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Uses constraint-based test generation to create edge cases (boundary values, empty inputs, maximum constraints) and random test case generation for stress testing, reporting pass/fail status and execution metrics (runtime, memory usage).","intents":["I want to verify my solution works before submitting to a judge","I need to generate edge case tests that my solution might fail on","I want to benchmark my solution's performance against the problem constraints","I need to identify which test cases my solution fails and debug from there"],"best_for":["competitive programmers validating solutions before submission","interview candidates stress-testing their implementations","developers building robust algorithmic code with high confidence","educators creating automated grading systems for programming assignments"],"limitations":["Test case generation is heuristic-based and may miss subtle edge cases not covered by constraint analysis","Execution environment may have different performance characteristics than actual judge (different CPU, memory limits, compiler optimizations)","Cannot validate correctness for problems with non-deterministic or floating-point outputs without custom validators","Test generation quality depends on problem statement clarity — ambiguous constraints produce incomplete test suites"],"requires":["TypeScript runtime","Electron framework","Language-specific compiler/interpreter for code execution","Problem constraints in structured or parseable format"],"input_types":["solution code (text)","problem statement (text)","constraints (structured data or text)","example test cases (JSON or text)","custom validator function (optional, code)"],"output_types":["test case results (JSON with pass/fail status)","execution metrics (runtime, memory, CPU usage)","failure details (input, expected output, actual output)","coverage report (which constraints are tested)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-inulute--phantom-lens__cap_4","uri":"capability://planning.reasoning.problem.difficulty.estimation.and.solution.approach.recommendation","name":"problem difficulty estimation and solution approach recommendation","description":"Analyzes problem statements to estimate difficulty level (easy/medium/hard) and recommend optimal solution approaches by identifying problem patterns (sorting, dynamic programming, graph traversal, etc.) and matching them against a knowledge base of algorithmic techniques. Provides confidence scores for each recommendation and explains the reasoning behind the difficulty assessment.","intents":["I want to know if this problem is within my current skill level before attempting it","I need a hint about which algorithmic technique to use without seeing the full solution","I want to understand what makes this problem harder than similar ones I've solved","I need to prioritize which problems to practice based on difficulty progression"],"best_for":["interview candidates building targeted practice plans","competitive programmers assessing problem difficulty before contests","students learning to recognize algorithmic patterns","educators creating difficulty-balanced problem sets"],"limitations":["Difficulty estimation is subjective and varies by individual experience — a 'medium' problem may be hard for beginners","Pattern recognition depends on problem statement clarity and may misclassify problems with unusual formulations","Recommendations are based on training data and may not account for novel or hybrid algorithmic approaches","Confidence scores are heuristic-based and don't guarantee accuracy of recommendations"],"requires":["TypeScript runtime","Electron framework","LLM with pattern recognition capabilities","Pre-trained knowledge base of algorithmic patterns and techniques"],"input_types":["problem statement (text)","constraints (structured or text)","user skill level (optional, enum: beginner/intermediate/advanced)","problem category (optional, string)"],"output_types":["difficulty level (enum: easy/medium/hard)","confidence score (0-1 float)","recommended approaches (array of strings with confidence scores)","reasoning explanation (text)","similar problems (array of problem references)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-inulute--phantom-lens__cap_5","uri":"capability://code.generation.editing.offline.first.code.generation.with.local.llm.support","name":"offline-first code generation with local llm support","description":"Enables code generation without requiring cloud API calls by supporting local LLM inference (via Ollama, llama.cpp, or similar), storing model weights locally and executing inference on the user's machine. Implements prompt caching and context compression to reduce memory footprint and inference latency, with fallback to cloud APIs when local inference is unavailable or insufficient.","intents":["I want to use code generation without sending my code to external servers","I need code generation to work offline or in restricted network environments","I want to reduce latency by running inference locally instead of waiting for cloud API responses","I need to avoid API rate limits and costs associated with cloud-based code generation"],"best_for":["security-conscious developers in regulated