{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-demo","slug":"demo","name":"Demo","type":"agent","url":"https://swe-agent.com/demo","page_url":"https://unfragile.ai/demo","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-demo__cap_0","uri":"capability://planning.reasoning.autonomous.github.issue.resolution.via.agent","name":"autonomous-github-issue-resolution-via-agent","description":"Deploys an agentic workflow that autonomously analyzes GitHub issues, generates solution code, and submits pull requests without human intervention. The system uses multi-step reasoning to decompose issues into subtasks, executes code generation and testing in sandboxed environments, and integrates with GitHub's API for issue tracking and PR submission. Architecture involves planning-reasoning loops that evaluate generated code against issue requirements before committing changes.","intents":["I want an AI agent to automatically fix bugs reported in my GitHub repository","I need to reduce manual triage and code review time for routine issues","I want to validate that generated solutions actually solve the stated problem before merging"],"best_for":["open-source maintainers managing high-volume issue queues","teams with well-documented issues and clear acceptance criteria","projects with comprehensive test suites to validate agent-generated fixes"],"limitations":["Requires well-structured GitHub issues with clear reproduction steps and expected behavior — ambiguous issues may result in incorrect solutions","Limited to issues solvable within single repository scope; cross-repo refactoring not supported","Agent success rate depends on test coverage; projects without tests cannot validate generated solutions automatically","No built-in handling of complex architectural decisions or design trade-offs requiring human judgment"],"requires":["GitHub repository with API token (classic or fine-grained PAT with repo read/write permissions)","CI/CD pipeline or test runner accessible to agent (GitHub Actions, local test suite, or equivalent)","Python 3.8+ or Node.js 16+ runtime environment","Sufficient API quota for LLM provider (OpenAI, Anthropic, or self-hosted model)"],"input_types":["GitHub issue text (title, description, labels, linked PRs)","Repository codebase (source files, test files, documentation)","Error logs and stack traces from issue reproduction"],"output_types":["Pull request with generated code changes","Commit messages with reasoning and test results","Structured logs of agent decision-making and validation steps"],"categories":["planning-reasoning","code-generation-editing","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-demo__cap_1","uri":"capability://code.generation.editing.codebase.context.aware.code.generation","name":"codebase-context-aware-code-generation","description":"Generates code solutions by first indexing and analyzing the target repository's full codebase, extracting patterns, dependencies, and architectural conventions. The system uses semantic code search and AST-based analysis to identify relevant existing implementations, then generates new code that adheres to the repository's style, naming conventions, and architectural patterns. Integration with version control systems enables the agent to understand code history and dependency graphs.","intents":["I want generated code to match my repository's existing patterns and conventions","I need the agent to understand my codebase structure before suggesting solutions","I want to avoid generated code that conflicts with existing implementations or introduces redundant dependencies"],"best_for":["teams with established codebases and strong architectural conventions","projects where consistency and maintainability are critical (financial systems, healthcare, critical infrastructure)","developers working in polyglot repositories with multiple languages and frameworks"],"limitations":["Indexing large codebases (>100k files) may introduce latency of 30-60 seconds before code generation begins","Semantic understanding limited to languages with robust AST parsers; dynamic languages (Ruby, Perl) have reduced pattern extraction accuracy","Cannot infer implicit architectural decisions from code alone; requires explicit documentation or comments to understand design intent","Context window limitations mean only the most relevant ~50-100 files can be included in generation prompts, potentially missing edge cases"],"requires":["Git repository with full history accessible locally or via API","Language-specific AST parsers (tree-sitter, Babel, rustc, etc.) for supported languages","Minimum 2GB RAM for codebase indexing on projects >50k files","LLM with sufficient context window (8k+ tokens) to process code examples and generation prompts"],"input_types":["GitHub/GitLab repository URL or local filesystem path","Issue description or feature request in natural language","Existing code snippets or file paths to use as reference"],"output_types":["Generated source code files adhering to repository conventions","Dependency declarations (package.json, requirements.txt, Cargo.toml, etc.)","Code comments explaining generated logic and architectural decisions"],"categories":["code-generation-editing","memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-demo__cap_2","uri":"capability://automation.workflow.test.driven.code.validation.and.refinement","name":"test-driven-code-validation-and-refinement","description":"Executes generated code against the repository's test suite in real-time, analyzes test failures, and iteratively refines code until tests pass. The system parses test output (assertion failures, stack traces, coverage reports), maps failures back to generated code sections, and uses this feedback to guide code regeneration. Supports multiple testing frameworks (pytest, Jest, RSpec, JUnit) and CI/CD integrations for end-to-end validation.","intents":["I want the agent to verify its generated code actually works before submitting a PR","I need the agent to debug and fix its own code when tests fail","I want visibility into which tests pass/fail and why the agent's solution succeeded or failed"],"best_for":["projects with comprehensive test coverage (>80% line coverage)","teams using test-driven development (TDD) practices","safety-critical systems where code correctness is