{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-swyx","slug":"swyx","name":"Swyx","type":"product","url":"https://twitter.com/swyx","page_url":"https://unfragile.ai/swyx","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-swyx__cap_0","uri":"capability://text.generation.language.real.time.collaborative.prompt.engineering.with.live.execution.feedback","name":"real-time collaborative prompt engineering with live execution feedback","description":"Enables multiple users to simultaneously edit and test AI prompts with instant execution results displayed in a shared workspace. Uses WebSocket-based real-time synchronization to propagate prompt changes across connected clients, with a backend execution engine that routes prompts to multiple LLM providers (OpenAI, Anthropic, etc.) and streams results back to all collaborators. Implements operational transformation or CRDT-style conflict resolution to handle concurrent edits without blocking.","intents":["I want to collaborate with my team on prompt optimization without version control friction","I need to A/B test different prompt variations against the same LLM in real-time","I want to see execution results instantly as I modify a prompt, without manual submission"],"best_for":["AI product teams iterating on prompt quality","prompt engineers working in distributed teams","researchers comparing LLM behavior across prompt variations"],"limitations":["Real-time sync adds latency overhead — likely 100-500ms per edit propagation depending on network","Concurrent edits to the same prompt section may require conflict resolution UI","Execution costs scale with number of simultaneous test runs across collaborators"],"requires":["Modern browser with WebSocket support","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Active internet connection for real-time sync and LLM calls"],"input_types":["text (prompt templates)","structured parameters (temperature, max_tokens, model selection)"],"output_types":["text (LLM completions)","structured metadata (latency, token count, cost estimates)"],"categories":["text-generation-language","automation-workflow","collaboration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.routing.with.cost.and.latency.optimization","name":"multi-provider llm routing with cost and latency optimization","description":"Abstracts prompt execution across multiple LLM providers (OpenAI, Anthropic, Cohere, local models) with intelligent routing based on cost, latency, and model capability constraints. Routes requests through a provider abstraction layer that normalizes API differences, handles rate limiting, and selects the optimal provider based on user-defined policies (e.g., 'use GPT-4 for complex reasoning, Claude for long context'). Likely implements a provider registry pattern with pluggable adapters for each LLM API.","intents":["I want to test the same prompt against multiple LLM providers without rewriting API calls","I need to automatically route requests to the cheapest provider that meets quality thresholds","I want to fall back to a secondary provider if the primary one is rate-limited or unavailable"],"best_for":["teams evaluating multiple LLM providers for production use","cost-conscious builders optimizing inference spend","researchers comparing model outputs across providers"],"limitations":["Provider abstraction adds ~50-200ms overhead per request due to normalization and routing logic","API key management complexity increases with each additional provider","Some provider-specific features (e.g., vision capabilities, function calling) may not be fully abstracted"],"requires":["API keys for one or more LLM providers","Network connectivity to provider endpoints","Configuration of routing policies (optional, defaults to round-robin)"],"input_types":["text (prompts)","structured routing policies (JSON or UI-defined rules)"],"output_types":["text (completions)","metadata (provider used, latency, cost, token counts)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_2","uri":"capability://planning.reasoning.prompt.versioning.and.a.b.testing.with.statistical.significance.tracking","name":"prompt versioning and a/b testing with statistical significance tracking","description":"Maintains a version history of prompts with the ability to run A/B tests comparing different versions against the same inputs. Tracks execution metrics (latency, cost, token usage) and output quality metrics (user ratings, automated evaluations) for each variant, then computes statistical significance to determine which prompt version performs better. Likely uses a database to store prompt versions, execution logs, and evaluation results, with a statistical analysis engine to compute p-values or confidence intervals.","intents":["I want to compare two prompt versions objectively using statistical tests, not just gut feeling","I need to track how prompt changes affect cost and latency across a large number of executions","I want to automatically detect when a new prompt version is a regression and roll back"],"best_for":["teams running production LLM applications with strict quality requirements","prompt engineers optimizing for specific metrics (cost, latency, accuracy)","data-driven organizations that want evidence before deploying prompt changes"],"limitations":["Statistical significance requires sufficient sample size — small test runs may not reach significance","Automated quality evaluation (if used) depends on proxy metrics that may not capture true quality","A/B test results are specific to the input distribution used — may not generalize"],"requires":["Persistent storage for prompt versions and execution logs","Mechanism to collect quality metrics (user feedback, automated evaluations, or both)","Sufficient traffic/test runs to achieve statistical power"],"input_types":["text (prompt