{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_pgrammer","slug":"pgrammer","name":"Pgrammer","type":"product","url":"https://www.pgrammer.com","page_url":"https://unfragile.ai/pgrammer","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_pgrammer__cap_0","uri":"capability://planning.reasoning.adaptive.difficulty.problem.generation","name":"adaptive-difficulty-problem-generation","description":"Generates coding interview problems that dynamically adjust difficulty based on user performance history, skill assessment, and identified weak areas. The system likely uses a multi-dimensional skill model tracking proficiency across data structures, algorithms, and problem-solving patterns, then selects problems from a curated pool that target gaps while maintaining engagement through graduated challenge progression.","intents":["I want coding problems that match my current skill level, not generic LeetCode collections","I need the platform to identify my weak areas in algorithms and data structures automatically","I want difficulty to increase gradually as I improve, not jump randomly between easy and hard"],"best_for":["Early-career developers (0-2 years experience) preparing for junior-to-mid-level interviews","Bootcamp graduates seeking personalized guidance beyond one-size-fits-all problem sets","Career-switchers who need targeted practice on specific algorithmic weak points"],"limitations":["Adaptive model requires sufficient historical performance data (likely 10+ problems solved) before meaningful personalization kicks in","May not effectively calibrate for niche domains (systems design, distributed systems) where problem diversity is limited","Unknown how the system handles skill regression or long breaks between sessions"],"requires":["Active user account with problem-solving history","Web browser with JavaScript support","Internet connectivity for real-time difficulty adjustment"],"input_types":["user performance metrics (pass/fail, time taken, code quality scores)","problem completion history","skill assessment responses"],"output_types":["coding problem statement with constraints","difficulty rating (numeric or categorical)","problem category tags (array, string, graph, etc.)"],"categories":["planning-reasoning","personalization-engine"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_1","uri":"capability://code.generation.editing.real.time.code.quality.analysis","name":"real-time-code-quality-analysis","description":"Analyzes submitted code immediately upon execution or submission, providing instant feedback on code quality metrics including time complexity, space complexity, algorithmic correctness, and code style. The system likely parses the abstract syntax tree (AST), performs static analysis for complexity estimation, and compares against reference solutions or known optimal approaches to generate actionable feedback within seconds.","intents":["I want immediate feedback on whether my solution is correct and efficient","I need to understand the time and space complexity of my code without manual calculation","I want to know specific code quality issues (naming, structure, edge cases) right after submission"],"best_for":["Developers seeking tight feedback loops during interview prep (vs. waiting for human code review)","Self-taught programmers who lack mentorship to identify complexity and style issues","Teams building internal interview prep tools who need automated code evaluation"],"limitations":["Complexity analysis is heuristic-based and may misclassify edge cases or amortized complexity scenarios","Cannot evaluate communication clarity, problem-solving approach explanation, or soft skills that human interviewers assess","Feedback quality depends on reference solution coverage; novel or unconventional approaches may receive incomplete analysis","No context awareness for language-specific idioms or performance characteristics (e.g., Python list vs. deque trade-offs)"],"requires":["Code submission in supported language (likely Python, JavaScript, Java, C++)","Problem context and constraints available in the system","Reference solutions or complexity benchmarks for comparison"],"input_types":["source code (text)","problem ID or context","execution test results (pass/fail, runtime)"],"output_types":["complexity analysis (time, space as Big-O notation)","correctness verdict (pass/fail with test case details)","code quality feedback (structured list of issues)","comparison to reference solutions (text summary)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_2","uri":"capability://data.processing.analysis.personalized.weak.area.identification","name":"personalized-weak-area-identification","description":"Analyzes patterns across a user's problem-solving history to identify systematic weak points in specific algorithmic domains, data structure knowledge, or problem-solving approaches. The system tracks metrics like failure rate by category, time-to-solution variance, and common mistake patterns, then surfaces these insights to guide future practice and problem selection.","intents":["I want to know which algorithmic topics I consistently struggle with","I need data-driven insights into my learning gaps rather than guessing what to practice","I want the platform to recommend focused practice areas based on my actual performance"],"best_for":["Self-directed learners who need objective data on skill gaps","Developers with limited mentorship access who rely on data-driven guidance","Interview prep coaches building personalized study plans for clients"],"limitations":["Requires minimum historical data (likely 15-20+ problems) before patterns become