{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-cody-by-sourcegraph","slug":"cody-by-sourcegraph","name":"Cody by Sourcegraph","type":"agent","url":"https://docs.sourcegraph.com/cody","page_url":"https://unfragile.ai/cody-by-sourcegraph","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-cody-by-sourcegraph__cap_0","uri":"capability://code.generation.editing.codebase.aware.code.generation.with.semantic.indexing","name":"codebase-aware code generation with semantic indexing","description":"Generates code by leveraging Sourcegraph's semantic code index to understand repository structure, dependencies, and patterns. Uses embeddings-based retrieval to surface relevant code context from the entire codebase, then passes this context to an LLM (Claude, GPT-4, or local models) to generate contextually appropriate code that follows existing patterns and conventions.","intents":["Generate new functions that follow the codebase's established patterns and naming conventions","Write code that integrates seamlessly with existing modules without breaking dependencies","Create implementations that reuse existing utilities and libraries already in the project","Generate boilerplate that matches the project's architectural style"],"best_for":["teams with large, complex codebases (100k+ LOC) where pattern consistency matters","developers working in monorepos with multiple interconnected services","organizations using Sourcegraph for code intelligence already"],"limitations":["Requires Sourcegraph instance to be running and indexed — cannot work offline or with unindexed repos","Indexing latency means newly committed code may not be immediately available for context (typically 5-15 minute delay)","Context window limits mean very large codebases may not surface all relevant patterns","Semantic indexing quality depends on code quality and documentation — poorly structured repos yield weaker context"],"requires":["Sourcegraph instance (self-hosted or cloud) with code indexed","LLM API access (OpenAI, Anthropic, or local model via Ollama)","IDE extension or web interface (VS Code, JetBrains, web UI)","Repository connected to Sourcegraph for indexing"],"input_types":["natural language prompt describing desired code","partial code snippet to complete or extend","file path or symbol name to generate around"],"output_types":["generated code (functions, classes, modules)","code snippets with inline comments","multi-file code changes"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_1","uri":"capability://text.generation.language.natural.language.code.explanation.and.documentation.generation","name":"natural language code explanation and documentation generation","description":"Analyzes selected code blocks and generates human-readable explanations, docstrings, and documentation by passing code through an LLM with optional codebase context. Can generate explanations at multiple levels of detail (one-liner, paragraph, full documentation) and produce documentation in multiple formats (JSDoc, Python docstrings, Markdown).","intents":["Understand what a complex function or module does without reading the implementation","Generate missing docstrings and comments for undocumented code","Create API documentation from code signatures and implementations","Explain architectural decisions or design patterns used in specific code sections"],"best_for":["teams with legacy codebases lacking documentation","developers onboarding to unfamiliar projects","technical writers generating API docs from source code"],"limitations":["Explanations may be inaccurate if code is obfuscated, uses non-standard patterns, or has misleading variable names","Generated docstrings may not capture all edge cases or error conditions","Cannot infer business logic intent — only explains technical implementation","Quality depends on LLM's training data; domain-specific or proprietary patterns may be misexplained"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Code selection in IDE or web interface","Optional: Sourcegraph indexing for enhanced context"],"input_types":["code snippet (function, class, module)","file path","symbol name"],"output_types":["natural language explanation (text)","formatted docstring (JSDoc, docstring, etc.)","Markdown documentation"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_10","uri":"capability://tool.use.integration.llm.model.selection.and.provider.abstraction","name":"llm model selection and provider abstraction","description":"Abstracts away LLM provider differences by supporting multiple LLM backends (OpenAI, Anthropic, local models via Ollama, etc.) through a unified interface. Allows users to switch between providers and models without changing code, and supports configuring different models for different tasks (code generation vs. explanation).","intents":["Use different LLM providers without changing Cody configuration","Switch to local models for privacy-sensitive code","Use cheaper models for simple tasks and more capable models for complex tasks","Maintain compatibility as new LLM providers and models emerge"],"best_for":["organizations with LLM provider preferences or cost constraints","teams needing to keep code analysis on-premises for security","developers experimenting with different models"],"limitations":["Model quality varies significantly — cheaper models produce lower-quality suggestions","Local models require significant compute resources and may be slower","API compatibility issues may arise when switching between providers","Some advanced features may only work with specific models or providers"],"requires":["API