{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-arcee-ai-coder-large","slug":"arcee-ai-coder-large","name":"Arcee AI: Coder Large","type":"model","url":"https://openrouter.ai/models/arcee-ai~coder-large","page_url":"https://unfragile.ai/arcee-ai-coder-large","categories":["model-training"],"tags":["arcee-ai","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$5.00e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-arcee-ai-coder-large__cap_0","uri":"capability://code.generation.editing.multi.file.codebase.aware.code.generation","name":"multi-file codebase-aware code generation","description":"Generates code with awareness of multi-file context by leveraging a 32k token context window, allowing the model to ingest entire modules, related files, and cross-file dependencies simultaneously. Built on Qwen 2.5-Instruct architecture with specialized training on permissively-licensed GitHub corpora, enabling it to understand file relationships, import patterns, and architectural conventions without requiring external indexing or retrieval systems.","intents":["Generate code that respects existing codebase patterns and conventions across multiple files","Create implementations that correctly handle cross-file dependencies and imports","Refactor code while maintaining consistency across related modules","Implement features that span multiple files with proper integration points"],"best_for":["Full-stack developers building feature implementations across multiple files","Teams migrating monolithic codebases to modular architectures","Solo developers working on projects with complex interdependencies"],"limitations":["32k context window limits total input to ~24k tokens of actual code after system prompts; large monorepos may require selective file inclusion","No built-in codebase indexing or semantic search — developers must manually select relevant files to include in context","Training data cutoff means unfamiliar frameworks or very recent libraries may not be well-represented in generated code","No persistent memory of previous interactions — each request starts fresh without project-specific learning"],"requires":["API access via OpenRouter or compatible endpoint","Code files formatted as text (no binary or compiled artifacts)","Total input (system prompt + files + query) under 32k tokens"],"input_types":["text (code in any language)","natural language instructions","code snippets with context markers"],"output_types":["code (any programming language)","code explanations","refactored code with comments"],"categories":["code-generation-editing","multi-file-context"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_1","uri":"capability://code.generation.editing.bug.fix.and.error.correction.synthesis","name":"bug-fix and error correction synthesis","description":"Identifies and generates fixes for code bugs by leveraging training on synthetic bug-fix corpora that pair buggy code with correct implementations. The model learns patterns of common errors (off-by-one, null pointer dereferences, logic errors) and can generate targeted corrections with explanations of what went wrong and why the fix works.","intents":["Identify bugs in provided code and generate corrected versions","Understand why a piece of code is broken and learn the fix pattern","Generate test cases that would catch the identified bug","Refactor error-prone code patterns to more robust implementations"],"best_for":["Developers debugging production issues or test failures","Code reviewers looking for automated bug detection assistance","Teams building CI/CD pipelines that need AI-assisted code quality gates"],"limitations":["Synthetic bug-fix training may not cover domain-specific bugs or edge cases in specialized libraries","Cannot execute code to verify fixes work — relies on pattern matching rather than runtime validation","May suggest fixes that compile but don't address root cause if context is insufficient","No access to error logs, stack traces, or runtime state — must work from static code analysis alone"],"requires":["Code snippet or file with identifiable bug patterns","Sufficient context (error message, expected behavior, or test case) to disambiguate the bug","API access via OpenRouter"],"input_types":["code with bugs","error messages or test failure descriptions","expected behavior specifications"],"output_types":["corrected code","explanation of the bug and fix","suggested test cases"],"categories":["code-generation-editing","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_10","uri":"capability://code.generation.editing.security.vulnerability.detection.and.remediation.code.generation","name":"security vulnerability detection and remediation code generation","description":"Identifies potential security vulnerabilities in code by recognizing dangerous patterns (SQL injection, XSS, insecure deserialization, etc.) learned from security-focused GitHub repositories and generates secure replacement code. Provides explanations of vulnerability types and remediation strategies without requiring external security scanning tools.","intents":["Identify security vulnerabilities in code before deployment","Generate secure replacement code for vulnerable patterns","Learn secure coding practices and vulnerability prevention","Implement security best practices for authentication, authorization, and data protection"],"best_for":["Developers building security-conscious