{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-tang-vu--contribai","slug":"tang-vu--contribai","name":"ContribAI","type":"agent","url":"https://github.com/tang-vu/ContribAI","page_url":"https://unfragile.ai/tang-vu--contribai","categories":["ai-agents"],"tags":["agent","ai","ai-agent","automation","autonomous-agent","code-analysis","code-quality","contributions","gemini","github","llm","open-source","pull-requests","python","security"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-tang-vu--contribai__cap_0","uri":"capability://search.retrieval.autonomous.repository.discovery.and.filtering","name":"autonomous-repository-discovery-and-filtering","description":"Automatically discovers open-source repositories matching configurable criteria (language, topic, star count, activity level) by querying GitHub's API with intelligent filtering logic. The agent maintains state about previously analyzed repos to avoid redundant processing and applies heuristic scoring to prioritize high-impact contribution opportunities based on code quality signals and maintenance status.","intents":["Find unmaintained or under-maintained open-source projects that need contributions","Discover repositories in specific programming languages or domains for targeted contributions","Identify projects with high community engagement but low contribution velocity","Build a pipeline of potential contribution targets without manual GitHub searching"],"best_for":["Teams automating open-source contribution workflows","Organizations seeking to increase their open-source footprint programmatically","Developers building autonomous agent systems that interact with GitHub"],"limitations":["GitHub API rate limits (60 requests/hour unauthenticated, 5000/hour authenticated) constrain discovery throughput","Filtering heuristics may miss niche projects that don't match standard quality signals","No built-in mechanism to detect toxic or low-quality projects before analysis begins"],"requires":["GitHub API access (personal access token or OAuth credentials)","Network connectivity to GitHub.com","Configuration file specifying search criteria (language, topics, min stars, etc.)"],"input_types":["configuration parameters (language, topic filters, star thresholds)","GitHub API credentials"],"output_types":["structured list of repository metadata (name, URL, language, stars, last commit date)","prioritized queue of repos for downstream analysis"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_1","uri":"capability://code.generation.editing.codebase.analysis.with.llm.semantic.understanding","name":"codebase-analysis-with-llm-semantic-understanding","description":"Analyzes cloned repository code by feeding file contents and directory structure to an LLM (Gemini or compatible) with semantic understanding prompts. The agent extracts architectural patterns, identifies code quality issues, security vulnerabilities, and documentation gaps by leveraging the LLM's ability to reason about code intent and best practices without requiring static analysis tool chains.","intents":["Understand the overall architecture and design patterns of an unfamiliar codebase quickly","Identify specific code quality issues, anti-patterns, or technical debt without running linters","Detect security vulnerabilities or unsafe patterns that static tools might miss","Prioritize which files or modules to focus on for meaningful contributions"],"best_for":["Autonomous agents that need to understand arbitrary codebases without project-specific configuration","Teams analyzing code quality across diverse language ecosystems","Developers building contribution agents that must reason about code semantics"],"limitations":["LLM context window limits analysis to smaller codebases or requires file selection heuristics","Semantic analysis is slower than static analysis tools (multiple LLM calls per repo)","LLM hallucinations may produce false positives for vulnerability detection","No language-specific AST parsing — relies on LLM's training data accuracy for each language"],"requires":["LLM API access (Gemini API key or compatible provider)","Git installed for repository cloning","Sufficient disk space for cloning target repositories","Network connectivity to LLM provider"],"input_types":["repository URL (git clone source)","code files (text content from cloned repo)","directory structure metadata"],"output_types":["structured analysis report (identified issues, patterns, recommendations)","prioritized list of improvement opportunities","semantic understanding of codebase architecture"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_2","uri":"capability://planning.reasoning.intelligent.issue.detection.and.prioritization","name":"intelligent-issue-detection-and-prioritization","description":"Scans repository issue trackers and code analysis results to identify fixable problems that align with the agent's capabilities and contribution scope. Uses LLM reasoning to evaluate issue complexity, estimate effort, assess impact, and rank issues by likelihood of successful PR acceptance based on project activity patterns and maintainer responsiveness.","intents":["Find 'good first issue' or 'help wanted' issues that the agent can realistically solve","Prioritize issues by impact and effort to maximize contribution success rate","Avoid wasting agent resources on stale issues or projects with unresponsive maintainers","Match detected code problems to existing GitHub issues to avoid duplicate work"],"best_for":["Autonomous agents operating across multiple repositories with limited compute budget","Teams optimizing contribution success rate and PR merge likelihood","Systems that need to balance exploration (finding new issues) with exploitation (solving high-confidence issues)"],"limitations":["Issue labels and descriptions are often inconsistent or missing across projects","No direct signal for maintainer