{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-pr-agent","slug":"pr-agent","name":"PR-Agent","type":"agent","url":"https://github.com/Codium-ai/pr-agent","page_url":"https://unfragile.ai/pr-agent","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-pr-agent__cap_0","uri":"capability://code.generation.editing.multi.model.pr.code.review.with.configurable.llm.backends","name":"multi-model pr code review with configurable llm backends","description":"Analyzes pull request diffs using pluggable LLM providers (OpenAI, Anthropic, Ollama, Azure, etc.) to generate structured code review feedback. Routes requests to configured models via a provider abstraction layer that normalizes API calls, handles streaming responses, and manages token limits per model. Supports both synchronous review and asynchronous batch processing for large changesets.","intents":["I want to automatically review PRs using my preferred LLM provider without vendor lock-in","I need code review feedback that respects my organization's model compliance and cost constraints","I want to run PR analysis on self-hosted or local models for privacy-sensitive codebases"],"best_for":["Engineering teams using multiple LLM providers or migrating between vendors","Organizations with strict data residency or compliance requirements","Teams wanting to optimize review cost by choosing cheaper models for routine checks"],"limitations":["Review quality varies significantly by model — GPT-4 produces more actionable feedback than smaller models but at 10-20x higher cost","Token limits on some models (e.g., Ollama) may truncate large diffs; requires manual diff splitting for files >4KB","Streaming responses add ~500ms latency overhead vs batch API calls; not suitable for synchronous webhook responses under 2s SLA"],"requires":["API key or endpoint for at least one supported LLM provider (OpenAI, Anthropic, Ollama, Azure OpenAI, etc.)","Python 3.8+","Git repository with accessible PR/commit metadata"],"input_types":["unified diff format (git diff output)","PR metadata (title, description, author, base/head commits)","code context (file paths, language, repository structure)"],"output_types":["structured review feedback (JSON with severity levels, line-specific comments, suggestions)","markdown-formatted review comments","raw LLM response with token usage metrics"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_1","uri":"capability://data.processing.analysis.incremental.diff.parsing.and.context.aware.code.review.scoping","name":"incremental diff parsing and context-aware code review scoping","description":"Parses unified diff format to extract changed lines, identify affected functions/classes, and build a minimal code context window that includes only relevant surrounding code. Uses AST-aware language detection to understand code structure and avoid reviewing auto-generated or vendored code. Implements smart filtering to exclude low-risk changes (whitespace, comments, formatting) from detailed review.","intents":["I want PR reviews to focus on logic changes, not formatting or comment-only diffs","I need reviews that understand which functions/classes are affected to provide contextual feedback","I want to skip reviewing auto-generated or third-party code to save review tokens and time"],"best_for":["Teams with large monorepos where most PRs touch multiple files","Projects with strict code style enforcement (linters) that generate noise in diffs","Teams wanting to reduce review latency by minimizing context sent to LLMs"],"limitations":["AST parsing only works for languages with supported parsers (Python, JavaScript, Java, Go, Rust, C++); falls back to regex-based heuristics for unsupported languages with reduced accuracy","Cannot reliably detect auto-generated code without explicit markers (e.g., @generated comments); may waste tokens reviewing generated files","Whitespace/formatting filtering uses heuristics and may incorrectly classify intentional spacing changes as noise"],"requires":["Git diff output in unified format","Language detection (automatic via file extension or explicit configuration)","Optional: .gitignore or exclude patterns to skip vendored code"],"input_types":["unified diff (git diff -U3 format)","file paths and language identifiers","optional: code context from repository (for AST parsing)"],"output_types":["filtered diff with only relevant changes","structured change metadata (affected functions, change type, risk level)","context window with surrounding code for LLM analysis"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_10","uri":"capability://code.generation.editing.language.specific.code.analysis.with.ast.parsing.and.semantic.understanding","name":"language-specific code analysis with ast parsing and semantic understanding","description":"Performs language-specific analysis using Abstract Syntax Tree (AST) parsing and semantic understanding for supported languages (Python, JavaScript, Java, Go, Rust, C++, etc.). Extracts code structure (functions, classes, imports, dependencies) to provide context-aware feedback that understands code semantics rather than just text patterns. Uses language-specific linters and type checkers (if available) to enhance analysis.","intents":["I want code review feedback that understands my code's structure and semantics","I need language-specific analysis (e.g., type safety in TypeScript, memory safety in Rust)","I want to detect issues that require semantic understanding (e.g., unused imports, type mismatches)"],"best_for":["Polyglot teams using multiple programming languages","Projects where language-specific best practices are important","Teams wanting to enforce type safety and semantic correctness"],"limitations":["AST parsing only works for languages with supported parsers; falls back to text-based analysis for unsupported languages with reduced accuracy","Semantic analysis requires language-specific knowledge; accuracy varies by language maturity and parser quality","Type checking requires compilation or type inference; may fail for incomplete code or complex type systems","Performance varies by language; parsing large files (>10K lines) can add significant latency"],"requires":["Language-specific parser or AST library (tree-sitter, language-native, etc.)","Optional: language-specific linter or type checker (eslint, mypy, rustc, etc.)","Code language identifier (automatic via file extension or explicit configuration)"],"input_types":["source code (for AST parsing)","unified diff (for change analysis)","optional: linter/type checker output"],"output_types":["AST-based code structure analysis","semantic feedback (type mismatches, unused variables, etc.)","language-specific best practice suggestions","integration with linter/type checker results"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_11","uri":"capability://automation.workflow.incremental.analysis.caching.and.performance.optimization","name":"incremental analysis caching and performance optimization","description":"Caches analysis results for unchanged code sections to avoid redundant LLM calls and parsing. Uses content hashing to detect changes and invalidate cache entries only when necessary. Implements incremental analysis that focuses on changed sections while reusing cached results for unchanged code, reducing latency and token usage by 30-50% for typical PRs.","intents":["I want PR reviews to complete faster without sacrificing quality","I need to reduce API costs by avoiding redundant LLM calls for unchanged code","I want to enable real-time PR feedback without waiting for full analysis"],"best_for":["Teams with high PR volume wanting to reduce review latency","Organizations optimizing for cost by minimizing LLM API calls","Projects with frequent PR updates (rebases, fixes) where incremental analysis saves significant time"],"limitations":["Cache invalidation requires accurate change detection; false positives (invalidating valid cache) waste time, false negatives (reusing stale cache) produce incorrect results","Cache storage requires persistent storage (disk, database); adds operational complexity","Incremental analysis may miss cross-file dependencies; changes in one file may affect analysis of another","Cache coherency issues in distributed setups (multiple workers); requires careful synchronization"],"requires":["Persistent cache storage (local disk, Redis, or similar)","Change detection mechanism (content hashing, git diff analysis)","Optional: distributed cache invalidation for multi-worker setups"],"input_types":["unified diff","previous analysis results (from cache)","code content (for hashing)"],"output_types":["cached analysis results (reused from previous runs)","new analysis results (for changed sections)","cache hit/miss metrics (for performance monitoring)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_12","uri":"capability://code.generation.editing.multi.language.documentation.generation.and.api.contract.validation","name":"multi-language documentation generation and api contract validation","description":"Analyzes code changes to detect new or modified functions, classes, and APIs, then generates documentation (docstrings, JSDoc, Javadoc, etc.) in the appropriate language format. Validates API contracts (function signatures, return types, exceptions) against documentation to detect inconsistencies. Suggests documentation updates when APIs change without corresponding documentation updates.","intents":["I want to ensure new APIs are documented without manual effort","I need to detect when API changes are not reflected in documentation","I want to enforce consistent documentation standards across my codebase"],"best_for":["Teams with strict documentation requirements or API-first development","Open-source projects wanting to improve API documentation quality","Organizations using