applications","Security teams reviewing code for vulnerabilities","Teams improving security posture of existing codebases"],"limitations":["Cannot identify vulnerabilities that require runtime context or environment-specific knowledge","May miss complex or novel vulnerability patterns not well-represented in training data","Security analysis is static only — cannot detect runtime exploits or configuration issues","Generated fixes may not be complete without understanding full application context","No access to threat models, security requirements, or compliance standards"],"requires":["Source code in supported language","Optional: security requirements or threat model","API access via OpenRouter"],"input_types":["source code","code snippets","security vulnerability descriptions"],"output_types":["secure code implementations","vulnerability explanations","remediation guidance","security best practice recommendations"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_11","uri":"capability://code.generation.editing.code.migration.and.language.porting.assistance","name":"code migration and language porting assistance","description":"Assists with migrating code between languages, frameworks, or architectural patterns by understanding equivalent constructs and idioms across different ecosystems learned from GitHub repositories. Generates migration guides, identifies breaking changes, and produces working implementations in target languages while preserving original functionality.","intents":["Migrate code from one programming language to another","Port applications between frameworks or architectural patterns","Upgrade code to newer language versions or framework versions","Identify and resolve breaking changes during migrations"],"best_for":["Teams migrating between technology stacks","Developers upgrading legacy systems to modern frameworks","Projects transitioning between programming languages"],"limitations":["Migration quality depends on semantic equivalence between source and target — some patterns may not have direct equivalents","Cannot verify that migrated code maintains behavioral equivalence without running tests","May miss framework-specific idioms or best practices in target language","Large-scale migrations may require manual intervention for complex architectural changes","No access to actual test suites or acceptance criteria to validate migration success"],"requires":["Source code in original language/framework","Target language/framework specified","API access via OpenRouter"],"input_types":["source code","framework specifications","migration requirements or constraints"],"output_types":["migrated code","migration guides","breaking change documentation","compatibility layers or adapters"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_12","uri":"capability://code.generation.editing.context.aware.code.completion.with.project.conventions","name":"context-aware code completion with project conventions","description":"Provides intelligent code completion suggestions that respect project-specific conventions, coding styles, and architectural patterns by analyzing surrounding code context within the 32k token window. Learns completion patterns from GitHub repositories to suggest not just syntactically correct completions but semantically appropriate code that matches project conventions.","intents":["Get code completion suggestions that match project style and conventions","Complete code with awareness of surrounding context and dependencies","Discover available APIs and methods in project context","Accelerate coding by suggesting multi-line completions"],"best_for":["Developers working in established codebases with strong conventions","Teams maintaining consistent code style across projects","IDE integration scenarios where context is available"],"limitations":["Completion quality depends on context provided — incomplete context produces generic suggestions","Cannot access project configuration files or style guides not included in context","May suggest completions that are syntactically correct but semantically wrong if context is ambiguous","No real-time feedback loop — cannot learn from user acceptance or rejection of suggestions","Limited to 32k token context window — very large files may lose context"],"requires":["Surrounding code context (typically 100-500 tokens before cursor)","Target language specified or inferred","API access via OpenRouter"],"input_types":["partial code with cursor position","surrounding code context","project conventions or style guide"],"output_types":["code completion suggestions","multi-line completions","function signatures","import statements"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_2","uri":"capability://code.generation.editing.language.agnostic.code.generation.across.15.languages","name":"language-agnostic code generation across 15+ languages","description":"Generates syntactically correct code across multiple programming languages (Python, JavaScript, TypeScript, Java, C++, Go, Rust, C#, PHP, Ruby, Kotlin, Swift, etc.) by learning language-specific syntax and idioms from permissively-licensed GitHub repositories. The model understands language-specific conventions, standard libraries, and common patterns without requiring separate language-specific models.","intents":["Generate code in a language different from the primary codebase language","Port implementations from one language to another