non-negotiable"],"limitations":["Requires test suite to be runnable in agent's execution environment; tests with external dependencies (databases, APIs) may fail or require mocking","Test quality directly impacts agent success — flaky tests or incomplete test coverage will cause agent to generate incorrect solutions","Iterative refinement adds latency; projects with slow test suites (>5 minutes per run) may timeout before agent converges on solution","Cannot validate non-functional requirements (performance, security, accessibility) that lack automated tests"],"requires":["Test runner executable in agent environment (pytest, Jest, RSpec, Maven, Gradle, etc.)","Test suite with clear pass/fail semantics and parseable output format","Sufficient execution time budget (typically 5-30 minutes per issue depending on test suite size)","Write access to repository for committing test results and generated code"],"input_types":["Test suite files (test_*.py, *.test.js, *_test.go, etc.)","Test output logs with assertion failures and stack traces","Coverage reports (optional, for identifying untested code paths)"],"output_types":["Refined code that passes all relevant tests","Test execution logs with pass/fail status per test case","Coverage delta reports showing impact of generated code on test coverage"],"categories":["automation-workflow","code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-demo__cap_3","uri":"capability://planning.reasoning.multi.step.issue.decomposition.and.planning","name":"multi-step-issue-decomposition-and-planning","description":"Analyzes GitHub issues to extract requirements, constraints, and dependencies, then decomposes complex issues into smaller, independently solvable subtasks. The system uses natural language understanding to identify implicit requirements, generates a task dependency graph, and creates an execution plan that respects ordering constraints. Integration with GitHub's issue/PR linking enables the agent to track subtask completion and coordinate multi-step solutions.","intents":["I want the agent to break down complex issues into manageable subtasks","I need the agent to understand dependencies between tasks and solve them in the right order","I want to see a clear plan of what the agent will do before it starts making changes"],"best_for":["large, complex issues that naturally decompose into multiple PRs","teams with well-documented issue templates and acceptance criteria","projects where issue complexity correlates with solution complexity"],"limitations":["Decomposition quality depends on issue description clarity; vague or poorly-written issues may result in incorrect task breakdown","Cannot handle issues requiring cross-team coordination or external approvals","Implicit dependencies (e.g., 'this feature requires database migration') may be missed if not explicitly stated in issue","No built-in mechanism to handle task failures that invalidate downstream tasks"],"requires":["GitHub issue with clear title, description, and acceptance criteria","Repository structure that supports multiple independent PRs for subtasks","LLM with strong reasoning capabilities (GPT-4, Claude 3+, or equivalent)"],"input_types":["GitHub issue text (title, description, labels, linked issues)","Related issues and PRs for context","Repository documentation and architecture guides"],"output_types":["Structured task breakdown (JSON or markdown) with subtasks and dependencies","Execution plan with ordering and estimated effort per subtask","Linked GitHub issues or draft PRs for each subtask"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-demo__cap_4","uri":"capability://tool.use.integration.github.api.integration.with.pr.submission","name":"github-api-integration-with-pr-submission","description":"Integrates with GitHub's REST and GraphQL APIs to read issues, analyze pull requests, commit code changes, and submit new PRs with generated solutions. The system handles authentication (OAuth, personal access tokens), manages rate limiting, and implements retry logic for transient failures. Supports creating linked issues for subtasks, adding labels and assignees, and posting comments with execution summaries.","intents":["I want the agent to automatically create pull requests with generated code","I need the agent to link generated PRs to the original issue and track relationships","I want the agent to communicate its progress and results back to the GitHub issue"],"best_for":["open-source projects with public GitHub repositories","teams using GitHub for issue tracking and code review","projects with automated CI/CD pipelines that validate PRs"],"limitations":["Requires GitHub API token with repo write permissions; cannot work with read-only access","Rate limited by GitHub API (60 requests/hour for unauthenticated, 5000/hour for authenticated); high-volume automation may hit limits","Cannot bypass branch protection rules or required status checks; generated PRs may be blocked if CI fails","No built-in support for GitHub Enterprise or self-hosted GitHub instances (requires custom configuration)"],"requires":["GitHub API token (classic PAT or fine-grained token) with 'repo' scope","GitHub repository with write access","Network connectivity to api.github.com (or custom GitHub Enterprise endpoint)","Python requests library or equivalent HTTP client"],"input_types":["GitHub issue URL or issue number","Generated code files and commit messages","PR title, description, and metadata"],"output_types":["Pull request URL and PR number","Commit hashes for generated changes","GitHub API response objects (issue, PR, commit data)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-demo__cap_5","uri":"capability://automation.workflow.sandbox.execution.environment.for.code.testing","name":"sandbox-execution-environment-for-code-testing","description":"Provides an isolated execution environment where generated code can be compiled, executed, and tested without affecting the host system. The system uses containerization (Docker) or process isolation to run code, captures stdout/stderr and exit codes, and enforces resource limits (CPU, memory, timeout). Supports multiple languages and runtimes (Python, Node.js, Go, Rust, Java, etc.) with automatic dependency installation.","intents":["I want to safely execute generated code without risking the host