generated API documentation (Swagger, Javadoc, etc.)"],"limitations":["Generated documentation may be generic or incomplete; requires human review and enhancement","Cannot detect missing documentation for complex APIs without semantic understanding","Documentation generation quality varies by language and framework; better for statically-typed languages (Java, TypeScript) than dynamic languages (Python, JavaScript)","Validation requires parsing documentation format (docstrings, JSDoc, etc.); may fail for non-standard formats"],"requires":["Diff of code changes","Code language and documentation format identifier","Optional: existing documentation samples (for style matching)"],"input_types":["unified diff","code language","optional: existing documentation"],"output_types":["generated documentation (docstrings, JSDoc, Javadoc, etc.)","API contract validation results (signature mismatches, missing parameters, etc.)","documentation update suggestions","consistency checks (style, format, completeness)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_2","uri":"capability://text.generation.language.automated.pr.description.and.title.improvement.suggestions","name":"automated pr description and title improvement suggestions","description":"Analyzes PR title and description against the actual code changes to identify gaps, inconsistencies, or missing context. Uses LLM to generate improved descriptions that accurately reflect the changes, suggest better titles, and identify missing information (e.g., breaking changes, migration steps). Integrates with PR metadata to validate descriptions against commit messages and issue references.","intents":["I want to ensure PR descriptions accurately reflect the code changes without manual review","I need to enforce consistent PR documentation standards across my team","I want to auto-generate descriptions for PRs with minimal or unclear documentation"],"best_for":["Teams with strict PR documentation requirements or compliance needs","Open-source projects wanting to improve contributor PR quality without gatekeeping","Organizations using PR descriptions for automated changelog generation"],"limitations":["Generated descriptions may miss domain-specific context or business rationale that only humans understand","Cannot detect breaking changes reliably without semantic versioning markers or API schema analysis","Suggestions are advisory only; requires human approval before merging, adding overhead if not integrated into CI/CD"],"requires":["PR title and description text","Diff of code changes","Optional: commit messages, issue references, version tags"],"input_types":["PR metadata (title, description, author)","unified diff","commit messages","issue/ticket references"],"output_types":["improved PR title suggestion","improved PR description with sections (Summary, Changes, Breaking Changes, Migration Guide)","structured feedback (missing sections, inconsistencies, clarity issues)","markdown-formatted suggestion ready for posting as comment"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_3","uri":"capability://code.generation.editing.automated.test.coverage.impact.analysis.and.suggestions","name":"automated test coverage impact analysis and suggestions","description":"Examines code changes to identify untested or under-tested logic, then suggests test cases or test file locations where coverage should be added. Parses existing test files to understand testing patterns and conventions, then generates test suggestions that match the project's style. Integrates with coverage reports (if available) to prioritize high-impact areas.","intents":["I want to identify untested code changes before they merge","I need suggestions for test cases that match my project's testing conventions","I want to enforce minimum test coverage for critical code paths"],"best_for":["Teams with strict test coverage requirements (e.g., >80% coverage)","Projects where untested code has caused production incidents","Teams wanting to improve test quality without manual code review overhead"],"limitations":["Cannot reliably detect whether logic is actually testable without running the code; may suggest tests for untestable code","Test suggestions are generic and may not match complex testing scenarios (e.g., async/concurrent code, mocking external services)","Requires understanding of project's test framework and conventions; accuracy varies by language and framework maturity","Does not validate that suggested tests actually pass or provide meaningful coverage"],"requires":["Diff of code changes","Existing test files in the repository (for pattern matching)","Optional: coverage report (LCOV, Cobertura, or similar format)"],"input_types":["unified diff","test file samples (to infer testing patterns)","optional: coverage report data","code language and test framework identifier"],"output_types":["list of untested code sections with risk assessment","suggested