while maintaining logic","Learn language idioms and best practices for unfamiliar languages","Generate polyglot solutions that work across multiple language ecosystems"],"best_for":["Full-stack developers working across multiple language stacks","Teams building SDKs or libraries that need multi-language implementations","Developers learning new programming languages"],"limitations":["Quality varies by language — well-represented languages (Python, JavaScript, Java) generate better code than niche languages","Cannot generate code for domain-specific languages (DSLs) or proprietary languages not in training data","May not understand language-specific performance characteristics or memory models","No built-in knowledge of language-specific build systems, package managers, or deployment patterns"],"requires":["Target language specified in prompt","API access via OpenRouter","Knowledge of target language syntax for validation"],"input_types":["natural language specifications","code in source language","pseudocode or algorithm descriptions"],"output_types":["code in target language","language-specific idioms and patterns","build/deployment instructions"],"categories":["code-generation-editing","multi-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_3","uri":"capability://text.generation.language.code.explanation.and.documentation.generation","name":"code explanation and documentation generation","description":"Generates natural language explanations of code functionality, architecture, and design decisions by analyzing code structure and patterns learned from GitHub repositories. Produces docstrings, comments, README sections, and architectural documentation that explain what code does and why it was written that way, with support for multiple documentation formats and styles.","intents":["Generate docstrings and inline comments for undocumented code","Create README and API documentation from code inspection","Explain complex algorithms or architectural patterns in plain language","Generate architecture decision records (ADRs) explaining design choices"],"best_for":["Teams improving code documentation coverage and quality","Open-source maintainers onboarding new contributors","Developers inheriting legacy codebases without documentation"],"limitations":["Cannot infer intent from code alone — may generate technically accurate but misleading explanations if code is poorly written","Documentation quality depends on code clarity; obfuscated or unconventional code produces poor explanations","No access to git history, issue trackers, or design discussions that provide context for why code was written","May generate verbose or redundant documentation if code is self-explanatory"],"requires":["Source code in any supported language","API access via OpenRouter","Optional: target documentation format (JSDoc, Sphinx, Markdown, etc.)"],"input_types":["source code","code snippets","function or class definitions"],"output_types":["docstrings","inline comments","markdown documentation","API documentation","architecture explanations"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_4","uri":"capability://code.generation.editing.code.review.and.quality.assessment","name":"code review and quality assessment","description":"Analyzes code for potential issues, style violations, performance problems, and architectural concerns by applying patterns learned from GitHub repositories and code review practices. Provides actionable feedback on code quality, security, maintainability, and performance without requiring external linting tools or static analysis frameworks.","intents":["Get automated code review feedback before submitting pull requests","Identify potential bugs, security issues, or performance problems in code","Learn code quality best practices and style conventions","Assess code maintainability and suggest refactoring opportunities"],"best_for":["Individual developers seeking code quality feedback","Teams building code quality gates into CI/CD pipelines","Code reviewers looking for automated assistance with routine checks"],"limitations":["Cannot execute code to identify runtime errors or performance bottlenecks — analysis is static only","May miss domain-specific concerns or business logic errors that require deeper context","Quality of feedback depends on code clarity and context provided","Cannot access project-specific style guides or conventions not evident in code","May produce false positives on unconventional but valid code patterns"],"requires":["Source code in supported language","API access via OpenRouter","Optional: project context or style guide"],"input_types":["source code","code snippets","pull request diffs"],"output_types":["code review comments","issue severity ratings","refactoring suggestions","best practice recommendations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_5","uri":"capability://code.generation.editing.test.case.generation.and.test.driven.development.support","name":"test case generation and test-driven development support","description":"Generates unit tests, integration tests, and edge case test scenarios by analyzing code structure and learning test patterns from GitHub repositories. Produces test code in the same language as the source code with appropriate testing frameworks, mocking strategies, and assertion patterns that match community conventions.","intents":["Generate unit tests for functions or classes with