{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-test-driver","slug":"test-driver","name":"Test Driver","type":"agent","url":"https://testdriver.ai/","page_url":"https://unfragile.ai/test-driver","categories":["testing-quality"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-test-driver__cap_0","uri":"capability://code.generation.editing.natural.language.to.test.code.generation","name":"natural-language-to-test-code-generation","description":"Converts natural language test descriptions into executable test code by leveraging vision-based UI understanding and MCP protocol integration. The system analyzes the application's visual state, identifies UI elements, and generates test scripts that interact with those elements based on the user's plain-English test intent. This approach eliminates the need for developers to write boilerplate test code or learn test framework syntax.","intents":["I want to describe a test in plain English and have it automatically converted to executable test code","I need to create test cases without writing Selenium/Playwright/Cypress code manually","I want to generate test suites quickly for rapid iteration on new features"],"best_for":["QA teams without strong programming backgrounds","development teams seeking to reduce test authoring time","startups prototyping test automation quickly"],"limitations":["Requires visual UI elements to be present and detectable — cannot test headless APIs or non-visual systems","Test code generation quality depends on clarity of natural language description; ambiguous descriptions may produce incorrect tests","Generated code language(s) not documented — unclear if tests are Playwright, Selenium, or proprietary format"],"requires":["GitHub repository with application code or deployed application","Application must be visually interactive (web, desktop, or extension)","MCP-compatible model integration (likely Claude or compatible LLM)"],"input_types":["natural language text description of test scenario","application UI (visual state captured via screenshot)"],"output_types":["executable test code (format unspecified)","test file ready for integration into CI/CD pipeline"],"categories":["code-generation-editing","test-automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_1","uri":"capability://image.visual.vision.based.ui.element.detection.and.interaction","name":"vision-based-ui-element-detection-and-interaction","description":"Analyzes application screenshots using computer vision to identify interactive UI elements (buttons, inputs, links, dropdowns) and their spatial relationships, then executes programmatic interactions (clicks, typing, scrolling) on those elements. The system caches the vision-derived representation of the UI to avoid redundant AI analysis on subsequent test runs when the UI remains unchanged, reducing latency and API calls.","intents":["I want tests to automatically find and interact with UI elements without hardcoding selectors","I need tests to adapt when UI elements move or change styling but retain the same functionality","I want to test applications where I don't have access to the source code or selector IDs"],"best_for":["testing third-party applications or SaaS products without source code access","teams with frequently changing UI layouts or design systems","cross-platform testing (web, desktop, extensions) where selector strategies differ"],"limitations":["Vision-based detection may fail on visually ambiguous or low-contrast UI elements","Caching effectiveness depends on UI stability — high-frequency UI changes reduce cache hit rates and increase AI invocations","Cannot reliably interact with canvas-based or custom-rendered UI elements that lack semantic structure","Latency on first run or after UI changes due to vision analysis overhead (specific latency not documented)"],"requires":["Application with visual UI (web, desktop, or extension)","Screenshot capability (browser DevTools, OS screenshot API, or instrumentation)","Vision-capable LLM model (likely Claude with vision capabilities)"],"input_types":["application screenshot or visual frame","natural language description of desired interaction"],"output_types":["coordinates or element references for interaction","execution logs of performed actions"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_10","uri":"capability://tool.use.integration.mcp.based.test.generation.and.execution.protocol","name":"mcp-based-test-generation-and-execution-protocol","description":"Uses the Model Context Protocol (MCP) to standardize communication between the test generation AI model and the test execution environment. MCP enables the system to abstract away model-specific details, support multiple LLM providers, and maintain consistent test generation and execution semantics across different configurations. The protocol handles tool invocation, context passing, and result streaming.","intents":["I want to use different LLM models for test generation without changing my test infrastructure","I need a standardized protocol for test generation that works across multiple AI providers","I want to extend TestDriver with custom tools and integrations via MCP"],"best_for":["organizations wanting flexibility in LLM provider selection","teams building custom integrations with TestDriver","enterprises with specific model requirements or compliance constraints"],"limitations":["MCP support details not documented — unclear which MCP features are implemented or which LLM providers are supported","Custom tool development for MCP not documented — no SDK or documentation for extending TestDriver via MCP","Model selection and configuration not exposed to users — unclear if users can choose between models or if model is fixed","MCP overhead and latency impact not documented"],"requires":["MCP-compatible LLM model (likely Claude or compatible implementation)","MCP client/server infrastructure","Understanding of MCP protocol and tool definitions"],"input_types":["test description and context","MCP tool definitions and schemas"],"output_types":["test code generated via MCP tool invocation","execution results and logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_2","uri":"capability://automation.workflow.adaptive.test.maintenance.on.ui.changes","name":"adaptive-test-maintenance-on-ui-changes","description":"Monitors application UI state across test runs and automatically re-invokes the AI model to update element detection and test logic when UI changes are detected. The system compares current visual state against cached representations, identifies what changed, and regenerates test steps to interact with the new UI layout while preserving the original test intent. This eliminates manual test maintenance when UI evolves.","intents":["I want tests to automatically adapt when my UI changes without requiring manual updates","I need to reduce the maintenance burden of keeping tests in sync with design changes","I want to detect when UI changes break tests and automatically fix them"],"best_for":["agile teams with frequent UI iterations and design changes","products with evolving design systems or A/B testing","teams lacking dedicated test maintenance resources"],"limitations":["Automatic adaptation may fail if UI changes fundamentally alter test semantics (e.g., button moved but functionality changed)","Requires re-invocation of AI model on every UI change, increasing latency and potential API costs (cost model unknown)","No mechanism documented for detecting false positives (minor visual changes that don't affect test validity)","Cannot distinguish between intentional UI changes and rendering artifacts or transient states"],"requires":["Baseline UI state captured and cached from initial test run","Mechanism to detect visual differences between runs (screenshot comparison)","Access to re-invoke AI model on demand"],"input_types":["current application screenshot","cached baseline UI representation","original test intent/description"],"output_types":["updated test code with new element references","change detection report (what UI elements changed)","test execution result"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_3","uri":"capability://automation.workflow.multi.platform.test.execution.and.orchestration","name":"multi-platform-test-execution-and-orchestration","description":"Executes generated test code across multiple application platforms (web browsers, Chrome extensions, VS Code extensions, Windows/macOS/Linux desktop applications) from a centralized cloud-based execution environment. The system manages platform-specific instrumentation, handles cross-platform UI interaction patterns, and collects execution telemetry (screenshots, logs, network traffic, performance metrics) in a unified format for reporting and analysis.","intents":["I want to run the same test logic across web and desktop versions of my application","I need to test browser extensions and desktop apps without maintaining separate test frameworks","I want centralized test execution and reporting across all my application platforms"],"best_for":["companies with multi-platform products (web + desktop + extensions)","teams seeking unified test infrastructure across heterogeneous platforms","organizations wanting to avoid maintaining multiple test frameworks"],"limitations":["Execution environment details not documented — unclear if tests run in VMs, containers, or browser-based sandboxes","Platform-specific limitations not disclosed (e.g., desktop app testing may require specific OS versions or dependencies)","No documentation on concurrent test execution limits or resource allocation per test","Cross-platform test compatibility not guaranteed — same test may behave differently on different platforms due to UI/interaction differences"],"requires":["Application deployed or accessible from cloud execution environment","Platform-specific instrumentation (browser automation, OS-level interaction APIs, extension loaders)","GitHub integration for test result reporting"],"input_types":["executable test code (format unspecified)","platform target specification (web, desktop, extension type)"],"output_types":["test execution results (pass/fail status)","video replay of test execution","execution logs and screenshots","performance metrics (CPU, memory, network timing)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_4","uri":"capability://data.processing.analysis.network.request.inspection.and.validation","name":"network-request-inspection-and-validation","description":"Intercepts and analyzes HTTP network traffic during test execution, capturing request/response headers, payloads, timing, and status codes. The system enables tests to validate API behavior, verify data flow, and assert on network-level conditions without requiring direct API access or code instrumentation. This is implemented via browser/application instrumentation that proxies or monitors network activity.","intents":["I want to verify that my application makes the correct API calls with the right parameters","I need to validate response data and HTTP status codes as part of my test assertions","I want to test OAuth flows and other network-dependent interactions"],"best_for":["testing applications with complex API interactions","validating third-party integrations and OAuth flows","teams needing to verify both UI and API behavior in a single test"],"limitations":["Network inspection may not work with encrypted traffic (HTTPS) without certificate pinning bypass or proxy configuration","Cannot inspect network traffic from native desktop applications without OS-level instrumentation (implementation unclear)","Timing data may be affected by proxy/instrumentation overhead, reducing accuracy for performance assertions","No documentation on handling of WebSocket, gRPC, or other non-HTTP protocols"],"requires":["Application network traffic must be observable (not using certificate pinning without bypass)","Browser automation or OS-level network instrumentation capability","Test code must include network assertions (format unspecified)"],"input_types":["test execution context with network activity","network assertion criteria (expected headers, payloads, status codes)"],"output_types":["captured HTTP request/response data","network