{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-clippy","slug":"clippy","name":"Clippy","type":"agent","url":"https://github.com/ennucore/clippy/","page_url":"https://unfragile.ai/clippy","categories":["ai-agents","testing-quality"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-clippy__cap_0","uri":"capability://planning.reasoning.multi.step.code.planning.with.task.decomposition","name":"multi-step code planning with task decomposition","description":"Clippy decomposes complex coding tasks into sequential, executable steps by analyzing user requirements and generating intermediate planning artifacts. The agent uses chain-of-thought reasoning to break down high-level goals (e.g., 'build a REST API') into concrete subtasks (schema design, endpoint implementation, testing), maintaining context across steps to ensure coherent execution flow and dependency ordering.","intents":["I need to break down a large feature into manageable coding tasks","Help me plan the architecture before I start writing code","Generate a step-by-step implementation roadmap for a complex system"],"best_for":["developers building complex features who want AI-assisted planning","teams needing structured task decomposition before implementation","solo developers prototyping multi-component systems"],"limitations":["Planning quality depends on clarity of initial requirements — vague specifications produce fragmented task lists","No persistent state between planning and execution — task context must be re-injected for each step","Limited to sequential planning; cannot generate parallel task graphs or dependency DAGs"],"requires":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible endpoint)","Task description or user requirement as text input"],"input_types":["text (natural language requirement or feature description)","code (existing codebase context for incremental planning)"],"output_types":["structured task list (steps with descriptions)","pseudo-code or implementation outline"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_1","uri":"capability://code.generation.editing.context.aware.code.generation.with.codebase.indexing","name":"context-aware code generation with codebase indexing","description":"Clippy generates code by first indexing the existing codebase to understand patterns, conventions, and dependencies, then using this context to produce code that matches the project's style and architecture. The agent analyzes imports, function signatures, naming conventions, and module structure to ensure generated code integrates seamlessly without requiring manual refactoring or style corrections.","intents":["Generate new functions that follow my project's coding style automatically","Create code that imports and uses existing utilities correctly","Extend existing modules with new features that maintain architectural consistency"],"best_for":["developers working in established codebases with strong conventions","teams maintaining large monorepos where consistency is critical","projects with custom frameworks or domain-specific patterns"],"limitations":["Indexing overhead scales with codebase size — very large repos (>100k LOC) may cause latency","Cannot infer implicit architectural patterns not visible in code (e.g., unwritten design principles)","Requires codebase to be locally accessible; does not support remote repository indexing"],"requires":["Python 3.8+","Local access to full codebase files","LLM API with sufficient context window (8k+ tokens recommended)"],"input_types":["text (feature request or function specification)","code (existing module or file to extend)"],"output_types":["code (generated functions, classes, or modules)","code with import statements and type hints"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_2","uri":"capability://code.generation.editing.iterative.code.debugging.with.error.analysis.and.fix.generation","name":"iterative code debugging with error analysis and fix generation","description":"Clippy executes generated code, captures runtime errors and test failures, analyzes the error messages and stack traces, then automatically generates corrected code. The agent maintains a debugging loop that re-executes code after each fix attempt, comparing output against expected behavior and refining fixes based on new error information.","intents":["Fix compilation or runtime errors in generated code automatically","Debug test failures by analyzing error messages and regenerating implementations","Iteratively improve code quality until all tests pass"],"best_for":["developers using AI code generation who want automated error recovery","CI/CD pipelines that need to validate and fix generated code before deployment","rapid prototyping scenarios where iteration speed matters more than first-pass quality"],"limitations":["Debugging loop can enter infinite cycles on ambiguous errors — requires manual intervention after 3-5 failed attempts","Error messages must be parseable (structured stack traces work better than cryptic compiler errors)","Cannot debug logical errors that don't produce exceptions — only catches runtime failures and test assertion failures","Execution environment must be sandboxed or trusted; running arbitrary generated code poses security risks"],"requires":["Python 3.8+","Execution environment (local interpreter or containerized runtime)","Test framework or error output mechanism to validate fixes","LLM API with code understanding capabilities"],"input_types":["code (generated or user-written code with errors)","error messages (stack traces, test output, compiler errors)"],"output_types":["corrected code","debugging report (errors found, fixes applied, iterations count)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_3","uri":"capability://code.generation.editing.automated.test.generation.and.validation","name":"automated test generation and validation","description":"Clippy generates unit tests for code based on function signatures, docstrings, and expected behavior, then executes tests against the implementation to validate correctness. The agent creates test cases covering happy paths, edge cases, and error conditions, and can regenerate implementation code if tests fail, creating a test-driven development loop.","intents":["Generate comprehensive test suites