statistically significant","May conflate difficulty with weak areas (e.g., a user might fail hard problems not due to weak fundamentals but due to problem complexity)","Cannot distinguish between knowledge gaps and execution errors (e.g., off-by-one bugs vs. algorithmic misunderstanding)","No longitudinal tracking of improvement over weeks/months to validate whether focused practice actually closes gaps"],"requires":["User account with sufficient problem-solving history","Categorized problem taxonomy (arrays, trees, graphs, dynamic programming, etc.)","Performance metrics tracked per problem (correctness, time, complexity)"],"input_types":["user performance history (array of problem attempts with outcomes)","problem metadata (category, difficulty, topic tags)","time-series data (when problems were solved)"],"output_types":["weak area summary (structured list with category, confidence score, recommendation)","performance heatmap (category vs. success rate)","recommended practice topics (prioritized list)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_3","uri":"capability://text.generation.language.ai.driven.hint.generation","name":"ai-driven-hint-generation","description":"Generates contextual hints and guidance when users are stuck on a problem, providing progressive levels of assistance from high-level strategy hints to specific code patterns. The system likely analyzes the user's submitted code, identifies the nature of the failure (wrong approach, implementation bug, edge case), and generates hints tailored to that specific gap without revealing the solution.","intents":["I'm stuck on a problem and need a nudge in the right direction without seeing the full solution","I want hints that guide my thinking rather than just telling me the answer","I need different levels of help depending on how stuck I am"],"best_for":["Self-learners who want to maintain problem-solving autonomy while getting unstuck","Developers preparing for interviews who need to practice thinking through problems independently","Bootcamp graduates who lack access to instructor office hours"],"limitations":["Hint quality depends on the system's ability to diagnose the root cause of failure, which is non-trivial for complex problems","May provide generic hints for novel problem variations where the system lacks reference solutions","Cannot assess whether a user is stuck due to knowledge gaps vs. careless mistakes, potentially providing misaligned guidance","Risk of hint-gaming: users may repeatedly request hints without attempting independent problem-solving"],"requires":["User code submission with execution results","Problem context and reference solutions","Hint database or generation model trained on problem-solution pairs"],"input_types":["user code (text)","test case results (pass/fail details)","problem statement and constraints"],"output_types":["hint text (natural language, progressive levels)","code pattern suggestions (optional, language-specific)","strategy guidance (high-level approach description)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_4","uri":"capability://planning.reasoning.interview.simulation.problem.sequencing","name":"interview-simulation-problem-sequencing","description":"Sequences problems to simulate realistic technical interview conditions, presenting a series of problems with time constraints, difficulty progression, and mixed topic coverage that mirrors actual interview formats. The system likely uses a scheduling algorithm that balances topic diversity, difficulty curve, and time limits to create coherent practice sessions.","intents":["I want to practice solving multiple problems in sequence under time pressure like a real interview","I need to experience mixed-topic problem sets rather than focused drills on single topics","I want to simulate the pacing and fatigue of a real technical interview"],"best_for":["Developers in final interview prep stages who need realistic simulation","Career-switchers practicing interview endurance and context-switching","Teams building interview prep curricula that need structured practice sessions"],"limitations":["Simulates technical problem-solving but cannot replicate communication, whiteboarding, or interviewer interaction dynamics","Time limits are arbitrary and may not match specific company interview formats","No feedback on problem-solving explanation or communication clarity, only code correctness","Session fatigue and stress response cannot be fully captured in a solo practice environment"],"requires":["User account with sufficient problem history for difficulty calibration","Problem pool with diverse topics and difficulty levels","Session configuration (duration, number of problems, topic mix)"],"input_types":["user skill level and weak areas","session parameters (duration, difficulty target)","problem pool metadata"],"output_types":["ordered problem sequence (array of problem IDs)","session summary (total time, problems solved, performance metrics)","performance comparison (vs. historical average or benchmarks)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_5","uri":"capability://data.processing.analysis.performance.benchmarking.against.peers","name":"performance-benchmarking-against-peers","description":"Compares user performance metrics (solve time, code quality, success rate) against anonymized peer cohorts or population benchmarks, providing context for skill assessment. The system likely aggregates performance data across users at similar skill levels and interview target companies, then surfaces percentile rankings and comparative insights.","intents":["I want to know how my interview prep progress