timing metrics","assertion pass/fail results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_5","uri":"capability://automation.workflow.test.result.reporting.and.github.integration","name":"test-result-reporting-and-github-integration","description":"Automatically posts test execution results to GitHub pull requests, including pass/fail status, video replays, execution logs, and JUnit XML exports. The system integrates with GitHub's PR workflow to block merges until tests pass, provide inline feedback on failures, and maintain historical test result trends. Results are stored in the TestDriver console dashboard for analysis and debugging.","intents":["I want test results to appear automatically on my GitHub PRs without manual integration","I need to block PRs from merging until tests pass","I want to see video replays of test failures to debug issues quickly"],"best_for":["GitHub-based development teams","organizations using GitHub Actions or other GitHub-integrated CI/CD","teams seeking tight integration between testing and code review workflows"],"limitations":["GitHub integration only — no documented support for GitLab, Bitbucket, or other Git platforms","Merge blocking requires GitHub branch protection rules configuration (not automatic)","Video replay storage and retention policies not documented — unclear how long videos are retained or where they're stored","JUnit XML export format not documented — compatibility with other CI/CD systems unknown"],"requires":["GitHub repository with write access for TestDriver bot","GitHub Actions or webhook integration configured","GitHub branch protection rules (optional, for merge blocking)"],"input_types":["test execution results (pass/fail, logs, video, metrics)","GitHub PR context (repo, PR number, commit SHA)"],"output_types":["GitHub PR comment with test results","JUnit XML file for CI/CD pipeline consumption","test result dashboard entry in TestDriver console"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_6","uri":"capability://data.processing.analysis.test.flakiness.detection.and.trend.analysis","name":"test-flakiness-detection-and-trend-analysis","description":"Tracks test execution results across multiple runs and identifies flaky tests (tests that pass inconsistently) by analyzing pass/fail patterns and failure frequency. The system maintains historical test result data in the TestDriver console dashboard, enabling teams to identify unreliable tests, understand failure trends, and prioritize test stabilization efforts. Metrics include pass rates, failure frequency, and temporal trends.","intents":["I want to identify which tests are flaky and unreliable","I need to understand test failure trends over time to prioritize stabilization","I want to see which tests are blocking my CI/CD pipeline most frequently"],"best_for":["teams with large test suites experiencing flakiness issues","organizations seeking to improve test reliability and CI/CD stability","teams needing data-driven insights into test quality"],"limitations":["Flakiness detection algorithm not documented — unclear how many runs are required to classify a test as flaky or what pass rate threshold is used","No documented mechanism for distinguishing between test flakiness and environmental instability (e.g., network timeouts, resource contention)","Trend analysis scope not specified — unclear if trends are per-test, per-suite, or per-platform","No documented actions or recommendations for fixing flaky tests"],"requires":["Multiple test runs across time (historical data collection)","Test result storage in TestDriver console","Access to TestDriver dashboard for viewing trends"],"input_types":["test execution results from multiple runs","test metadata (name, platform, suite)"],"output_types":["flakiness classification (flaky vs stable)","pass rate percentage","failure frequency metrics","trend visualization in dashboard"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_7","uri":"capability://automation.workflow.rich.media.and.oauth.flow.testing","name":"rich-media-and-oauth-flow-testing","description":"Enables testing of complex user interactions including file uploads, PDF viewing, canvas-based content, video playback, and OAuth authentication flows. The system handles these interactions through the same vision-based UI detection and interaction mechanism, treating rich media elements as interactive UI components. This allows end-to-end testing of features that traditional test frameworks struggle with.","intents":["I want to test file upload functionality and verify that files are processed correctly","I need to test OAuth login flows without hardcoding credentials","I want to test applications that use canvas, video, or other embedded media"],"best_for":["applications with complex authentication flows (OAuth, SAML, multi-factor)","products with file upload, document processing, or media features","teams testing rich media applications (video platforms, design tools, document editors)"],"limitations":["Canvas and video element interaction relies on vision-based detection, which may be unreliable for dynamic or rapidly changing content","OAuth flow testing requires credential handling mechanism not documented — unclear how credentials are stored, rotated, or secured","PDF interaction capabilities not detailed — unclear if tests can extract text, verify content, or only interact with UI elements","File upload testing limited to files accessible from test execution environment — cannot test with arbitrary user files"],"requires":["Application with rich media or OAuth integration","Vision-capable model for detecting interactive elements within media","Credential management for OAuth testing (implementation unspecified)"],"input_types":["test description including rich media interaction steps","file paths or OAuth provider credentials","application UI with embedded media"],"output_types":["test execution result with media interaction logs","verification of file processing or OAuth completion","video replay showing media