responsiveness without historical PR analysis","Complexity estimation is heuristic-based and may underestimate effort for novel problem types","Cannot detect issues that exist in code but aren't tracked in issue tracker"],"requires":["GitHub API access to read issues and PR history","LLM API for complexity and impact reasoning","Historical data on project PR merge rates (optional but improves prioritization)"],"input_types":["GitHub issue metadata (title, description, labels, creation date, comments)","code analysis results from previous capability","project activity metrics (PR merge rate, issue resolution time)"],"output_types":["ranked list of issues with effort/impact scores","structured problem definition for each selected issue","confidence score for successful PR acceptance"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_3","uri":"capability://code.generation.editing.llm.driven.fix.generation.with.context.awareness","name":"llm-driven-fix-generation-with-context-awareness","description":"Generates code fixes by prompting an LLM with detailed context: the identified problem, relevant code snippets, project coding style, existing tests, and dependency constraints. The agent constructs context-aware prompts that include the full file being modified, related files, and project-specific patterns extracted from codebase analysis, enabling the LLM to generate fixes that align with project conventions and architecture.","intents":["Generate bug fixes that match the project's coding style and architectural patterns","Create feature implementations that integrate seamlessly with existing code","Produce fixes that pass existing tests without breaking other functionality","Generate documentation or test improvements alongside code changes"],"best_for":["Autonomous agents contributing to diverse open-source projects with varying code styles","Teams that need generated code to be immediately mergeable without extensive review cycles","Developers building agents that must respect project-specific conventions and constraints"],"limitations":["LLM context window limits the amount of surrounding code that can be included in prompts","Generated fixes may not compile or pass tests without iterative refinement","No guarantee that generated code follows security best practices or performance requirements","Requires accurate problem definition — garbage input to LLM produces garbage output"],"requires":["LLM API access (Gemini or compatible)","Complete codebase context (files, dependencies, test suite)","Clear problem definition from issue detection capability","Project configuration files (package.json, requirements.txt, etc.) for dependency context"],"input_types":["problem description (from issue detection)","relevant code snippets and full files","project coding style guidelines (inferred or explicit)","test files and test framework information","dependency manifest files"],"output_types":["generated code patch or complete file modifications","test code (unit tests, integration tests)","documentation updates","commit message suggestions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_4","uri":"capability://automation.workflow.iterative.fix.validation.and.refinement","name":"iterative-fix-validation-and-refinement","description":"Validates generated fixes by running the project's test suite, linters, and type checkers locally. If validation fails, the agent feeds error messages and test output back to the LLM with a refinement prompt, iteratively improving the fix until it passes all checks or reaches a maximum iteration limit. This closes the loop between generation and validation without human intervention.","intents":["Ensure generated fixes actually compile and pass tests before submitting PRs","Automatically refine broken fixes by analyzing error messages and retrying","Catch style violations and type errors before PR submission","Reduce PR rejection rate by validating fixes locally first"],"best_for":["Autonomous agents that must operate without human review before PR submission","Teams seeking to minimize PR rejection and review cycles","Systems that need high-confidence fixes before interacting with maintainers"],"limitations":["Requires project dependencies to be installed — adds setup time and complexity","Test suite may be slow, limiting iteration speed for large projects","Some projects have flaky tests that fail non-deterministically, causing false negatives","LLM may not understand complex error messages or may get stuck in refinement loops","No access to runtime behavior — only static validation is possible"],"requires":["Project build tools and test framework installed (pytest, Jest, Maven, etc.)","Project dependencies installed (npm install, pip install, etc.)","LLM API for refinement prompting","Sufficient disk space and compute for running test suites"],"input_types":["generated code from fix-generation capability","test suite files and test configuration","project build configuration","error messages and test output"],"output_types":["validated, passing code changes","test execution reports","refinement history (iterations performed)","confidence score for fix quality"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_5","uri":"capability://tool.use.integration.github.pr.creation.with.semantic.commit.messages","name":"github-pr-creation-with-semantic-commit-messages","description":"Automatically creates pull requests on GitHub with semantically meaningful commit messages, detailed PR descriptions, and proper branch naming. The agent generates PR descriptions by summarizing the fix, explaining the rationale, linking to related issues, and highlighting any breaking changes or dependencies. Uses GitHub API to create branches, commit changes, and open PRs with proper metadata.","intents":["Submit validated fixes to GitHub without manual PR creation","Generate professional PR descriptions that explain