for new functions automatically","Validate generated code against expected behavior before deployment","Ensure code quality through automated test coverage"],"best_for":["teams adopting test-driven development with AI assistance","projects requiring high code quality and test coverage","rapid development cycles where manual test writing is a bottleneck"],"limitations":["Test generation quality depends on clarity of function specifications — poorly documented functions produce weak tests","Cannot generate tests for non-deterministic or stateful code without explicit state management","Edge case coverage is heuristic-based and may miss domain-specific corner cases","Requires test framework to be pre-installed and configured (pytest, unittest, etc.)"],"requires":["Python 3.8+","Test framework (pytest, unittest, or compatible)","Function signatures and docstrings or specification text","Execution environment to run generated tests"],"input_types":["code (function or module to test)","text (specification or expected behavior description)"],"output_types":["test code (unit tests in pytest or unittest format)","test execution report (pass/fail status, coverage metrics)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_4","uri":"capability://code.generation.editing.multi.language.code.generation.with.syntax.adaptation","name":"multi-language code generation with syntax adaptation","description":"Clippy generates code in multiple programming languages (Python, JavaScript, Java, Go, etc.) by understanding language-specific syntax, idioms, and standard libraries. The agent adapts generated code to match target language conventions (e.g., snake_case for Python, camelCase for JavaScript) and uses appropriate language features (async/await, generators, type hints) based on the target language.","intents":["Generate code in a specific programming language from a high-level description","Translate or port code between different languages while maintaining functionality","Create polyglot systems where different components are in different languages"],"best_for":["polyglot teams working across multiple languages","projects requiring code generation in less common languages","developers learning new languages who want AI-assisted code generation"],"limitations":["Language support varies — some languages may have weaker generation quality due to less training data","Idiom translation is imperfect — generated code may be syntactically correct but not idiomatic","Standard library knowledge is limited to commonly-used APIs; obscure or version-specific features may not be handled correctly","Type system differences between languages can cause semantic mismatches in translated code"],"requires":["Python 3.8+","LLM with multi-language training data","Target language compiler or interpreter for validation","Language-specific execution environment"],"input_types":["text (feature description or specification)","code (code in source language to translate)"],"output_types":["code (generated code in target language)","code with language-specific idioms and conventions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_5","uri":"capability://automation.workflow.agent.driven.workflow.orchestration.with.tool.integration","name":"agent-driven workflow orchestration with tool integration","description":"Clippy operates as an autonomous agent that chains together multiple tools (code execution, testing, file I/O, version control) to complete multi-step coding tasks without human intervention. The agent maintains execution state, decides which tools to invoke based on task progress, and handles tool output to guide subsequent actions, implementing a planning-execution-feedback loop.","intents":["Execute a complete coding task from planning through testing without manual steps","Automate repetitive coding workflows (scaffolding, refactoring, migration)","Integrate code generation with version control and deployment pipelines"],"best_for":["teams automating code generation in CI/CD pipelines","developers who want hands-off code generation and validation","organizations standardizing code generation workflows"],"limitations":["Agent decision-making can be unpredictable — same input may produce different tool sequences on different runs","Tool integration requires explicit configuration; not all tools are automatically available","Long-running workflows may exceed LLM context windows, losing execution history","Error recovery is limited — if a tool fails, the agent may not gracefully handle the failure"],"requires":["Python 3.8+","Tool implementations (code executor, test runner, file system access, git client)","LLM API with function calling or tool use capabilities","Execution environment with necessary permissions (file write, process execution)"],"input_types":["text (high-level task description)","structured task specification (JSON or YAML)"],"output_types":["code (generated and validated)","execution log (tools invoked, results, decisions made)","artifacts (files created, tests run, commits made)"],"categories":["automation-workflow","tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_6","uri":"capability://code.generation.editing.incremental.code.refactoring.with.impact.analysis","name":"incremental code refactoring with impact analysis","description":"Clippy refactors code by analyzing dependencies and call graphs to understand the impact of changes, then generates refactored code that maintains backward compatibility or explicitly documents breaking changes. The agent can rename functions, extract methods, reorganize modules, and apply design patterns while tracking which parts of the codebase are affected and validating that tests still pass after refactoring.","intents":["Refactor code to improve readability or performance while maintaining functionality","Extract common patterns into reusable utilities across the codebase","Apply design patterns to improve code structure and maintainability"],"best_for":["teams maintaining legacy codebases that need modernization","developers wanting to improve code quality without manual refactoring","projects with comprehensive test coverage that can validate refactoring"],"limitations":["Refactoring correctness depends on test coverage — untested code paths may break silently","Impact analysis