comprehensive coverage","Create edge case and boundary condition tests automatically","Generate integration tests that verify interactions between components","Support test-driven development by generating test stubs from specifications"],"best_for":["Teams improving test coverage metrics","Developers practicing test-driven development","Projects migrating to higher testing standards"],"limitations":["Generated tests may not cover all business logic or domain-specific edge cases","Cannot understand test intent from code alone — may generate technically valid but semantically incorrect tests","Test quality depends on code clarity and function signatures","May not understand complex mocking requirements or test infrastructure specific to the project","No access to actual test data, fixtures, or test environment configuration"],"requires":["Source code with clear function/class definitions","Testing framework specified or inferred from project","API access via OpenRouter"],"input_types":["function or class definitions","source code","test specifications or requirements"],"output_types":["unit test code","integration test code","test fixtures and mocks","test data generators"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_6","uri":"capability://code.generation.editing.api.and.sdk.integration.code.generation","name":"api and sdk integration code generation","description":"Generates code that integrates with external APIs and SDKs by learning integration patterns from GitHub repositories containing real API usage examples. Produces correct API calls, error handling, authentication flows, and response parsing code without requiring manual API documentation lookup or trial-and-error integration.","intents":["Generate code that correctly calls external APIs with proper authentication and error handling","Create SDK wrapper code that abstracts API complexity","Implement webhook handlers and event-driven integrations","Generate code for multi-API orchestration and data transformation"],"best_for":["Developers integrating third-party services into applications","Teams building API client libraries or SDKs","Backend developers implementing microservice communication"],"limitations":["API knowledge limited to what was in training data — recent APIs or proprietary APIs may not be well-represented","Cannot verify that generated code actually works with live API endpoints","May not understand API-specific rate limiting, pagination, or error handling requirements","Authentication patterns may be outdated or incomplete if API specifications changed after training cutoff","No access to actual API credentials or test environments"],"requires":["API name or documentation in prompt","Target language specified","API access via OpenRouter"],"input_types":["API name or endpoint","natural language integration requirements","API documentation snippets"],"output_types":["API client code","authentication and authorization code","error handling and retry logic","data transformation and parsing code"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_7","uri":"capability://code.generation.editing.code.refactoring.and.architectural.improvement.suggestions","name":"code refactoring and architectural improvement suggestions","description":"Suggests and implements code refactorings that improve maintainability, performance, and architectural quality by analyzing code structure against patterns learned from well-architected GitHub repositories. Provides refactoring recommendations with explanations of benefits and potential risks, supporting incremental improvements to legacy codebases.","intents":["Identify opportunities to improve code structure and reduce technical debt","Refactor code to follow SOLID principles and design patterns","Modernize legacy code to use current language features and idioms","Improve code performance by suggesting algorithmic or structural improvements"],"best_for":["Teams managing legacy codebases","Developers learning design patterns and architectural best practices","Projects undergoing technical debt reduction initiatives"],"limitations":["Refactoring suggestions may not account for project-specific constraints or business requirements","Cannot verify that refactored code maintains behavioral equivalence without running tests","May suggest refactorings that break existing APIs or require coordinated changes across multiple files","Performance improvement suggestions are based on patterns, not actual profiling data","Large-scale architectural changes may require human judgment beyond model capabilities"],"requires":["Source code in supported language","Optional: performance metrics or quality goals","API access via OpenRouter"],"input_types":["source code","code snippets","architectural diagrams or descriptions"],"output_types":["refactored code","refactoring explanations","migration guides","performance improvement estimates"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_8","uri":"capability://code.generation.editing.natural.language.to.code.translation.with.context.preservation","name":"natural language to code translation with context preservation","description":"Translates natural language specifications and pseudocode into executable code by learning how developers express intent in comments, docstrings, and specifications from GitHub repositories. Preserves context from surrounding code and project conventions to generate implementations