{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_code-coach","slug":"code-coach","name":"Code Coach","type":"product","url":"https://www.trycodecoach.com","page_url":"https://unfragile.ai/code-coach","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_code-coach__cap_0","uri":"capability://memory.knowledge.faang.focused.coding.problem.curation.and.retrieval","name":"faang-focused coding problem curation and retrieval","description":"Maintains a curated database of coding problems specifically filtered and categorized by FAANG interview patterns, difficulty progression, and topic relevance. The system uses semantic tagging and problem metadata (company, frequency, topic cluster) to surface interview-relevant questions while filtering out irrelevant LeetCode-style problems. Problems are organized in a structured curriculum path rather than a flat list, enabling progressive difficulty scaffolding aligned with actual interview preparation timelines.","intents":["I want to practice only the types of problems Google, Meta, Amazon, Apple, and Netflix actually ask in interviews","I need a structured learning path that progresses from fundamentals to advanced interview-level problems","I want to avoid wasting time on obscure algorithm problems that won't appear in FAANG interviews"],"best_for":["Software engineers with 1-3 years of experience preparing for FAANG technical interviews","Career changers targeting top-tier companies who need curated, focused problem sets","Developers who have attempted LeetCode but felt overwhelmed by the breadth and lack of interview specificity"],"limitations":["Problem database size and update frequency not publicly disclosed — unclear if problems stay current with actual interview trends","Curation is static and company-specific; cannot dynamically adjust based on real-time interview question shifts","Limited to coding problems; does not cover system design, behavioral, or non-algorithmic interview components"],"requires":["Active subscription to Code Coach platform","Web browser with JavaScript support","Basic familiarity with at least one programming language"],"input_types":["problem selection (via UI navigation)","user difficulty preference"],"output_types":["structured problem statement (text + code templates)","problem metadata (company, difficulty, topic tags)","curriculum progression indicators"],"categories":["memory-knowledge","interview-preparation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_code-coach__cap_1","uri":"capability://code.generation.editing.real.time.ai.code.evaluation.with.interview.specific.feedback","name":"real-time ai code evaluation with interview-specific feedback","description":"Analyzes submitted code solutions using an LLM-based evaluation engine that provides instant feedback on correctness, time/space complexity, code quality, and interview readiness. The system likely uses AST parsing or semantic code analysis to detect algorithmic patterns, then generates natural language feedback highlighting specific improvements. Feedback is framed around interview expectations (e.g., 'Your solution is O(n²) but interviewers typically expect O(n log n) for this problem') rather than generic code quality metrics.","intents":["I want immediate feedback on my solution without waiting for human code review","I need to understand not just if my code is correct, but whether it meets FAANG interview standards","I want to know how my solution compares to optimal approaches and what optimizations I missed"],"best_for":["Engineers preparing for interviews who need rapid iteration cycles","Self-taught developers without access to experienced mentors for code review","Candidates practicing at odd hours when human reviewers are unavailable"],"limitations":["AI feedback may miss nuanced edge cases or context-specific optimizations that human interviewers would catch","Feedback quality depends on LLM accuracy; complex algorithmic problems may receive generic or partially incorrect suggestions","No personalized learning trajectory — feedback is problem-specific, not adaptive to individual weak areas across multiple attempts"],"requires":["Active Code Coach subscription","Code submission in supported language (likely Python, Java, C++, JavaScript)","LLM API access (internal to Code Coach infrastructure)"],"input_types":["code solution (text)","problem context (implicit from problem ID)"],"output_types":["correctness verdict (pass/fail)","complexity analysis (time/space Big-O)","natural language feedback (text)","improvement suggestions (text + code snippets)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_code-coach__cap_2","uri":"capability://automation.workflow.interactive.interview.simulation.environment.with.time.constraints","name":"interactive interview simulation environment with time constraints","description":"Provides a sandboxed coding environment that mimics real FAANG interview conditions, including enforced time limits, read-only problem statements, and a code editor with syntax highlighting and basic IDE features. The environment likely tracks submission history, execution time, and test case results. Time constraints are configurable but default to realistic interview durations (45-60 minutes for coding rounds), creating psychological pressure similar to actual interviews and enabling candidates to practice time management and stress resilience.","intents":["I want to practice solving problems under time pressure to reduce interview anxiety","I need to simulate the exact environment and constraints I'll face in a real FAANG interview","I want to track my performance over time and see if I'm improving in speed and accuracy"],"best_for":["Candidates with interview anxiety who benefit from repeated exposure to realistic conditions","Engineers who solve problems correctly but struggle with time management during interviews","Developers preparing for their first FAANG