the change and its impact","Automatically link PRs to related issues for project tracking","Create properly formatted commit messages that follow project conventions"],"best_for":["Autonomous agents that need to interact with GitHub programmatically","Teams automating contribution workflows across multiple repositories","Developers building agents that must create PRs that pass maintainer review"],"limitations":["Requires write access to target repositories (fork-based workflow or direct access)","GitHub API rate limits may throttle PR creation if agent operates at scale","Cannot guarantee PR will be reviewed or merged — depends on maintainer responsiveness","PR descriptions generated by LLM may be verbose or miss important context","No built-in mechanism to handle PR feedback or iterate on rejected PRs"],"requires":["GitHub API token with 'repo' scope (for creating branches and PRs)","Git installed and configured with GitHub credentials","Repository fork or write access to target repository","LLM API for generating PR descriptions"],"input_types":["validated code changes (from validation capability)","issue metadata and problem description","project conventions and PR template (if available)","commit message guidelines"],"output_types":["GitHub PR URL","PR metadata (number, branch name, commit hash)","PR description text","commit message"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_6","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.fallback","name":"multi-provider-llm-abstraction-with-fallback","description":"Abstracts LLM interactions behind a provider-agnostic interface that supports multiple LLM backends (Gemini, OpenAI, Anthropic, local Ollama) with automatic fallback. If one provider fails or hits rate limits, the agent transparently switches to an alternative provider without interrupting the workflow. Manages API keys, request formatting, and response parsing for each provider.","intents":["Use multiple LLM providers to avoid single-provider dependency and rate limit issues","Switch between providers based on cost, latency, or capability requirements","Gracefully handle provider outages without stopping the agent","Support both cloud and local LLM deployments for flexibility"],"best_for":["Autonomous agents operating at scale with high LLM call volume","Teams seeking cost optimization by using cheaper providers for simple tasks","Developers building agents that must be resilient to provider outages","Organizations with privacy requirements that prefer local LLM deployment"],"limitations":["Different providers have different model capabilities — fallback may produce lower-quality results","Switching providers mid-workflow may introduce inconsistency in reasoning or output format","Requires managing multiple API keys and credentials","Local Ollama deployment requires significant compute resources and setup overhead","No automatic cost tracking or optimization across providers"],"requires":["API keys for at least one LLM provider (Gemini, OpenAI, Anthropic, or local Ollama)","Network connectivity to cloud providers or local Ollama instance","Configuration file specifying provider preferences and fallback order"],"input_types":["LLM prompts (text)","provider configuration (API keys, model names, preferences)","fallback strategy specification"],"output_types":["LLM responses (text, structured JSON)","provider metadata (which provider was used, latency, cost estimate)"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_7","uri":"capability://data.processing.analysis.project.specific.configuration.inference","name":"project-specific-configuration-inference","description":"Automatically detects and infers project configuration by analyzing repository structure, manifest files (package.json, requirements.txt, Cargo.toml, etc.), CI/CD configuration (GitHub Actions, GitLab CI), and code patterns. Extracts coding style conventions, dependency constraints, test framework, build tools, and project-specific patterns without requiring explicit configuration files.","intents":["Understand project setup and conventions without reading documentation","Generate code that matches project-specific style and patterns automatically","Identify test framework and test patterns for generating appropriate tests","Detect build tools and dependency management approach for proper integration"],"best_for":["Autonomous agents that must adapt to arbitrary project configurations","Teams analyzing diverse open-source projects with varying conventions","Developers building agents that need to respect project-specific constraints"],"limitations":["Inference is heuristic-based and may miss non-standard configurations","Requires parsing multiple file formats (JSON, YAML, TOML, etc.) with potential parsing errors","Cannot detect implicit conventions that aren't reflected in configuration files","May misidentify patterns in small or atypical projects"],"requires":["Cloned repository with full directory structure","Parsers for common manifest formats (JSON, YAML, TOML)","Access to CI/CD configuration files"],"input_types":["repository directory structure","manifest files (package.json, requirements.txt, etc.)","CI/CD configuration files","source code files (for pattern inference)"],"output_types":["structured project configuration (language, framework, test tool, build tool)","detected coding style conventions","dependency constraints and versions","test framework and patterns"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_8","uri":"capability://automation.workflow.autonomous.workflow.orchestration.and.state.management","name":"autonomous-workflow-orchestration-and-state-management","description":"Orchestrates the entire contribution workflow as a state machine: repository discovery → codebase analysis → issue detection → fix generation → validation → PR creation. Maintains persistent state about processed repositories, attempted