that integrate seamlessly with existing codebases.","intents":["Implement features from natural language specifications or user stories","Convert pseudocode or algorithm descriptions into working code","Generate code from comments or docstrings that describe intended behavior","Implement requirements from technical specifications or design documents"],"best_for":["Product teams translating requirements into code","Developers implementing features from specifications","Teams practicing specification-driven development"],"limitations":["Ambiguous or incomplete specifications may result in incorrect implementations","Cannot clarify intent with stakeholders — must work from provided specification alone","May misinterpret domain-specific terminology or business logic","Generated code may not match project-specific conventions if not explicitly specified","No access to acceptance criteria or test cases that define correctness"],"requires":["Natural language specification or pseudocode","Target language specified","Optional: existing code context or project conventions","API access via OpenRouter"],"input_types":["natural language specifications","pseudocode","comments describing intended behavior","user stories or requirements"],"output_types":["executable code","implementation with comments","test cases or acceptance criteria"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-arcee-ai-coder-large__cap_9","uri":"capability://code.generation.editing.performance.optimization.and.algorithmic.improvement.suggestions","name":"performance optimization and algorithmic improvement suggestions","description":"Analyzes code for performance bottlenecks and suggests algorithmic or structural improvements by recognizing inefficient patterns and comparing against optimized implementations from GitHub repositories. Provides specific optimization recommendations with complexity analysis and estimated performance improvements.","intents":["Identify performance bottlenecks in code and suggest optimizations","Replace inefficient algorithms with better alternatives","Optimize data structures for specific use cases","Suggest caching, memoization, or parallelization strategies"],"best_for":["Developers optimizing performance-critical code","Teams addressing scalability issues","Performance engineers improving application efficiency"],"limitations":["Optimization suggestions are based on algorithmic patterns, not actual profiling or benchmarking","Cannot account for runtime characteristics, memory constraints, or hardware specifics","May suggest optimizations that trade readability or maintainability for performance","No access to actual performance metrics or bottleneck data","Optimization effectiveness depends on actual workload characteristics not visible in code"],"requires":["Source code with identifiable performance concerns","Optional: performance metrics or profiling data","API access via OpenRouter"],"input_types":["source code","performance metrics or profiling output","workload descriptions"],"output_types":["optimized code","complexity analysis","performance improvement estimates","optimization trade-off explanations"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["API access via OpenRouter or compatible endpoint","Code files formatted as text (no binary or compiled artifacts)","Total input (system prompt + files + query) under 32k tokens","Code snippet or file with identifiable bug patterns","Sufficient context (error message, expected behavior, or test case) to disambiguate the bug","API access via OpenRouter","Source code in supported language","Optional: security requirements or threat model","Source code in original language/framework","Target language/framework specified"],"failure_modes":["32k context window limits total input to ~24k tokens of actual code after system prompts; large monorepos may require selective file inclusion","No built-in codebase indexing or semantic search — developers must manually select relevant files to include in context","Training data cutoff means unfamiliar frameworks or very recent libraries may not be well-represented in generated code","No persistent memory of previous interactions — each request starts fresh without project-specific learning","Synthetic bug-fix training may not cover domain-specific bugs or edge cases in specialized libraries","Cannot execute code to verify fixes work — relies on pattern matching rather than runtime validation","May suggest fixes that compile but don't address root cause if context is insufficient","No access to error logs, stack traces, or runtime state — must work from static code analysis alone","Cannot identify vulnerabilities that require runtime context or environment-specific knowledge","May miss complex or novel vulnerability patterns not well-represented in training data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","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=arcee-ai-coder-large","compare_url":"https://unfragile.ai/compare?artifact=arcee-ai-coder-large"}},"signature":"fWsOcbdtdK1qSmt8Kg5OUqlAQF9yGRibc9j1jEmqeLK8BAUTXNRy1KQv/1O5EzngYqZLuVF82ybuVwFC5KlnBg==","signedAt":"2026-06-21T02:18:34.679Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/arcee-ai-coder-large","artifact":"https://unfragile.ai/arcee-ai-coder-large","verify":"https://unfragile.ai/api/v1/verify?slug=arcee-ai-coder-large","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"}}