{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47350416","slug":"rudel-claude-code-session-analytics","name":"Rudel – Claude Code Session Analytics","type":"repo","url":"https://github.com/obsessiondb/rudel","page_url":"https://unfragile.ai/rudel-claude-code-session-analytics","categories":["observability"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47350416__cap_0","uri":"capability://memory.knowledge.claude.api.session.conversation.capture.and.persistence","name":"claude api session conversation capture and persistence","description":"Captures and stores the complete conversation history from Claude API interactions during code sessions by intercepting API requests/responses and persisting them to a local database or file store. Uses a middleware or wrapper pattern around the Anthropic SDK to log all messages, tokens, and metadata without modifying application code, enabling full session reconstruction and replay.","intents":["I want to analyze what prompts I gave Claude and how it responded across multiple code sessions","I need to audit the exact conversation flow and token usage for billing and performance analysis","I want to replay past Claude interactions to understand how a solution was derived"],"best_for":["developers using Claude API for code generation and debugging workflows","teams auditing AI-assisted development practices and token spend","researchers studying prompt engineering patterns in code generation"],"limitations":["Requires integration point in application code or SDK wrapper — cannot passively capture existing Claude integrations without code changes","Storage overhead grows linearly with conversation length and token count — no built-in compression or archival strategy","No real-time streaming analysis — captures complete messages only after API round-trip completes"],"requires":["Python 3.8+ or Node.js 16+ (depending on SDK language)","Anthropic API key with active Claude model access","Local filesystem or database write permissions for session storage"],"input_types":["Claude API request/response payloads","conversation metadata (timestamps, model, tokens)"],"output_types":["structured session logs (JSON or database records)","conversation transcripts"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47350416__cap_1","uri":"capability://data.processing.analysis.code.session.analytics.and.metrics.extraction","name":"code session analytics and metrics extraction","description":"Analyzes captured Claude code sessions to extract quantitative metrics including token efficiency, prompt-response patterns, code quality indicators, and iteration counts. Parses conversation transcripts to identify code blocks, refactoring cycles, and problem-solving approaches using regex or AST-based pattern matching to categorize interactions by type (generation, debugging, optimization).","intents":["I want to measure how many iterations Claude needed to solve a coding problem","I need to understand token efficiency — how many tokens did I spend per line of working code generated","I want to identify patterns in my prompting style that lead to better code outcomes"],"best_for":["individual developers optimizing their Claude usage patterns","engineering teams benchmarking AI-assisted development productivity","product managers tracking AI tool adoption and effectiveness metrics"],"limitations":["Metrics accuracy depends on consistent conversation structure — unstructured or multi-turn debugging sessions may produce noisy data","Code quality metrics are heuristic-based (line count, complexity) rather than semantic — cannot measure actual correctness without external test execution","No cross-session aggregation or trend analysis — each session analyzed in isolation without longitudinal context"],"requires":["Captured session logs from Claude API interactions","Python 3.8+ for analytics processing","Optional: AST parser library (ast, tree-sitter) for code structure analysis"],"input_types":["structured session logs with conversation history","code blocks extracted from Claude responses"],"output_types":["JSON metrics objects","CSV reports","visualization-ready structured data"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47350416__cap_2","uri":"capability://data.processing.analysis.session.visualization.and.interactive.exploration","name":"session visualization and interactive exploration","description":"Generates interactive dashboards and visual representations of Claude code sessions, displaying conversation flow, token usage over time, code block evolution, and iteration patterns. Likely uses a web framework (React, Vue) or visualization library (D3, Plotly) to render session timelines, token burn-down charts, and conversation graphs that allow filtering and drilling into specific interactions.","intents":["I want to visually see how my conversation with Claude evolved and where I spent the most tokens","I need to present session analytics to my team in an understandable visual format","I want to compare multiple code sessions side-by-side to identify patterns"],"best_for":["developers who learn better from visual representations of data","team leads presenting AI productivity metrics to stakeholders","researchers analyzing prompt engineering effectiveness across multiple sessions"],"limitations":["Visualization performance degrades with very long sessions (1000+ turns) — rendering may become sluggish without pagination or aggregation","Limited to pre-built chart types — custom analysis requires extending visualization templates","No real-time dashboard updates — requires manual refresh or polling to see new session data"],"requires":["Web browser with modern JavaScript support (ES6+)","Session analytics data in structured format (JSON)","Optional: local web server or cloud deployment for hosting dashboard"],"input_types":["structured analytics data (metrics, timelines, token counts)","session metadata"],"output_types":["interactive HTML dashboards","SVG/PNG static visualizations","exportable reports"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47350416__cap_3","uri":"capability://planning.reasoning.prompt.pattern.recognition.and.recommendation","name":"prompt pattern recognition and recommendation","description":"Analyzes historical Claude code sessions to identify effective prompt patterns and anti-patterns, using NLP or rule-based matching to categorize prompts by structure, specificity, and outcome quality. Generates recommendations for improving future prompts based on correlation between prompt characteristics (length, clarity, examples provided) and code quality or token efficiency metrics extracted from past sessions.","intents":["I want to understand which types of prompts lead Claude to generate better code","I need suggestions for how to rephrase my prompts to get faster, more accurate responses","I want to learn from my successful Claude interactions to improve my prompting technique"],"best_for":["individual developers improving their prompt engineering skills","teams establishing best practices for Claude-assisted development","prompt engineers studying effectiveness of different instruction styles"],"limitations":["Recommendations are correlative, not causal — cannot definitively prove prompt structure caused better outcomes without controlled experiments","Requires sufficient historical data (20+ sessions) for