industries","competitive programmers in offline contest environments","users with unreliable internet connectivity","organizations with strict data residency requirements"],"limitations":["Local inference quality depends on model size and available GPU memory — smaller models may produce lower-quality solutions","Inference latency on CPU-only machines can exceed cloud API latency, negating the offline advantage","Requires significant disk space for model weights (7B parameter model ≈ 4-7GB, 13B ≈ 8-13GB)","Setup complexity is higher than cloud-only approach — requires model download, runtime installation, and GPU driver configuration"],"requires":["TypeScript runtime","Electron framework","Local LLM runtime (Ollama, llama.cpp, vLLM, or similar)","8GB+ RAM (16GB+ recommended for larger models)","GPU with CUDA/Metal support (optional but strongly recommended for acceptable latency)"],"input_types":["problem statement (text)","code context (text)","model selection (enum: local/cloud/hybrid)","inference parameters (JSON: temperature, max_tokens, etc.)"],"output_types":["generated code (text)","inference metadata (JSON: latency, model used, tokens generated)","fallback indicator (boolean: whether cloud API was used)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-inulute--phantom-lens__cap_6","uri":"capability://automation.workflow.interview.session.simulation.with.real.time.feedback","name":"interview session simulation with real-time feedback","description":"Simulates a live technical interview by presenting problems with time constraints, recording solution attempts, and providing real-time feedback on code quality, approach, and communication clarity. Tracks metrics like time-to-solution, code efficiency, and explanation quality, comparing performance against historical benchmarks and providing actionable improvement suggestions.","intents":["I want to practice technical interviews in a realistic time-pressured environment","I need feedback on my problem-solving approach and communication during interviews","I want to track my improvement over time and identify weak areas","I need to simulate the exact interview format used by specific companies"],"best_for":["job candidates preparing for technical interviews","career changers building interview confidence","developers practicing for promotion interviews","interview coaches creating personalized training programs"],"limitations":["Simulated interviews lack the psychological pressure and interpersonal dynamics of real interviews","Feedback is generated by LLM and may not match actual interviewer expectations or company-specific evaluation criteria","Time constraints are artificial and don't account for real-world interruptions or clarification questions","Communication quality assessment is limited to written explanations — doesn't evaluate verbal communication or body language"],"requires":["TypeScript runtime","Electron framework","LLM with strong reasoning and evaluation capabilities","Problem database with company-specific problem sets","Timer and session recording functionality"],"input_types":["interview format (enum: phone/onsite/virtual)","company name (optional, string)","difficulty level (enum: easy/medium/hard)","problem category (optional, array of strings)","time limit (integer, minutes)"],"output_types":["problem statement (text)","solution code (text)","execution results (JSON)","feedback report (structured: approach quality, code quality, communication, time management)","performance metrics (JSON: time-to-solution, efficiency score, explanation clarity score)","improvement suggestions (array of actionable recommendations)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-inulute--phantom-lens__cap_7","uri":"capability://code.generation.editing.solution.comparison.and.optimization.analysis","name":"solution comparison and optimization analysis","description":"Compares multiple solution approaches to the same problem by analyzing time complexity, space complexity, code readability, and practical performance metrics. Generates a ranked comparison table showing trade-offs between approaches (e.g., O(n log n) sort vs O(n) counting sort with space overhead), and recommends the optimal approach based on problem constraints and user preferences.","intents":["I want to understand the trade-offs between different solution approaches","I need to choose between multiple valid solutions based on performance characteristics","I want to learn why one approach is better than another for specific constraints","I need to optimize my solution after getting it working"],"best_for":["developers optimizing existing solutions","students learning algorithmic trade-offs","interview candidates explaining solution choices","performance engineers analyzing algorithmic bottlenecks"],"limitations":["Complexity analysis is theoretical and may not match actual performance due to constant factors and cache behavior","Practical performance comparison requires execution on representative test cases — results may vary with different inputs","Readability assessment is subjective and depends on coding style preferences","Optimization recommendations may not account for language-specific performance characteristics or compiler optimizations"],"requires":["TypeScript runtime","Electron