issues, and PR submissions to avoid redundant work. Implements retry logic, error recovery, and graceful degradation when individual steps fail.","intents":["Run the agent continuously to discover and contribute to multiple repositories","Resume interrupted workflows without losing progress or reprocessing completed steps","Track which repositories and issues have been attempted to avoid duplicate effort","Handle failures gracefully and continue processing other repositories"],"best_for":["Autonomous agents operating continuously or on a schedule","Teams running contribution agents at scale across many repositories","Systems that need to maintain state across multiple execution sessions"],"limitations":["State persistence requires external storage (database, file system) — no built-in persistence","Workflow orchestration adds complexity and potential for deadlocks or race conditions","Error recovery heuristics may not handle all failure modes gracefully","No built-in monitoring or alerting for workflow failures","State management overhead increases latency for each workflow step"],"requires":["Persistent storage for workflow state (file system, database, or cloud storage)","Logging infrastructure for debugging workflow issues","Configuration for retry policies and error handling"],"input_types":["workflow configuration (discovery criteria, issue filters, contribution scope)","previous workflow state (if resuming)"],"output_types":["workflow execution logs","state snapshots (for resuming interrupted workflows)","summary of contributions attempted and PRs created"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-tang-vu--contribai__cap_9","uri":"capability://safety.moderation.security.and.quality.filtering.for.contribution.safety","name":"security-and-quality-filtering-for-contribution-safety","description":"Applies safety filters before submitting PRs to prevent contributing malicious or low-quality code. Checks for common security vulnerabilities (SQL injection patterns, hardcoded credentials, unsafe deserialization), code quality metrics (complexity, duplication, test coverage), and project-specific constraints. Blocks PR submission if generated code fails safety checks, with configurable thresholds.","intents":["Prevent the agent from submitting PRs with security vulnerabilities or malicious code","Ensure generated code meets minimum quality standards before submission","Avoid contributing code that violates project-specific constraints or policies","Maintain the agent's reputation by filtering out low-quality contributions"],"best_for":["Autonomous agents operating without human review before PR submission","Teams concerned about agent-generated code quality and security","Organizations with strict security and quality policies"],"limitations":["Security checks are heuristic-based and may miss sophisticated vulnerabilities","Quality metrics are approximate and may flag legitimate code as low-quality","Cannot detect all types of security issues without running the code","Overly strict filters may prevent valid contributions from being submitted","Requires tuning thresholds for each project or domain"],"requires":["Static analysis tools or LLM-based security checking","Code quality metrics (complexity, duplication, coverage)","Configuration for security and quality thresholds"],"input_types":["generated code from fix-generation capability","project security and quality policies","safety threshold configuration"],"output_types":["safety check results (pass/fail)","detailed report of security and quality issues found","recommendation to submit or reject PR"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["GitHub API access (personal access token or OAuth credentials)","Network connectivity to GitHub.com","Configuration file specifying search criteria (language, topics, min stars, etc.)","LLM API access (Gemini API key or compatible provider)","Git installed for repository cloning","Sufficient disk space for cloning target repositories","Network connectivity to LLM provider","GitHub API access to read issues and PR history","LLM API for complexity and impact reasoning","Historical data on project PR merge rates (optional but improves prioritization)"],"failure_modes":["GitHub API rate limits (60 requests/hour unauthenticated, 5000/hour authenticated) constrain discovery throughput","Filtering heuristics may miss niche projects that don't match standard quality signals","No built-in mechanism to detect toxic or low-quality projects before analysis begins","LLM context window limits analysis to smaller codebases or requires file selection heuristics","Semantic analysis is slower than static analysis tools (multiple LLM calls per repo)","LLM hallucinations may produce false positives for vulnerability detection","No language-specific AST parsing — relies on LLM's training data accuracy for each language","Issue labels and descriptions are often inconsistent or missing across projects","No direct signal for maintainer responsiveness without historical PR analysis","Complexity estimation is heuristic-based and may underestimate effort for novel problem types","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3184820576311394,"quality":0.45,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:57:09.058Z","last_commit":"2026-05-01T01:07:09Z"},"community":{"stars":239,"forks":90,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tang-vu--contribai","compare_url":"https://unfragile.ai/compare?artifact=tang-vu--contribai"}},"signature":"JqkDYGNlgA23V7MttcYqIKeUeZ0iOOxl/Us481Y6Sv9dDP2p6/H+rP/FwtgBuTTe0Huo5dqqHLRxgofI53HLBA==","signedAt":"2026-06-20T21:28:35.079Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tang-vu--contribai","artifact":"https://unfragile.ai/tang-vu--contribai","verify":"https://unfragile.ai/api/v1/verify?slug=tang-vu--contribai","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"}}