versions)","structured test inputs (datasets or input distributions)","quality metrics (ratings, evaluation scores)"],"output_types":["structured test results (p-values, confidence intervals, winner determination)","visualizations (performance charts, metric comparisons)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_3","uri":"capability://text.generation.language.prompt.template.parameterization.with.variable.injection.and.validation","name":"prompt template parameterization with variable injection and validation","description":"Allows users to define prompt templates with placeholders for dynamic variables (e.g., {{user_input}}, {{context}}, {{model_name}}) that are injected at execution time. Supports variable validation rules (e.g., 'context must be < 2000 tokens', 'user_input must not be empty') and type coercion (e.g., converting numbers to text). Likely uses a templating engine (Handlebars, Jinja2-style) with a validation schema layer to ensure injected variables meet constraints before execution.","intents":["I want to reuse the same prompt template across different inputs without manual editing","I need to validate that injected variables meet constraints before sending to the LLM","I want to parameterize system prompts, examples, and user inputs separately for flexibility"],"best_for":["teams building production LLM applications with reusable prompts","developers creating prompt libraries for internal use","non-technical users who want to customize prompts without code"],"limitations":["Complex template logic (conditionals, loops) may be limited compared to full programming languages","Validation rules are static — cannot adapt based on runtime context","Template syntax learning curve for non-technical users"],"requires":["Templating engine support (built-in or via library)","Schema definition for variables (JSON Schema or similar)","Execution context with variable values at runtime"],"input_types":["text (prompt templates with placeholders)","structured variable definitions (schema)","runtime variable values (any type)"],"output_types":["text (rendered prompts with variables injected)","validation errors (if variables fail constraints)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_4","uri":"capability://data.processing.analysis.execution.history.and.audit.logging.with.cost.tracking","name":"execution history and audit logging with cost tracking","description":"Records every prompt execution with full context (input, output, model used, provider, latency, token counts, cost) in an immutable audit log. Provides search and filtering across execution history (by date, model, cost range, output quality) and generates cost reports aggregated by time period, model, or prompt. Likely stores logs in a database with indexing for fast retrieval and includes a UI for browsing and exporting logs.","intents":["I want to understand how much each prompt execution costs and identify cost outliers","I need to audit which models were used for which requests for compliance or debugging","I want to export execution logs for analysis in external tools (BI, spreadsheets)"],"best_for":["teams managing LLM costs in production","organizations with compliance or audit requirements","builders analyzing LLM usage patterns for optimization"],"limitations":["Storage costs scale with execution volume — high-traffic applications may need log retention policies","Real-time cost tracking depends on accurate token counting from providers, which may lag","Audit logs are immutable but not cryptographically signed — suitable for internal use, not legal evidence"],"requires":["Persistent storage for execution logs (database or log service)","Accurate token counting from LLM providers","Mechanism to correlate executions with cost data"],"input_types":["execution metadata (model, provider, latency, tokens, cost)"],"output_types":["structured logs (JSON or CSV export)","cost reports (aggregated by time, model, prompt)","visualizations (cost trends, usage patterns)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_5","uri":"capability://data.processing.analysis.prompt.evaluation.and.quality.scoring.with.custom.metrics","name":"prompt evaluation and quality scoring with custom metrics","description":"Allows users to define custom evaluation metrics (e.g., 'response contains all required fields', 'sentiment is positive', 'length < 500 tokens') and automatically score prompt outputs against these metrics. Supports both rule-based evaluations (regex, token counting, field extraction) and LLM-based evaluations (using a separate LLM to judge quality). Stores evaluation results alongside execution logs for trend analysis and comparison across prompt versions.","intents":["I want to automatically score prompt outputs against quality criteria without manual review","I need to track quality metrics over time to detect regressions in prompt performance","I want to use LLM-based evaluation (e.g., 'is this response helpful?') as a proxy for human judgment"],"best_for":["teams optimizing prompt quality with data-driven metrics","builders implementing automated quality gates in CI/CD pipelines","researchers comparing LLM outputs across models or prompts"],"limitations":["Rule-based metrics are brittle — may not capture nuanced quality aspects","LLM-based evaluation adds latency and cost (requires additional LLM calls)","Evaluation metrics are proxies for true quality — may not correlate with user satisfaction"],"requires":["Definition of evaluation metrics (rules or LLM-based)","Execution logs with prompt outputs","For LLM-based evaluation: API key for evaluation model"],"input_types":["text (prompt outputs)","structured metric definitions (rules or evaluation prompts)"],"output_types":["structured scores (numeric or categorical)","evaluation reports (pass/fail, score