framework","LLM with complexity analysis capabilities","Code execution environment for practical benchmarking","Multiple solution implementations (at least 2)"],"input_types":["solution code array (array of code strings)","problem statement (text)","constraints (structured data)","test cases (JSON array)","optimization criteria (enum: speed/memory/readability/balance)"],"output_types":["comparison table (structured: approach, time complexity, space complexity, readability score, practical performance)","trade-off analysis (text explanation of pros/cons)","recommendation (string with reasoning)","optimization suggestions (array of specific improvements)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-inulute--phantom-lens__cap_8","uri":"capability://memory.knowledge.problem.pattern.library.with.searchable.examples","name":"problem pattern library with searchable examples","description":"Maintains a searchable library of algorithmic patterns (two-pointer, sliding window, binary search, dynamic programming, graph traversal, etc.) with canonical problem examples, solution templates, and complexity analysis. Enables semantic search to find relevant patterns based on problem description, and provides pattern-specific code templates that can be adapted to new problems.","intents":["I want to find problems similar to one I'm stuck on to learn the pattern","I need a template for a specific algorithmic pattern to start my solution","I want to understand all the variations of a pattern and when to use each","I need to build a personal knowledge base of patterns I've learned"],"best_for":["students building algorithmic pattern recognition skills","developers learning new problem domains","interview candidates reviewing pattern-based approaches","educators creating pattern-based curriculum"],"limitations":["Pattern library is static and may not include newly emerging patterns or company-specific variations","Semantic search quality depends on problem description clarity — vague descriptions may return irrelevant patterns","Templates are generic and require significant customization for specific problems","Pattern categorization is subjective — some problems fit multiple patterns, and library organization may not match user mental models"],"requires":["TypeScript runtime","Electron framework","Vector database or semantic search index for pattern matching","Pre-populated pattern library with examples and templates"],"input_types":["search query (text: problem description or pattern name)","filter criteria (optional, JSON: difficulty, language, company)","pattern name (optional, string)"],"output_types":["pattern list (array of pattern objects with name, description, complexity)","example problems (array of problem references)","code template (text with placeholders)","complexity analysis (text and mathematical notation)","related patterns (array of similar patterns)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["TypeScript runtime environment","Electron framework (v13+) for desktop application","API access to LLM provider (OpenAI, Anthropic, or local model)","Problem statement in text or structured format","TypeScript runtime","Electron framework","LLM with multi-language training data","Language-specific compiler/interpreter for validation (optional but recommended)","LLM with strong reasoning capabilities","Code editor with syntax highlighting and annotation support"],"failure_modes":["Generated solutions may not be optimal for all edge cases or large input constraints","Requires clear problem statement parsing — ambiguous or poorly formatted problems may produce incorrect solutions","No guarantee of solution correctness without independent verification against test cases","Context window limitations may affect solution quality for very complex multi-part problems","Language-specific libraries and idioms may not have direct equivalents across all target languages","Performance characteristics vary significantly between languages — generated code may not maintain equivalent time/space complexity across all targets","Requires language-specific syntax validation to ensure generated code is actually compilable","Some advanced language features (e.g., Rust ownership system) cannot be automatically synthesized from higher-level representations","Explanations are generated by LLM and may contain conceptual errors or oversimplifications","Complex algorithms with multiple valid approaches may produce explanations that don't match the candidate's preferred mental model","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.11919306146416289,"quality":0.43,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:13.678Z","last_commit":"2026-02-22T15:05:42Z"},"community":{"stars":106,"forks":7,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=inulute--phantom-lens","compare_url":"https://unfragile.ai/compare?artifact=inulute--phantom-lens"}},"signature":"v8vOjdyZQul7RgzFFzpINdaibLGXZ9nZKG+pGpsamwNMpAhQKL5KOqNy6wDXqW81o2KTEEhlXdlu3dRPWasqCA==","signedAt":"2026-06-22T01:57:42.816Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/inulute--phantom-lens","artifact":"https://unfragile.ai/inulute--phantom-lens","verify":"https://unfragile.ai/api/v1/verify?slug=inulute--phantom-lens","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"}}