test cases (as pseudocode or skeleton code)","recommended test file locations","coverage impact estimate (e.g., 'this change reduces coverage by 3%')"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_4","uri":"capability://safety.moderation.security.vulnerability.detection.in.code.changes","name":"security vulnerability detection in code changes","description":"Scans PR diffs for common security vulnerabilities (SQL injection, XSS, hardcoded secrets, insecure cryptography, etc.) using pattern matching and LLM-based semantic analysis. Integrates with SAST tools (if available) and cross-references against known vulnerability databases. Provides severity ratings and remediation suggestions for each finding.","intents":["I want to catch security vulnerabilities before they reach production","I need to enforce security best practices in code review without manual security expertise","I want to identify hardcoded secrets and credentials in PRs automatically"],"best_for":["Teams handling sensitive data or user authentication","Organizations with compliance requirements (SOC 2, HIPAA, PCI-DSS)","Open-source projects wanting to prevent security regressions from contributors"],"limitations":["Pattern-based detection has high false-positive rates for complex vulnerabilities; requires manual validation","Cannot detect logic-level vulnerabilities (e.g., authorization bypass) without deep semantic analysis","Hardcoded secret detection may miss obfuscated or encoded secrets","Effectiveness varies by language; better coverage for web languages (Python, JavaScript, PHP) than compiled languages"],"requires":["Diff of code changes","Code language identifier","Optional: SAST tool output (e.g., Semgrep, Snyk) for enhanced detection"],"input_types":["unified diff","code language","optional: SAST scan results"],"output_types":["list of detected vulnerabilities with severity (critical, high, medium, low)","code snippets showing vulnerable patterns","remediation suggestions or secure code examples","links to vulnerability documentation (CWE, OWASP)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_5","uri":"capability://code.generation.editing.performance.impact.assessment.and.optimization.suggestions","name":"performance impact assessment and optimization suggestions","description":"Analyzes code changes to identify potential performance regressions (algorithmic complexity increases, new database queries, memory leaks, etc.) and suggests optimizations. Uses heuristic analysis of code patterns (nested loops, database calls, memory allocations) combined with LLM reasoning to assess impact. Integrates with performance benchmarks (if available) to quantify expected impact.","intents":["I want to catch performance regressions before they reach production","I need to identify optimization opportunities in code changes","I want to understand the performance impact of architectural changes"],"best_for":["Performance-critical applications (e.g., real-time systems, high-traffic services)","Teams with strict SLA requirements or latency budgets","Organizations wanting to prevent performance debt accumulation"],"limitations":["Cannot accurately predict performance impact without running code; estimates are heuristic-based and may be off by orders of magnitude","Requires understanding of data structures, algorithms, and runtime behavior; accuracy varies by code complexity","Cannot detect performance issues in external dependencies or third-party libraries","Optimization suggestions may conflict with other goals (readability, maintainability, correctness)"],"requires":["Diff of code changes","Code language and framework identifier","Optional: performance benchmark results or profiling data"],"input_types":["unified diff","code language","optional: benchmark/profiling data"],"output_types":["list of potential performance issues with estimated impact (e.g., 'O(n²) loop may cause 10x slowdown for large datasets')","optimization suggestions with code examples","risk assessment (low/medium/high impact)","links to performance best practices or documentation"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_6","uri":"capability://memory.knowledge.codebase.aware.context.injection.for.review.consistency","name":"codebase-aware context injection for review consistency","description":"Indexes the repository to build a codebase knowledge base (function signatures, class hierarchies, common patterns, architectural conventions) and injects relevant context into review prompts. Uses semantic search to find similar code patterns and architectural examples from the codebase, ensuring review feedback aligns with existing conventions. Supports custom context injection via configuration (e.g., architectural guidelines, coding standards).","intents":["I want PR reviews to respect my codebase's architectural patterns and conventions","I need reviews that reference similar code in the repo to suggest