is limited to statically-analyzable dependencies; dynamic dispatch or reflection-based code may be missed","Large-scale refactoring across many files may require multiple iterations and human review","Cannot refactor code with external dependencies or APIs that are not visible in the codebase"],"requires":["Python 3.8+","Full codebase access for dependency analysis","Comprehensive test suite to validate refactoring","LLM with code understanding capabilities"],"input_types":["code (module or function to refactor)","text (refactoring goal or pattern to apply)"],"output_types":["refactored code","impact report (files affected, breaking changes, test results)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_7","uri":"capability://text.generation.language.context.preserving.multi.turn.code.conversation","name":"context-preserving multi-turn code conversation","description":"Clippy maintains conversation state across multiple user interactions, allowing developers to iteratively refine code through natural language feedback. The agent remembers previous code generation decisions, maintains a working version of the code, and can apply incremental changes based on user requests without losing context or requiring full code re-specification.","intents":["Have a back-and-forth conversation to iteratively improve generated code","Request small modifications to previously generated code without re-specifying the entire function","Get explanations of generated code and ask follow-up questions"],"best_for":["developers who prefer iterative refinement over one-shot generation","exploratory coding sessions where requirements evolve","learning scenarios where developers want to understand generated code"],"limitations":["Context window limits conversation length — very long conversations may lose early context","State management is in-memory; conversation history is lost if the agent restarts","Ambiguous user requests may be interpreted differently than intended, requiring clarification","No persistent storage of conversation history across sessions"],"requires":["Python 3.8+","LLM API with sufficient context window (8k+ tokens)","Conversation state management (in-memory or external storage)"],"input_types":["text (natural language requests and feedback)","code (user-provided code to modify or extend)"],"output_types":["code (generated or modified)","text (explanations, clarifications, suggestions)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-clippy__cap_8","uri":"capability://text.generation.language.documentation.generation.from.code.analysis","name":"documentation generation from code analysis","description":"Clippy analyzes code structure, function signatures, and implementation details to automatically generate documentation including docstrings, README sections, and API documentation. The agent infers function purpose from implementation logic and variable names, generates examples based on code patterns, and creates documentation that stays synchronized with code changes.","intents":["Generate docstrings for functions that lack documentation","Create API documentation from code signatures and implementations","Generate README sections describing module functionality"],"best_for":["teams with poor documentation coverage who want automated documentation generation","open-source projects needing comprehensive API docs","developers who want to improve code documentation without manual effort"],"limitations":["Documentation quality depends on code clarity — poorly-written code produces poor documentation","Cannot infer high-level design intent from code alone; architectural documentation must be provided separately","Generated examples may not cover all use cases or edge cases","Documentation must be manually reviewed and edited for accuracy and completeness"],"requires":["Python 3.8+","Code to document (functions, classes, modules)","LLM with code understanding capabilities"],"input_types":["code (functions, classes, modules to document)"],"output_types":["text (docstrings in various formats: Google, NumPy, Sphinx)","markdown (API documentation, README sections)","code examples"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","LLM API access (OpenAI, Anthropic, or compatible endpoint)","Task description or user requirement as text input","Local access to full codebase files","LLM API with sufficient context window (8k+ tokens recommended)","Execution environment (local interpreter or containerized runtime)","Test framework or error output mechanism to validate fixes","LLM API with code understanding capabilities","Test framework (pytest, unittest, or compatible)","Function signatures and docstrings or specification text"],"failure_modes":["Planning quality depends on clarity of initial requirements — vague specifications produce fragmented task lists","No persistent state between planning and execution — task context must be re-injected for each step","Limited to sequential planning; cannot generate parallel task graphs or dependency DAGs","Indexing overhead scales with codebase size — very large repos (>100k LOC) may cause latency","Cannot infer implicit architectural patterns not visible in code (e.g., unwritten design principles)","Requires codebase to be locally accessible; does not support remote repository indexing","Debugging loop can enter infinite cycles on ambiguous errors — requires manual intervention after 3-5 failed attempts","Error messages must be parseable (structured stack traces work better than cryptic compiler errors)","Cannot debug logical errors that don't produce exceptions — only catches runtime failures and test assertion failures","Execution environment must be sandboxed or trusted; running arbitrary generated code poses security risks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:02.371Z","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=clippy","compare_url":"https://unfragile.ai/compare?artifact=clippy"}},"signature":"VhajbGnsxZVLzv6QQqT3qOqfekbvi5BU0orquO0SzkE9xUKxGgBPlDMMJmzSBdETcfKRwPDppl3USFBB+vqPBA==","signedAt":"2026-07-08T14:32:24.175Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/clippy","artifact":"https://unfragile.ai/clippy","verify":"https://unfragile.ai/api/v1/verify?slug=clippy","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"}}