distributions)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_6","uri":"capability://automation.workflow.prompt.sharing.and.team.collaboration.with.access.control","name":"prompt sharing and team collaboration with access control","description":"Enables users to share prompts with team members via links or direct invitations, with granular access control (view-only, edit, admin). Tracks who modified a prompt and when, providing a change history with diffs. Supports commenting on prompts for asynchronous feedback and discussion. Likely uses a permission model (RBAC or similar) with a database to track ownership, access grants, and change history.","intents":["I want to share a prompt with my team for feedback without exposing it publicly","I need to track who changed a prompt and why, for accountability","I want to discuss prompt improvements asynchronously without context switching"],"best_for":["teams collaborating on prompt development","organizations with multiple teams sharing prompt libraries","enterprises with access control and audit requirements"],"limitations":["Granular access control adds complexity — may require careful permission management","Commenting threads can become unwieldy for long discussions","No built-in integration with external communication tools (Slack, email)"],"requires":["User authentication and authorization system","Database for storing permissions and change history","Mechanism to generate shareable links or send invitations"],"input_types":["prompts (text)","access control settings (role assignments)","comments (text)"],"output_types":["shareable links or invitations","change history with diffs","comment threads"],"categories":["automation-workflow","collaboration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-swyx__cap_7","uri":"capability://search.retrieval.prompt.library.and.search.with.semantic.discovery","name":"prompt library and search with semantic discovery","description":"Maintains a searchable library of prompts with metadata (tags, description, author, creation date) and supports both keyword search and semantic search (finding similar prompts based on embedding similarity). Allows users to organize prompts into collections or categories and discover prompts by browsing or searching. Likely uses a vector database (Pinecone, Weaviate, or similar) to enable semantic search across prompt descriptions or content.","intents":["I want to find existing prompts similar to what I'm trying to build without manual browsing","I need to organize prompts by domain or use case for easy discovery","I want to reuse proven prompts from my team's library instead of starting from scratch"],"best_for":["teams building large prompt libraries over time","organizations with multiple teams sharing prompt knowledge","builders looking to accelerate prompt development with templates"],"limitations":["Semantic search quality depends on embedding model — may miss relevant prompts if descriptions are vague","Library maintenance burden increases with size — requires curation and deprecation policies","Search results may be overwhelming for large libraries without good filtering"],"requires":["Prompt metadata (tags, descriptions)","Vector database for semantic search (optional but recommended)","Embedding model for computing prompt similarity"],"input_types":["prompts (text)","metadata (tags, descriptions)","search queries (keyword or semantic)"],"output_types":["search results (ranked by relevance)","prompt details (full text, metadata, usage stats)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":19,"verified":false,"data_access_risk":"high","permissions":["Modern browser with WebSocket support","API keys for at least one LLM provider (OpenAI, Anthropic, etc.)","Active internet connection for real-time sync and LLM calls","API keys for one or more LLM providers","Network connectivity to provider endpoints","Configuration of routing policies (optional, defaults to round-robin)","Persistent storage for prompt versions and execution logs","Mechanism to collect quality metrics (user feedback, automated evaluations, or both)","Sufficient traffic/test runs to achieve statistical power","Templating engine support (built-in or via library)"],"failure_modes":["Real-time sync adds latency overhead — likely 100-500ms per edit propagation depending on network","Concurrent edits to the same prompt section may require conflict resolution UI","Execution costs scale with number of simultaneous test runs across collaborators","Provider abstraction adds ~50-200ms overhead per request due to normalization and routing logic","API key management complexity increases with each additional provider","Some provider-specific features (e.g., vision capabilities, function calling) may not be fully abstracted","Statistical significance requires sufficient sample size — small test runs may not reach significance","Automated quality evaluation (if used) depends on proxy metrics that may not capture true quality","A/B test results are specific to the input distribution used — may not generalize","Complex template logic (conditionals, loops) may be limited compared to full programming languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.16,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"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":"inactive","updated_at":"2026-06-17T09:51:04.049Z","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=swyx","compare_url":"https://unfragile.ai/compare?artifact=swyx"}},"signature":"xzOHTgfZ0A/poSWa+V9jQRw/nseY+30v+z0ucNGJkxMVr989WbYhtJjC23sK5HSEiA+/pOBXQW665htKZMf+AA==","signedAt":"2026-06-22T15:43:27.938Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/swyx","artifact":"https://unfragile.ai/swyx","verify":"https://unfragile.ai/api/v1/verify?slug=swyx","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"}}