key for chosen LLM provider (OpenAI, Anthropic, etc.)","Or: Local LLM setup with Ollama or similar","Configuration file or UI to specify model and provider"],"input_types":["model name and provider configuration","API credentials"],"output_types":["unified LLM interface for all Cody features"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_2","uri":"capability://code.generation.editing.multi.file.code.refactoring.with.dependency.tracking","name":"multi-file code refactoring with dependency tracking","description":"Performs refactoring operations (rename, extract, move, restructure) across multiple files while maintaining referential integrity. Uses Sourcegraph's semantic index to identify all usages of symbols, then generates coordinated changes across the codebase to preserve functionality. Supports both automated refactoring and LLM-assisted refactoring for complex transformations.","intents":["Rename a function or class and automatically update all call sites across the codebase","Extract a utility function and update all locations where the pattern is used","Move code between modules and update all imports and references","Refactor code to follow new architectural patterns while preserving behavior"],"best_for":["teams maintaining large codebases where manual refactoring is error-prone","developers performing large-scale architectural changes","teams migrating between frameworks or design patterns"],"limitations":["Refactoring accuracy depends on semantic index quality — dynamic imports or reflection may not be tracked","Cannot refactor across language boundaries in polyglot repos without manual intervention","LLM-assisted refactoring may introduce subtle bugs in complex transformations","Requires write access to repository and CI/CD integration to validate changes"],"requires":["Sourcegraph instance with semantic indexing enabled","LLM API access for complex refactoring suggestions","IDE extension or web interface with refactoring UI","Git repository with write permissions"],"input_types":["symbol name or code selection","refactoring operation type (rename, extract, move, etc.)","natural language description of desired transformation"],"output_types":["multi-file code changes (diffs)","refactoring plan with affected files listed","automated pull request with changes"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_3","uri":"capability://code.generation.editing.context.aware.code.completion.with.repository.patterns","name":"context-aware code completion with repository patterns","description":"Provides inline code completion suggestions by analyzing the current file context, surrounding code patterns, and repository-wide conventions. Uses a combination of local syntax analysis and Sourcegraph's semantic index to suggest completions that match the project's style, imports, and architectural patterns. Supports multi-line completions and function signature inference.","intents":["Complete function calls with correct parameters based on how they're used elsewhere in the codebase","Auto-complete imports by suggesting modules already used in similar contexts","Suggest variable names and patterns that match the project's naming conventions","Complete multi-line code blocks (loops, conditionals, function bodies) based on context"],"best_for":["developers working in codebases with strong conventions and patterns","teams using consistent architectural styles across modules","developers who want completions that match existing code style"],"limitations":["Completion quality degrades in codebases with inconsistent patterns or poor naming","Latency may be noticeable (200-500ms) due to semantic index queries","Cannot complete code that uses dynamic imports or runtime-generated APIs","May suggest outdated patterns if codebase has evolved without refactoring"],"requires":["IDE extension (VS Code, JetBrains, etc.)","Sourcegraph instance with semantic indexing","LLM API access (OpenAI, Anthropic, or local model)"],"input_types":["partial code at cursor position","surrounding code context (file, function scope)","file type and language"],"output_types":["completion suggestion (text)","multi-line code block","ranked list of alternative completions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_4","uri":"capability://search.retrieval.intelligent.code.search.with.natural.language.queries","name":"intelligent code search with natural language queries","description":"Enables searching code using natural language descriptions rather than regex or keywords. Converts natural language queries to semantic embeddings and searches Sourcegraph's indexed codebase for matching code patterns, functions, and implementations. Returns ranked results with code snippets and context about where matches are used.","intents":["Find all functions that handle authentication without knowing their exact names","Locate code that implements a specific pattern or algorithm","Search for examples of how a particular library is used across the codebase","Find all places where a specific business logic is implemented"],"best_for":["developers onboarding to large unfamiliar codebases","teams searching for code examples and patterns","refactoring efforts where you need to find all instances of a pattern"],"limitations":["Search quality depends on code documentation and naming clarity","Semantic search may miss code that implements the pattern differently than described","Cannot search across private repositories without appropriate access","Embedding-based search may return false positives if similar patterns exist for different