interview who lack prior interview experience"],"limitations":["Simulation cannot fully replicate the cognitive load of a live interview with a human evaluator asking clarifying questions","No video/audio component — missing the communication and explanation aspects that are critical in real interviews","Time constraints are fixed and cannot adapt to problem difficulty or candidate skill level"],"requires":["Active Code Coach subscription","Web browser with JavaScript and WebSocket support","Stable internet connection (for real-time code execution)"],"input_types":["problem selection","time limit preference (optional)","code submission (text)"],"output_types":["test case results (pass/fail per test)","execution metrics (runtime, memory usage)","submission history (timestamps, code versions)","performance summary (problems solved, time taken, success rate)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_code-coach__cap_3","uri":"capability://planning.reasoning.structured.curriculum.progression.with.adaptive.difficulty.sequencing","name":"structured curriculum progression with adaptive difficulty sequencing","description":"Organizes problems into a multi-stage learning curriculum that progresses from foundational data structures and algorithms to advanced interview-level problems, with explicit prerequisites and topic dependencies. The system likely tracks user progress across problems and may recommend next steps based on completion history. Difficulty sequencing is designed to build confidence and competency incrementally, preventing the 'overwhelming breadth' problem that plagues general platforms. Curriculum may include topic-specific modules (e.g., 'Arrays and Strings', 'Trees and Graphs', 'Dynamic Programming') with curated problem subsets.","intents":["I want a clear roadmap for interview preparation instead of randomly jumping between problems","I need to know which topics to focus on first and in what order to build foundational skills","I want to see my progress through a structured curriculum and understand what I've mastered vs. what needs work"],"best_for":["Career changers and bootcamp graduates who lack a structured CS foundation","Engineers new to FAANG interview preparation who need guidance on what to study","Developers who benefit from explicit learning paths and milestone tracking"],"limitations":["Curriculum is static and one-size-fits-all; cannot adapt to individual learning pace or prior knowledge gaps","No assessment mechanism to skip ahead if a candidate already masters foundational topics","Curriculum may not reflect the latest shifts in FAANG interview question distributions"],"requires":["Active Code Coach subscription","Completion of initial onboarding or skill assessment (if available)"],"input_types":["user profile (experience level, target companies)","problem completion history"],"output_types":["curriculum roadmap (visual or text)","next recommended problem","progress indicators (% complete per topic)","mastery badges or milestone markers"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_code-coach__cap_4","uri":"capability://data.processing.analysis.performance.analytics.and.interview.readiness.scoring","name":"performance analytics and interview readiness scoring","description":"Tracks user performance metrics across solved problems (success rate, time taken, complexity of solutions) and aggregates them into interview readiness indicators or scores. The system likely calculates metrics such as problems solved per topic, average solution quality, time management efficiency, and consistency across multiple attempts. Analytics may be visualized as dashboards or progress reports, enabling candidates to identify weak areas and track improvement over time. Readiness scoring may incorporate company-specific benchmarks (e.g., 'You've solved 80% of Google's typical problem set').","intents":["I want to know if I'm ready for my upcoming FAANG interview","I need to identify which topics or problem types I'm weakest in so I can focus my study time","I want to track my improvement over weeks of preparation and see if my practice is paying off"],"best_for":["Candidates with limited time who need data-driven guidance on where to focus effort","Engineers preparing for multiple FAANG interviews who want to benchmark readiness across companies","Self-directed learners who benefit from quantitative progress metrics and goal tracking"],"limitations":["Readiness scores are heuristic-based and may not correlate strongly with actual interview performance","Analytics are retrospective (based on past performance) and cannot predict future interview outcomes","No integration with actual interview results — cannot validate whether high readiness scores translate to offers"],"requires":["Active Code Coach subscription","Completion of multiple problems to generate meaningful analytics"],"input_types":["problem completion history","solution code and execution metrics","user profile (target companies)"],"output_types":["performance dashboard (visual)","readiness score (numeric or categorical)","topic-specific performance breakdown","weak area recommendations","progress charts (over time)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_code-coach__cap_5","uri":"capability://code.generation.editing.multi.language.code.execution.and.testing.with.sandbox.isolation","name":"multi-language code execution and testing with sandbox isolation","description":"Executes user-submitted code in a sandboxed environment supporting multiple programming languages (likely Python, Java, C++, JavaScript, Go, etc.) and runs test cases against submitted solutions. The sandbox isolates code execution to prevent malicious or resource-intensive code from affecting platform stability. Test results are returned with detailed output (pass/fail per test case, execution time, memory usage, error messages). The system likely uses containerization (Docker) or language-specific runtimes to manage execution safely and efficiently.","intents":["I want to test my code against multiple test cases and see which ones pass or fail","I need to verify that my solution works correctly before submitting for AI feedback","I want to practice in my preferred programming language without worrying about environment setup"],"best_for":["Engineers practicing in multiple languages who need consistent testing across platforms","Candidates who want to verify correctness before receiving AI feedback","Developers without local development environment setup or those practicing on mobile/limited devices"],"limitations":["Sandbox execution may have latency overhead compared to local execution (100-500ms per submission)","Resource limits (memory, CPU, execution time) may prevent testing of very large inputs or computationally intensive algorithms","Limited debugging capabilities — no interactive debugger or step-through execution"],"requires":["Active Code Coach subscription","Code in a supported language (Python, Java, C++, JavaScript, Go, or similar)","Stable internet connection for code submission and result retrieval"],"input_types":["code solution (text, language-specific)","test case inputs (implicit from problem definition)"],"output_types":["test case results (pass/fail per test)","execution metrics (runtime in ms, memory in MB)","error messages or stack traces (if applicable)","output comparison (expected vs. actual)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_code-coach__cap_6","uri":"capability://memory.knowledge.company.specific.problem.filtering.and.interview.format.customization","name":"company-specific problem filtering and interview format customization","description":"Allows users to filter problems by target company (Google, Meta, Amazon, Apple, Netflix) and customize the interview simulation environment to match that company's specific format, constraints, and expectations. The system likely maintains company-specific metadata (typical problem difficulty distribution, time limits, interview round structure) and surfaces problems tagged with that company's interview history. Users can select a company and receive a curated problem set and simulation environment tailored to that company's interview style.","intents":["I'm interviewing at Google next month — I want to practice problems that Google specifically asks","I want to understand the differences in interview style and difficulty between FAANG companies","I need to prepare for multiple FAANG companies with different interview formats and problem distributions"],"best_for":["Candidates with specific FAANG target companies who want focused, company-specific preparation","Engineers interviewing at multiple FAANG companies simultaneously who need to adapt their preparation","Developers who benefit from understanding company-specific interview patterns and expectations"],"limitations":["Company-specific data is based on historical interview patterns and may not reflect current or future question distributions","Cannot account for individual interviewer variation or team-specific interview styles within a company","Limited to the five major FAANG companies; does not cover other top-tier tech companies (Microsoft, Apple, etc. beyond the FAANG acronym)"],"requires":["Active Code Coach subscription","Selection of target company (via UI)"],"input_types":["target company selection","user experience level"],"output_types":["company-specific problem set","company interview format description","typical difficulty distribution","company-specific performance benchmarks"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active subscription to Code Coach platform","Web browser with JavaScript support","Basic familiarity with at least one programming language","Active Code Coach subscription","Code submission in supported language (likely Python, Java, C++, JavaScript)","LLM API access (internal to Code Coach infrastructure)","Web browser with JavaScript and WebSocket support","Stable internet connection (for real-time code execution)","Completion of initial onboarding or skill assessment (if available)","Completion of multiple problems to generate meaningful analytics"],"failure_modes":["Problem database size and update frequency not publicly disclosed — unclear if problems stay current with actual interview trends","Curation is static and company-specific; cannot dynamically adjust based on real-time interview question shifts","Limited to coding problems; does not cover system design, behavioral, or non-algorithmic interview components","AI feedback may miss nuanced edge cases or context-specific optimizations that human interviewers would catch","Feedback quality depends on LLM accuracy; complex algorithmic problems may receive generic or partially incorrect suggestions","No personalized learning trajectory — feedback is problem-specific, not adaptive to individual weak areas across multiple attempts","Simulation cannot fully replicate the cognitive load of a live interview with a human evaluator asking clarifying questions","No video/audio component — missing the communication and explanation aspects that are critical in real interviews","Time constraints are fixed and cannot adapt to problem difficulty or candidate skill level","Curriculum is static and one-size-fits-all; cannot adapt to individual learning pace or prior knowledge gaps","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=code-coach","compare_url":"https://unfragile.ai/compare?artifact=code-coach"}},"signature":"WR3KO+efW+o26N794PuMexagZ7rvTyql9k5xxb25ZnNjtJw7/l3V8P7LgCpuKqf4778Nb2YgQW04MZqrr3EjDQ==","signedAt":"2026-06-21T18:00:19.596Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/code-coach","artifact":"https://unfragile.ai/code-coach","verify":"https://unfragile.ai/api/v1/verify?slug=code-coach","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"}}