compares to others at my level","I need objective benchmarks to assess whether I'm ready for interviews","I want to see how my performance compares to people targeting the same companies"],"best_for":["Self-directed learners who lack external validation of readiness","Developers with imposter syndrome who need data-driven confidence assessment","Career-switchers evaluating whether their prep timeline is realistic"],"limitations":["Benchmarks are only meaningful with sufficient user population; small communities may have noisy or unrepresentative data","Peer comparison can be demoralizing if not contextualized with growth trajectory and practice duration","Unknown whether benchmarks account for language choice, problem difficulty distribution, or other confounding variables","Privacy concerns: even anonymized data may reveal sensitive information about company hiring patterns or interview difficulty"],"requires":["Sufficient user population for meaningful peer cohorts (likely 100+ users per skill level)","Anonymized performance data aggregation","User consent for benchmarking participation"],"input_types":["user performance metrics (solve time, success rate, code quality scores)","user metadata (skill level, target company, experience level)"],"output_types":["percentile ranking (numeric, e.g., 75th percentile)","peer comparison summary (text with context)","benchmark trends (how performance changes over time for cohort)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_6","uri":"capability://code.generation.editing.multi.language.code.execution.and.testing","name":"multi-language-code-execution-and-testing","description":"Executes user-submitted code in multiple programming languages (likely Python, JavaScript, Java, C++, Go, etc.) against a test case suite, capturing output, runtime, and memory usage. The system likely uses containerized execution environments or sandboxed interpreters to safely run untrusted code, with timeout and resource limits to prevent abuse.","intents":["I want to test my code against multiple test cases immediately after submission","I need to see actual runtime and memory usage, not just correctness","I want to practice in my preferred language without switching platforms"],"best_for":["Developers preparing for interviews in their primary language","Teams building language-agnostic interview prep tools","Polyglots who want to practice in multiple languages"],"limitations":["Execution environment may not perfectly replicate production conditions (e.g., different Python versions, JVM heap settings)","Memory and runtime measurements are approximate and may vary based on system load","No support for languages outside the curated set (e.g., Rust, Kotlin, Scala)","Timeout limits may be too strict for legitimate slow algorithms or too lenient for inefficient code"],"requires":["Supported programming language (Python 3.x, JavaScript/Node.js, Java 8+, C++11+, etc.)","Code submission with valid syntax for the target language","Test case suite for the problem"],"input_types":["source code (text, language-specific)","test case inputs (structured data or raw input)","execution parameters (timeout, memory limit)"],"output_types":["execution results (pass/fail per test case)","runtime metrics (execution time in ms, memory in MB)","error output (stderr, stack traces for failures)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_7","uri":"capability://data.processing.analysis.progress.tracking.and.learning.analytics","name":"progress-tracking-and-learning-analytics","description":"Tracks user progress across multiple dimensions (problems solved, success rate, time-to-solution trends, topic mastery) and visualizes learning trajectories over time. The system likely stores historical performance data, computes rolling averages and trend lines, and generates dashboards showing improvement in specific areas.","intents":["I want to see my progress over weeks and months to stay motivated","I need to identify which topics I've improved in and which still need work","I want to track my time-to-solution improvements to measure readiness"],"best_for":["Self-directed learners who need motivation and progress visibility","Developers with long interview prep timelines (3+ months) who need milestone tracking","Mentors and coaches building personalized study plans for mentees"],"limitations":["Progress metrics are lagging indicators; short-term fluctuations may not reflect true learning","No causal analysis: system cannot determine whether progress is due to practice, improved understanding, or problem difficulty variation","Requires consistent engagement; gaps in practice history make trend analysis unreliable","May create false confidence if metrics improve due to problem difficulty decrease rather than skill increase"],"requires":["User account with historical problem-solving data (minimum 10-20 problems)","Consistent tracking of performance metrics per problem","Time-series data storage for trend analysis"],"input_types":["user performance history (problem ID, outcome, time, date)","problem metadata (category, difficulty)"],"output_types":["progress dashboard (visualizations: line charts, heatmaps)","learning summary (text: topics mastered, areas needing work)","trend analysis (improvement rate, estimated readiness date)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_8","uri":"capability://text.generation.language.problem.explanation.and.solution.walkthrough","name":"problem-explanation-and-solution-walkthrough","description":"Provides detailed explanations of problem solutions including algorithmic approach, implementation details, complexity analysis, and common