purposes"],"requires":["Sourcegraph instance with semantic indexing and embeddings enabled","Web interface or IDE extension","Repository indexed and up-to-date"],"input_types":["natural language query (e.g., 'functions that validate email addresses')","code snippet to find similar implementations"],"output_types":["ranked list of matching code snippets","file paths and line numbers","context about usage and dependencies"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_5","uri":"capability://code.generation.editing.bug.detection.and.fix.suggestion.with.codebase.context","name":"bug detection and fix suggestion with codebase context","description":"Analyzes code for potential bugs by examining patterns, type mismatches, and common error conditions, then suggests fixes based on how similar issues are handled elsewhere in the codebase. Uses static analysis combined with LLM reasoning to identify issues and propose corrections that align with project conventions.","intents":["Identify null pointer dereferences and suggest null checks based on project patterns","Find type mismatches and suggest corrections that match the codebase's type handling style","Detect off-by-one errors and boundary condition bugs","Identify resource leaks and suggest proper cleanup patterns used elsewhere in the project"],"best_for":["teams wanting automated code review for common bug patterns","developers working in dynamically-typed languages where type errors are common","projects with strict quality standards"],"limitations":["Cannot detect logic bugs that require understanding business requirements","False positives are common if codebase uses unconventional patterns","Suggested fixes may not be optimal — require human review before applying","Effectiveness depends on code quality and test coverage"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Sourcegraph instance for context (optional but improves accuracy)","IDE extension or web interface"],"input_types":["code file or snippet","specific function or module to analyze"],"output_types":["list of identified issues with severity levels","suggested fixes with explanations","code diffs showing proposed changes"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_6","uri":"capability://code.generation.editing.test.generation.with.coverage.aware.suggestions","name":"test generation with coverage-aware suggestions","description":"Generates unit tests for functions and modules by analyzing code structure, dependencies, and existing test patterns in the codebase. Uses LLM to create test cases covering normal paths, edge cases, and error conditions, then formats them according to the project's testing framework and style conventions.","intents":["Generate unit tests for untested functions","Create test cases for edge cases and error conditions","Generate integration tests that follow the project's testing patterns","Increase code coverage by suggesting tests for uncovered code paths"],"best_for":["teams improving test coverage on legacy code","developers writing new code and wanting immediate test coverage","projects with established testing patterns and frameworks"],"limitations":["Generated tests may not cover all business logic edge cases","Tests require human review to ensure they test the right behavior","Cannot generate tests for code with external dependencies without mocking setup","Test quality depends on how well the function's intent is expressed in code"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","Sourcegraph instance to analyze existing test patterns (optional)","IDE extension or web interface","Testing framework installed (Jest, pytest, JUnit, etc.)"],"input_types":["function or class to test","file path","code snippet"],"output_types":["generated test code (matching project's framework)","test cases with assertions","mock/stub setup code"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_7","uri":"capability://memory.knowledge.cross.repository.dependency.analysis.and.impact.assessment","name":"cross-repository dependency analysis and impact assessment","description":"Analyzes how changes in one repository affect dependent repositories by querying Sourcegraph's dependency graph. Identifies all downstream consumers of a module, function, or API, then assesses the impact of proposed changes and suggests migration paths for breaking changes.","intents":["Understand the blast radius of a breaking API change before making it","Find all repositories that depend on a specific library or module","Plan migrations when deprecating functionality","Identify unused code that can be safely removed"],"best_for":["teams managing shared libraries or platform code","organizations with monorepos or multi-repo architectures","teams performing large-scale refactoring across multiple projects"],"limitations":["Requires all repositories to be indexed in Sourcegraph — cannot analyze external dependencies","Dynamic imports and reflection-based dependencies may not be tracked","Dependency graph may be incomplete if repositories are not properly configured","Cannot assess runtime impact — only static code dependencies"],"requires":["Sourcegraph instance with multiple repositories indexed","Dependency graph configured and up-to-date","Access to all relevant repositories"],"input_types":["module or function name","file path","API signature"],"output_types":["list of dependent repositories","impact assessment (number of affected files, functions, etc.)","suggested migration paths","breaking change analysis"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_8","uri":"capability://text.generation.language.conversational.code.assistant.with.multi.turn.context","name":"conversational