],"best_for":["teams that want standardized debugging configuration across developers","developers who want fine-grained control over LLM inference parameters","organizations with specific LLM provider requirements (e.g., on-premise Ollama)"],"limitations":["Configuration complexity may overwhelm new users — too many options without good defaults","Workspace-level configuration requires all team members to use VSCode with the same workspace","No validation of configuration values — invalid settings may cause runtime errors","Configuration changes require editor restart to take effect (in some cases)","No built-in migration path for configuration changes across extension versions"],"requires":["VSCode 1.80 or later with settings API support","User familiarity with VSCode settings (settings.json or UI)"],"input_types":["user configuration input (UI forms or JSON)","workspace settings (settings.json)","environment variables (for API keys)"],"output_types":["validated configuration object","default configuration for new users","configuration schema for IDE autocomplete"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["VSCode 1.80 or later","Node.js 16+ for extension runtime","Language server or linter configured for the target language","API key for LLM provider (OpenAI, Anthropic, or local Ollama instance)","API key for OpenAI (GPT-4 recommended), Anthropic (Claude), or local Ollama instance","Network connectivity for cloud-based LLM providers","Sufficient API quota to handle debugging sessions","VSCode 1.80 or later with webview API support","Local storage for metrics (VSCode workspace storage)","Multiple LLM models with different capabilities (fast + capable)"],"failure_modes":["Requires VSCode extension runtime — cannot debug code outside the editor context","Depends on LSP availability for the target language — unsupported languages will have limited error detection","Real-time monitoring adds overhead to editor responsiveness if error frequency is very high (>100 errors/minute)","Cannot intercept runtime errors from external processes or headless execution environments","LLM reasoning quality depends on model capability — weaker models (e.g., GPT-3.5) may miss subtle bugs","Requires API calls to external LLM providers, adding latency (typically 1-5 seconds per analysis)","Cannot reason about runtime state or memory contents — only static code analysis","Context window limits may truncate large files or complex stack traces","Hallucination risk: LLM may suggest incorrect fixes if error context is ambiguous","Metrics collection adds overhead — timing instrumentation may skew results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=blinky","compare_url":"https://unfragile.ai/compare?artifact=blinky"}},"signature":"sqBQURYOjcRs65mQJ+FkvObl1YMFbGw/+mHk269fg2rksYJIaXBDkN1m7bskwouUeHFuePU7p118eZYquhBrAQ==","signedAt":"2026-06-20T06:38:26.691Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/blinky","artifact":"https://unfragile.ai/blinky","verify":"https://unfragile.ai/api/v1/verify?slug=blinky","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"}}