pitfalls. The system likely stores curated explanations written by experts or generated from reference solutions, then surfaces them on-demand after users submit their own attempts.","intents":["I want to understand the optimal solution approach after I've attempted the problem","I need to learn why my approach was suboptimal or incorrect","I want to see multiple solution approaches and their trade-offs"],"best_for":["Learners who benefit from post-attempt explanation (vs. pre-attempt tutorials)","Developers seeking to understand algorithmic patterns and trade-offs","Self-taught programmers building mental models of problem-solving approaches"],"limitations":["Explanations are static and may not address user's specific solution or misconceptions","Quality depends on explanation curation; generic or poorly-written explanations reduce learning value","No interactive walkthrough or step-by-step debugging of user's specific code","May enable passive learning if users read explanations without attempting problems first"],"requires":["Problem ID and context","Curated solution explanations (text, code, diagrams)","User submission history (to gate access until after attempt)"],"input_types":["problem ID","user submission status (attempted or not)"],"output_types":["explanation text (markdown or HTML)","solution code (language-specific, with comments)","complexity analysis (Big-O notation with justification)","alternative approaches (list with trade-offs)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pgrammer__cap_9","uri":"capability://tool.use.integration.freemium.access.with.premium.features","name":"freemium-access-with-premium-features","description":"Offers a freemium model where basic problem-solving and feedback are available to all users, with premium tiers unlocking advanced features like unlimited hint generation, detailed analytics, interview simulations, and priority support. The system likely uses feature flags or subscription checks to gate premium functionality.","intents":["I want to try the platform risk-free before committing to paid prep resources","I need basic interview prep without paying, but would upgrade for advanced features","I want to evaluate whether the platform's AI feedback is worth the cost"],"best_for":["Students and early-career developers with limited budgets","Career-switchers evaluating multiple interview prep platforms","Individuals exploring whether AI-driven prep is effective before investing"],"limitations":["Free tier may have limited problem access or delayed feedback, reducing effectiveness","Freemium model may create friction if key features (like adaptive difficulty) require premium","Unknown conversion rate or retention metrics; platform viability depends on sufficient premium adoption","Free users may not generate enough data for effective personalization, creating poor experience that discourages upgrade"],"requires":["User account creation (email or social login)","Payment method for premium tier (credit card, subscription service)"],"input_types":["user subscription status","feature access requests"],"output_types":["feature availability (boolean per feature)","upgrade prompts (UI messaging, paywall)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Active user account with problem-solving history","Web browser with JavaScript support","Internet connectivity for real-time difficulty adjustment","Code submission in supported language (likely Python, JavaScript, Java, C++)","Problem context and constraints available in the system","Reference solutions or complexity benchmarks for comparison","User account with sufficient problem-solving history","Categorized problem taxonomy (arrays, trees, graphs, dynamic programming, etc.)","Performance metrics tracked per problem (correctness, time, complexity)","User code submission with execution results"],"failure_modes":["Adaptive model requires sufficient historical performance data (likely 10+ problems solved) before meaningful personalization kicks in","May not effectively calibrate for niche domains (systems design, distributed systems) where problem diversity is limited","Unknown how the system handles skill regression or long breaks between sessions","Complexity analysis is heuristic-based and may misclassify edge cases or amortized complexity scenarios","Cannot evaluate communication clarity, problem-solving approach explanation, or soft skills that human interviewers assess","Feedback quality depends on reference solution coverage; novel or unconventional approaches may receive incomplete analysis","No context awareness for language-specific idioms or performance characteristics (e.g., Python list vs. deque trade-offs)","Requires minimum historical data (likely 15-20+ problems) before patterns become statistically significant","May conflate difficulty with weak areas (e.g., a user might fail hard problems not due to weak fundamentals but due to problem complexity)","Cannot distinguish between knowledge gaps and execution errors (e.g., off-by-one bugs vs. algorithmic misunderstanding)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.2,"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":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=pgrammer","compare_url":"https://unfragile.ai/compare?artifact=pgrammer"}},"signature":"aS7jKq/alMVt9cnmkW8d44RuvZysvFSChcou+PXro7+f4HQj6PL1LI60xrnmH+6ZA//cnuonYZavxaFFo+AvCA==","signedAt":"2026-06-21T15:52:26.207Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pgrammer","artifact":"https://unfragile.ai/pgrammer","verify":"https://unfragile.ai/api/v1/verify?slug=pgrammer","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"}}