code assistant with multi-turn context","description":"Provides an interactive chat interface where developers can ask questions about code, request changes, and iterate on solutions. Maintains conversation history and code context across multiple turns, allowing follow-up questions and refinements. Supports both code-specific questions and general development questions with codebase awareness.","intents":["Ask questions about how specific code works and get explanations","Request code changes and iterate on them through conversation","Get suggestions for improving code quality and performance","Ask architectural questions and get answers grounded in the actual codebase"],"best_for":["developers learning unfamiliar codebases","teams using Cody as a pair programmer for code review and improvement","developers who prefer conversational interaction over single-shot code generation"],"limitations":["Context window limits mean very long conversations may lose earlier context","LLM may hallucinate or provide inaccurate information if codebase context is incomplete","Conversation history is not persisted across sessions (unless explicitly saved)","Multi-turn refinement may require multiple API calls, increasing latency and cost"],"requires":["LLM API access (OpenAI, Anthropic, or local model)","IDE extension or web interface","Optional: Sourcegraph instance for enhanced codebase context"],"input_types":["natural language questions","code snippets or file references","follow-up refinements"],"output_types":["natural language responses","code suggestions and examples","explanations and documentation"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-cody-by-sourcegraph__cap_9","uri":"capability://code.generation.editing.ide.integrated.code.review.with.inline.suggestions","name":"ide-integrated code review with inline suggestions","description":"Provides code review feedback directly in the IDE by analyzing code changes (diffs) and suggesting improvements. Uses LLM to identify potential issues, style violations, and optimization opportunities, then displays suggestions inline with the ability to apply fixes directly. Integrates with git to analyze staged changes and pull requests.","intents":["Get automated code review feedback before submitting a pull request","Identify style violations and suggest corrections that match project conventions","Find performance issues and suggest optimizations","Ensure code follows security best practices"],"best_for":["teams wanting to shift code review left (earlier in development)","developers working solo without peer review","projects with strict code quality standards"],"limitations":["Suggestions may be overly conservative or miss context-specific requirements","Cannot understand business logic intent — only technical code quality","False positives common if project uses unconventional patterns","Requires developer to manually review and apply suggestions"],"requires":["IDE extension (VS Code, JetBrains, etc.)","LLM API access (OpenAI, Anthropic, or local model)","Git integration for diff analysis","Optional: Sourcegraph instance for codebase context"],"input_types":["code diff (staged changes)","pull request","file or function to review"],"output_types":["inline suggestions with explanations","severity levels (error, warning, info)","suggested fixes with one-click apply"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Sourcegraph instance (self-hosted or cloud) with code indexed","LLM API access (OpenAI, Anthropic, or local model via Ollama)","IDE extension or web interface (VS Code, JetBrains, web UI)","Repository connected to Sourcegraph for indexing","LLM API access (OpenAI, Anthropic, or local model)","Code selection in IDE or web interface","Optional: Sourcegraph indexing for enhanced context","API key for chosen LLM provider (OpenAI, Anthropic, etc.)","Or: Local LLM setup with Ollama or similar","Configuration file or UI to specify model and provider"],"failure_modes":["Requires Sourcegraph instance to be running and indexed — cannot work offline or with unindexed repos","Indexing latency means newly committed code may not be immediately available for context (typically 5-15 minute delay)","Context window limits mean very large codebases may not surface all relevant patterns","Semantic indexing quality depends on code quality and documentation — poorly structured repos yield weaker context","Explanations may be inaccurate if code is obfuscated, uses non-standard patterns, or has misleading variable names","Generated docstrings may not capture all edge cases or error conditions","Cannot infer business logic intent — only explains technical implementation","Quality depends on LLM's training data; domain-specific or proprietary patterns may be misexplained","Model quality varies significantly — cheaper models produce lower-quality suggestions","Local models require significant compute resources and may be slower","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","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=cody-by-sourcegraph","compare_url":"https://unfragile.ai/compare?artifact=cody-by-sourcegraph"}},"signature":"5PH2RDiXmR7ghVdBSALrkccl188A5sczb5hw2wcCkxV+VIQvGJCy/n1xdiIteadno5Bq853pGWFhq6EDVfhHAQ==","signedAt":"2026-06-22T15:18:51.141Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cody-by-sourcegraph","artifact":"https://unfragile.ai/cody-by-sourcegraph","verify":"https://unfragile.ai/api/v1/verify?slug=cody-by-sourcegraph","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"}}