meaningful pattern detection — sparse session history produces unreliable recommendations","No semantic understanding of prompt intent — pattern matching is syntactic and may miss nuanced differences in effective prompting approaches"],"requires":["Multiple captured Claude code sessions (minimum 10-20 for meaningful patterns)","Session analytics with code quality and token efficiency metrics","Optional: NLP library (spaCy, NLTK) for advanced prompt analysis"],"input_types":["historical prompts from Claude sessions","associated outcome metrics (code quality, token count, iteration count)"],"output_types":["prompt pattern classifications","recommendation reports","best-practice guidelines"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47350416__cap_4","uri":"capability://data.processing.analysis.multi.session.comparison.and.trend.analysis","name":"multi-session comparison and trend analysis","description":"Aggregates metrics and patterns across multiple Claude code sessions to identify trends, regressions, and improvements in productivity over time. Implements time-series analysis to track token efficiency, code quality, and iteration counts across sessions, enabling detection of performance degradation or improvement patterns and correlation with external factors (time of day, session duration, problem complexity).","intents":["I want to see if my Claude usage is becoming more efficient over time as I learn better prompting","I need to identify if certain types of coding problems consistently require more Claude iterations","I want to correlate session metrics with external factors like time of day or problem domain"],"best_for":["developers tracking their productivity improvements with Claude over weeks/months","teams measuring the impact of prompt engineering training on AI-assisted development","researchers studying how developer skill with Claude evolves over time"],"limitations":["Trend detection requires minimum 20-30 sessions for statistical significance — insufficient data produces spurious correlations","Cannot control for confounding variables (problem difficulty, code domain, developer fatigue) without manual annotation","Time-series analysis assumes consistent session structure — highly variable session types (quick fixes vs. large features) may obscure trends"],"requires":["Minimum 20+ captured Claude code sessions with consistent metadata","Session analytics data with temporal information (timestamps, duration)","Optional: statistical analysis library (scipy, pandas) for trend detection"],"input_types":["multiple session analytics records","temporal metadata (dates, times)","session characteristics (duration, problem type, code domain)"],"output_types":["trend reports with statistical summaries","time-series visualizations","correlation analysis results"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47350416__cap_5","uri":"capability://data.processing.analysis.session.export.and.reporting","name":"session export and reporting","description":"Exports captured Claude code sessions and analytics in multiple formats (JSON, CSV, PDF, Markdown) for sharing, archival, and integration with external tools. Implements templated report generation that combines conversation transcripts, metrics summaries, and visualizations into human-readable documents suitable for documentation, team sharing, or compliance auditing.","intents":["I want to export a session transcript to share with my team for code review","I need to generate a report showing token usage and productivity metrics for billing or budget tracking","I want to archive sessions in a portable format for long-term reference"],"best_for":["developers sharing Claude-assisted code solutions with teammates","engineering managers tracking AI tool usage and costs","compliance teams auditing AI-assisted development for governance"],"limitations":["Export performance degrades with very large sessions (10,000+ tokens) — may require streaming or chunked export","PDF generation adds dependency on external library (wkhtmltopdf, Puppeteer) with platform-specific installation requirements","No built-in anonymization or redaction — sensitive code or API keys in conversations must be manually removed before sharing"],"requires":["Captured session data in structured format","Python 3.8+ or Node.js 16+ for export processing","Optional: PDF generation library (reportlab, Puppeteer) for report generation"],"input_types":["structured session records","analytics metrics","conversation transcripts"],"output_types":["JSON exports","CSV data files","PDF reports","Markdown transcripts","HTML documents"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 16+ (depending on SDK language)","Anthropic API key with active Claude model access","Local filesystem or database write permissions for session storage","Captured session logs from Claude API interactions","Python 3.8+ for analytics processing","Optional: AST parser library (ast, tree-sitter) for code structure analysis","Web browser with modern JavaScript support (ES6+)","Session analytics data in structured format (JSON)","Optional: local web server or cloud deployment for hosting dashboard","Multiple captured Claude code sessions (minimum 10-20 for meaningful patterns)"],"failure_modes":["Requires integration point in application code or SDK wrapper — cannot passively capture existing Claude integrations without code changes","Storage overhead grows linearly with conversation length and token count — no built-in compression or archival strategy","No real-time streaming analysis — captures complete messages only after API round-trip completes","Metrics accuracy depends on consistent conversation structure — unstructured or multi-turn debugging sessions may produce noisy data","Code quality metrics are heuristic-based (line count, complexity) rather than semantic — cannot measure actual correctness without external test execution","No cross-session aggregation or trend analysis — each session analyzed in isolation without longitudinal context","Visualization performance degrades with very long sessions (1000+ turns) — rendering may become sluggish without pagination or aggregation","Limited to pre-built chart types — custom analysis requires extending visualization templates","No real-time dashboard updates — requires manual refresh or polling to see new session data","Recommendations are correlative, not causal — cannot definitively prove prompt structure caused better outcomes without controlled experiments","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.22,"ecosystem":0.46,"match_graph":0.25,"freshness":0.6,"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":"2026-06-17T09:51:04.692Z","last_scraped_at":"2026-05-04T08:10:06.238Z","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=rudel-claude-code-session-analytics","compare_url":"https://unfragile.ai/compare?artifact=rudel-claude-code-session-analytics"}},"signature":"Zkrt18dWYJgxW0408mmwtKYHvc1Vv7bF9EepF7JuUmjmD/hmKHmddohI/w0AcBKK1o3uq0ccpeOezbUCjx5vAw==","signedAt":"2026-06-20T03:03:23.988Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rudel-claude-code-session-analytics","artifact":"https://unfragile.ai/rudel-claude-code-session-analytics","verify":"https://unfragile.ai/api/v1/verify?slug=rudel-claude-code-session-analytics","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"}}