system","I need to run tests and validate code in an isolated environment","I want to capture detailed execution logs and error messages for debugging"],"best_for":["systems that execute untrusted or generated code","CI/CD pipelines requiring isolated test environments","polyglot projects supporting multiple programming languages"],"limitations":["Container startup adds 2-5 second latency per execution; frequent test runs may accumulate significant overhead","Network isolation may prevent tests that require external API calls (unless explicitly whitelisted)","Resource limits (memory, CPU, timeout) may cause legitimate long-running tests to fail","Requires Docker daemon or equivalent containerization runtime; not available in all deployment environments"],"requires":["Docker daemon running with sufficient resources (minimum 2GB RAM, 2 CPU cores)","Language-specific base images or runtime environments (python:3.11, node:18, golang:1.21, etc.)","Write access to temporary directories for code and test artifacts","Network access to package registries (PyPI, npm, Maven Central) for dependency installation"],"input_types":["Source code files (Python, JavaScript, Go, Rust, Java, etc.)","Dependency manifests (requirements.txt, package.json, go.mod, Cargo.toml, pom.xml)","Test files and test runner commands"],"output_types":["Execution logs (stdout, stderr)","Exit code and signal information","Resource usage metrics (CPU, memory, execution time)","Test results in structured format (JUnit XML, TAP, etc.)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-demo__cap_6","uri":"capability://planning.reasoning.error.analysis.and.debugging.feedback.loop","name":"error-analysis-and-debugging-feedback-loop","description":"Analyzes test failures, compilation errors, and runtime exceptions to extract actionable debugging information, then feeds this back to the code generation system as constraints for refinement. The system parses error messages, maps them to source code locations, identifies root causes (type errors, logic errors, missing imports), and generates targeted fixes. Supports multiple error formats (Python tracebacks, JavaScript stack traces, compiler diagnostics, etc.).","intents":["I want the agent to understand why its generated code failed","I need the agent to fix its own mistakes without human intervention","I want detailed error analysis to understand what went wrong"],"best_for":["iterative code generation workflows where refinement is expected","projects with clear error messages and stack traces","systems where automated debugging can reduce human intervention"],"limitations":["Error analysis quality depends on error message clarity; cryptic or obfuscated errors may not be analyzable","Cannot fix errors caused by missing external dependencies or unavailable services","Infinite loops possible if agent generates code that produces the same error repeatedly; requires iteration limits","Some errors (e.g., performance regressions, security vulnerabilities) may not be detectable from test failures alone"],"requires":["Test suite or execution environment that produces parseable error output","Error message format documentation or examples for custom error types","LLM with strong reasoning capabilities to map errors to root causes"],"input_types":["Error messages and stack traces","Generated source code files","Test output logs with failure details"],"output_types":["Structured error analysis (root cause, affected code sections, suggested fixes)","Refined code with fixes applied","Debugging logs showing error analysis process"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["GitHub repository with API token (classic or fine-grained PAT with repo read/write permissions)","CI/CD pipeline or test runner accessible to agent (GitHub Actions, local test suite, or equivalent)","Python 3.8+ or Node.js 16+ runtime environment","Sufficient API quota for LLM provider (OpenAI, Anthropic, or self-hosted model)","Git repository with full history accessible locally or via API","Language-specific AST parsers (tree-sitter, Babel, rustc, etc.) for supported languages","Minimum 2GB RAM for codebase indexing on projects >50k files","LLM with sufficient context window (8k+ tokens) to process code examples and generation prompts","Test runner executable in agent environment (pytest, Jest, RSpec, Maven, Gradle, etc.)","Test suite with clear pass/fail semantics and parseable output format"],"failure_modes":["Requires well-structured GitHub issues with clear reproduction steps and expected behavior — ambiguous issues may result in incorrect solutions","Limited to issues solvable within single repository scope; cross-repo refactoring not supported","Agent success rate depends on test coverage; projects without tests cannot validate generated solutions automatically","No built-in handling of complex architectural decisions or design trade-offs requiring human judgment","Indexing large codebases (>100k files) may introduce latency of 30-60 seconds before code generation begins","Semantic understanding limited to languages with robust AST parsers; dynamic languages (Ruby, Perl) have reduced pattern extraction accuracy","Cannot infer implicit architectural decisions from code alone; requires explicit documentation or comments to understand design intent","Context window limitations mean only the most relevant ~50-100 files can be included in generation prompts, potentially missing edge cases","Requires test suite to be runnable in agent's execution environment; tests with external dependencies (databases, APIs) may fail or require mocking","Test quality directly impacts agent success — flaky tests or incomplete test coverage will cause agent to generate incorrect solutions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.24,"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":"2026-06-17T09:51:03.037Z","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=demo","compare_url":"https://unfragile.ai/compare?artifact=demo"}},"signature":"AX/hHNbP2OHDaB3ugH6zD2yFY3E3BWAbd4ogn838/cmOtqvGZtMzCpxhr5KNCN9wJyRRivnT0lXjM9jzAjn+CQ==","signedAt":"2026-06-21T04:03:01.853Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/demo","artifact":"https://unfragile.ai/demo","verify":"https://unfragile.ai/api/v1/verify?slug=demo","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"}}