consistent approaches","I want to enforce custom coding standards without manual review overhead"],"best_for":["Large teams with established architectural patterns and conventions","Monorepos where consistency across modules is critical","Organizations with custom coding standards or domain-specific patterns"],"limitations":["Indexing large codebases (>100K files) can take minutes and consume significant memory; requires caching/incremental updates","Semantic search may return irrelevant examples if codebase has inconsistent patterns","Custom context injection requires manual configuration and maintenance; not automatically inferred","Context injection adds latency (~500ms-2s per review) and increases token usage by 20-40%"],"requires":["Access to full repository source code","Optional: custom configuration file (e.g., .pr-agent.yaml) with context rules","Sufficient memory for codebase indexing (varies by repo size)"],"input_types":["repository source code (for indexing)","diff of code changes","optional: custom context configuration"],"output_types":["injected context (relevant code examples, architectural patterns)","review feedback aligned with codebase conventions","suggestions referencing similar code in the repo"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_7","uri":"capability://data.processing.analysis.batch.pr.analysis.and.reporting.with.trend.tracking","name":"batch pr analysis and reporting with trend tracking","description":"Processes multiple PRs in batch mode to generate aggregated reports on code quality trends, review patterns, and team metrics. Tracks metrics over time (e.g., average review time, common issue types, code churn) and identifies trends (e.g., increasing complexity, declining test coverage). Generates visualizations and summaries for team dashboards or executive reporting.","intents":["I want to track code quality trends across my team's PRs","I need metrics to identify systemic issues (e.g., declining test coverage, increasing complexity)","I want to generate reports for stakeholders on code review effectiveness"],"best_for":["Engineering managers wanting to track team metrics and trends","Teams with compliance/audit requirements for code quality documentation","Organizations using code quality as a KPI for team performance"],"limitations":["Batch processing can be slow for large repositories (100+ PRs); requires background job scheduling","Trend analysis requires historical data; initial reports (first week) have limited value","Metrics may be gamed or misinterpreted (e.g., high review count doesn't indicate quality)","Privacy concerns: aggregated metrics may reveal individual developer patterns"],"requires":["Access to PR history (git log, GitHub/GitLab API)","Sufficient storage for historical metrics data","Optional: dashboard/reporting tool for visualization"],"input_types":["PR metadata (title, description, author, timestamps)","diffs of multiple PRs","optional: review comments, approval data"],"output_types":["aggregated metrics (average review time, issues per PR, code churn)","trend analysis (quality improving/declining, complexity trends)","visualizations (charts, graphs)","summary reports (markdown, PDF, or JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_8","uri":"capability://tool.use.integration.github.gitlab.bitbucket.webhook.integration.with.automated.comment.posting","name":"github/gitlab/bitbucket webhook integration with automated comment posting","description":"Integrates with Git platform webhooks (GitHub, GitLab, Bitbucket) to automatically trigger PR analysis when new PRs are opened or updated. Posts review feedback directly as PR comments, suggestions, or reviews using platform-native APIs. Handles authentication, rate limiting, and idempotency to ensure reliable operation. Supports custom comment formatting and threading for readability.","intents":["I want PR reviews to happen automatically without manual triggers","I need review feedback posted directly in my Git platform's native interface","I want to integrate PR analysis into my existing CI/CD pipeline"],"best_for":["Teams using GitHub, GitLab, or Bitbucket for code hosting","Organizations wanting to automate code review without external tools","Teams with existing CI/CD pipelines wanting to add PR analysis as a step"],"limitations":["Webhook delivery is asynchronous; reviews may take 10-30 seconds to appear, creating perceived latency","Rate limiting on Git platform APIs (e.g., GitHub's 5000 requests/hour) may throttle reviews during high activity","Requires webhook secret management and HTTPS endpoint; adds operational complexity","Platform API changes may break integration; requires maintenance and version pinning"],"requires":["GitHub, GitLab, or Bitbucket account with repository access","API token with PR/comment write permissions","Publicly accessible HTTPS endpoint for webhook delivery (or self-hosted runner)","Webhook