interactions"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_8","uri":"capability://automation.workflow.test.execution.video.replay.and.debugging","name":"test-execution-video-replay-and-debugging","description":"Records video of test execution including all UI interactions, network requests, and system state changes, then makes videos available in the TestDriver console for debugging and analysis. The system captures visual evidence of what the test did, enabling developers to understand failures without re-running tests or examining logs. Videos include synchronized logs and performance metrics for comprehensive debugging context.","intents":["I want to see exactly what happened during a test failure without re-running the test","I need to debug test failures quickly by watching a video replay","I want to share test execution evidence with team members for collaborative debugging"],"best_for":["teams debugging complex test failures","organizations with distributed teams needing to share test execution evidence","teams seeking to reduce debugging time through visual evidence"],"limitations":["Video storage and retention policies not documented — unclear how long videos are kept or what storage limits apply","Video replay may not capture all relevant state (e.g., browser console errors, network timing details) — requires cross-referencing with logs","Large test suites may generate significant video storage costs (cost model unknown)","Video playback performance not documented — unclear if videos are streamed or downloaded"],"requires":["Test execution environment with screen capture capability","Video encoding and storage infrastructure","TestDriver console access for viewing videos"],"input_types":["test execution with screen capture enabled","execution logs and metrics"],"output_types":["video file of test execution","synchronized logs and metrics overlay","shareable video link in TestDriver console"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-test-driver__cap_9","uri":"capability://data.processing.analysis.performance.monitoring.during.test.execution","name":"performance-monitoring-during-test-execution","description":"Collects CPU, memory, and network performance metrics during test execution and makes them available for analysis and assertion. The system monitors system resource usage and application performance characteristics, enabling tests to validate not just functional correctness but also performance requirements. Metrics are captured alongside test results for trend analysis.","intents":["I want to verify that my application meets performance requirements during testing","I need to detect performance regressions introduced by code changes","I want to monitor resource usage (CPU, memory) during test execution"],"best_for":["performance-sensitive applications (real-time systems, resource-constrained environments)","teams seeking to catch performance regressions in CI/CD","organizations with strict performance SLAs"],"limitations":["Performance metrics collection overhead not documented — monitoring may affect measured performance","Metric granularity and sampling rate not specified — unclear if metrics are per-second, per-operation, or aggregated","Performance assertion mechanisms not documented — unclear how tests define and validate performance thresholds","Cross-platform performance comparison not addressed — same test may have different performance characteristics on different platforms"],"requires":["Test execution environment with system monitoring capability","Performance metric collection infrastructure","Test code with performance assertions (format unspecified)"],"input_types":["test execution context","performance threshold definitions"],"output_types":["CPU usage metrics","memory usage metrics","network bandwidth metrics","performance assertion results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["GitHub repository with application code or deployed application","Application must be visually interactive (web, desktop, or extension)","MCP-compatible model integration (likely Claude or compatible LLM)","Application with visual UI (web, desktop, or extension)","Screenshot capability (browser DevTools, OS screenshot API, or instrumentation)","Vision-capable LLM model (likely Claude with vision capabilities)","MCP-compatible LLM model (likely Claude or compatible implementation)","MCP client/server infrastructure","Understanding of MCP protocol and tool definitions","Baseline UI state captured and cached from initial test run"],"failure_modes":["Requires visual UI elements to be present and detectable — cannot test headless APIs or non-visual systems","Test code generation quality depends on clarity of natural language description; ambiguous descriptions may produce incorrect tests","Generated code language(s) not documented — unclear if tests are Playwright, Selenium, or proprietary format","Vision-based detection may fail on visually ambiguous or low-contrast UI elements","Caching effectiveness depends on UI stability — high-frequency UI changes reduce cache hit rates and increase AI invocations","Cannot reliably interact with canvas-based or custom-rendered UI elements that lack semantic structure","Latency on first run or after UI changes due to vision analysis overhead (specific latency not documented)","MCP support details not documented — unclear which MCP features are implemented or which LLM providers are supported","Custom tool development for MCP not documented — no SDK or documentation for extending TestDriver via MCP","Model selection and configuration not exposed to users — unclear if users can choose between models or if model is fixed","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.050Z","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=test-driver","compare_url":"https://unfragile.ai/compare?artifact=test-driver"}},"signature":"pAPGJui5sjHcJQwAQ81xppkwftH5Uxoh9hdoromqG6HkjBx4k7kUHoKy7FavT8/MS8OqICIzQAEgwvJ2ei64AQ==","signedAt":"2026-06-21T21:23:21.320Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/test-driver","artifact":"https://unfragile.ai/test-driver","verify":"https://unfragile.ai/api/v1/verify?slug=test-driver","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"}}