secret for security"],"input_types":["webhook payload (PR metadata, diff, event type)","Git platform API responses"],"output_types":["PR comments (posted via platform API)","PR reviews (with approval/request changes status)","PR suggestions (inline code suggestions)","webhook delivery logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pr-agent__cap_9","uri":"capability://planning.reasoning.configurable.review.rules.and.custom.prompt.engineering","name":"configurable review rules and custom prompt engineering","description":"Allows users to define custom review rules (e.g., 'flag PRs that increase file size by >20%', 'require tests for changes to auth module') and custom prompts for LLM analysis. Uses a rule engine to evaluate conditions against PR metadata and diffs, then applies custom prompts to focus LLM analysis on specific concerns. Supports rule composition and conditional logic for complex scenarios.","intents":["I want to enforce custom code review policies specific to my organization","I need to focus PR reviews on high-risk areas (e.g., auth, payments, data handling)","I want to customize review feedback to match my team's priorities and standards"],"best_for":["Organizations with custom coding standards or compliance requirements","Teams wanting to enforce domain-specific review policies (e.g., security-critical modules)","Advanced users comfortable with prompt engineering and rule configuration"],"limitations":["Rule configuration requires technical expertise; non-technical users may struggle with syntax","Custom prompts can produce inconsistent results if not carefully engineered; requires testing and iteration","Rule evaluation adds latency (~100-500ms per PR); complex rules may timeout","No built-in validation for rule correctness; misconfigured rules may silently fail or produce unexpected results"],"requires":["Configuration file (YAML, JSON, or similar) with rule definitions","Understanding of rule syntax and LLM prompt engineering","Optional: testing/validation framework for rules"],"input_types":["rule definitions (conditions, actions, custom prompts)","PR metadata and diffs (for rule evaluation)"],"output_types":["rule evaluation results (matched rules, triggered actions)","customized review feedback based on applied rules","rule execution logs (for debugging)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["API key or endpoint for at least one supported LLM provider (OpenAI, Anthropic, Ollama, Azure OpenAI, etc.)","Python 3.8+","Git repository with accessible PR/commit metadata","Git diff output in unified format","Language detection (automatic via file extension or explicit configuration)","Optional: .gitignore or exclude patterns to skip vendored code","Language-specific parser or AST library (tree-sitter, language-native, etc.)","Optional: language-specific linter or type checker (eslint, mypy, rustc, etc.)","Code language identifier (automatic via file extension or explicit configuration)","Persistent cache storage (local disk, Redis, or similar)"],"failure_modes":["Review quality varies significantly by model — GPT-4 produces more actionable feedback than smaller models but at 10-20x higher cost","Token limits on some models (e.g., Ollama) may truncate large diffs; requires manual diff splitting for files >4KB","Streaming responses add ~500ms latency overhead vs batch API calls; not suitable for synchronous webhook responses under 2s SLA","AST parsing only works for languages with supported parsers (Python, JavaScript, Java, Go, Rust, C++); falls back to regex-based heuristics for unsupported languages with reduced accuracy","Cannot reliably detect auto-generated code without explicit markers (e.g., @generated comments); may waste tokens reviewing generated files","Whitespace/formatting filtering uses heuristics and may incorrectly classify intentional spacing changes as noise","AST parsing only works for languages with supported parsers; falls back to text-based analysis for unsupported languages with reduced accuracy","Semantic analysis requires language-specific knowledge; accuracy varies by language maturity and parser quality","Type checking requires compilation or type inference; may fail for incomplete code or complex type systems","Performance varies by language; parsing large files (>10K lines) can add significant latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:04.047Z","last_scraped_at":"2026-05-03T14:00:23.056Z","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=pr-agent","compare_url":"https://unfragile.ai/compare?artifact=pr-agent"}},"signature":"l9NZHoillqBAiBsPnXrxUU4sxHRWN8XU+NzWt89Dn2j0Abh6cdFiVrH5qpXC80GEdlcZRx/+zFwnlvldkJm7BQ==","signedAt":"2026-06-21T04:56:21.056Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pr-agent","artifact":"https://unfragile.ai/pr-agent","verify":"https://unfragile.ai/api/v1/verify?slug=pr-agent","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"}}