{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_docudo","slug":"docudo","name":"DocuDo","type":"product","url":"https://docudo.xyz","page_url":"https://unfragile.ai/docudo","categories":["documentation"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_docudo__cap_0","uri":"capability://text.generation.language.context.aware.readme.generation.from.code.snippets","name":"context-aware readme generation from code snippets","description":"Analyzes provided code snippets, project metadata, and structural hints to generate README files with appropriate sections (installation, usage, API overview, contributing guidelines). Uses prompt engineering to extract semantic intent from code patterns and project structure, then templates the output into markdown with context-aware section ordering. The system infers documentation depth based on input complexity rather than applying one-size-fits-all templates.","intents":["I want to quickly generate a README for my new open-source project without writing from scratch","I need to create baseline documentation that covers installation, usage, and contribution guidelines in minutes","I want AI to infer the right sections and structure based on my actual codebase rather than forcing me into a rigid template"],"best_for":["solo developers launching open-source projects on tight timelines","small teams needing rapid documentation iteration cycles","indie developers who lack dedicated technical writers"],"limitations":["Struggles with domain-specific terminology and architectural nuance — outputs generic explanations for complex systems","Cannot infer undocumented design decisions or non-obvious code patterns without explicit hints","Generates boilerplate that often requires 30-50% human refinement for production-quality docs","No awareness of project-specific conventions or style guides unless explicitly provided"],"requires":["Code snippets or file paths (minimum 100 lines recommended for context)","Project name and basic description","Internet connection for API calls to underlying LLM"],"input_types":["code snippets (Python, JavaScript, Go, etc.)","project metadata (name, description, version)","structured hints (tech stack, dependencies)"],"output_types":["markdown (README.md format)","structured sections (installation, usage, API, contributing)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_1","uri":"capability://text.generation.language.api.documentation.generation.from.code.signatures","name":"api documentation generation from code signatures","description":"Extracts function signatures, parameter types, return types, and docstring hints from source code to auto-generate structured API documentation in markdown or HTML format. Parses language-specific syntax (Python docstrings, JSDoc, Go comments) to populate parameter descriptions, type information, and usage examples. Applies heuristic-based example generation for common patterns (CRUD operations, authentication flows) when explicit examples are absent.","intents":["I want to generate API documentation from my function signatures without manually writing each parameter description","I need to keep API docs in sync with code changes by regenerating from source","I want to create interactive API reference docs with type hints and example calls"],"best_for":["backend developers building REST or gRPC APIs who need rapid doc generation","library maintainers wanting to auto-generate reference documentation from code","teams using code-first API design patterns"],"limitations":["Relies on existing docstrings or comments — generates placeholder descriptions if source lacks documentation","Cannot infer business logic or domain-specific constraints from signatures alone","Example generation is pattern-based and may not reflect real-world usage for complex APIs","Limited support for advanced type systems (generics, union types, conditional types) depending on language"],"requires":["Source code with function/method signatures (Python, JavaScript, Go, Java, etc.)","Optional: existing docstrings or comments for richer output","Language parser compatible with the codebase"],"input_types":["source code files","function signatures with type annotations","docstrings or inline comments"],"output_types":["markdown API reference","HTML documentation","structured JSON schema"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_2","uri":"capability://text.generation.language.setup.and.installation.guide.generation","name":"setup and installation guide generation","description":"Analyzes project dependencies, build configuration files (package.json, requirements.txt, go.mod, Dockerfile), and platform-specific requirements to generate step-by-step installation guides. Detects the target audience (developers vs end-users) and generates appropriate complexity levels. Includes platform-specific instructions (macOS, Linux, Windows) and handles common gotchas (version conflicts, environment variables, prerequisite tools).","intents":["I need to create installation instructions that cover multiple operating systems and dependency managers","I want to generate setup guides that anticipate common errors and platform-specific issues","I need to create quick-start guides for developers unfamiliar with my project's tech stack"],"best_for":["open-source maintainers needing multi-platform installation docs","teams with diverse developer environments (Windows, macOS, Linux)","projects with complex dependency chains or version constraints"],"limitations":["Cannot detect environment-specific issues that aren't encoded in dependency files","May miss platform-specific gotchas for niche operating systems or architectures","Assumes standard package managers — struggles with custom build systems or monorepos","Generated instructions may be outdated if dependencies change frequently"],"requires":["Dependency manifest files (package.json, requirements.txt, go.mod, Gemfile, etc.)","Build configuration (Dockerfile, Makefile, setup.py, etc.)","Optional: known platform-specific requirements or constraints"],"input_types":["dependency files","build configuration","project metadata (supported platforms, minimum versions)"],"output_types":["markdown installation guide","platform-specific sections","troubleshooting FAQ"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_3","uri":"capability://code.generation.editing.code.example.and.usage.pattern.generation","name":"code example and usage pattern generation","description":"Generates practical code examples and usage patterns based on function signatures, class definitions, and inferred use cases. Uses prompt engineering to create realistic, runnable examples that demonstrate common workflows (authentication, CRUD operations, error handling). Adapts examples to match the detected language and framework conventions, including proper imports, error handling, and best practices.","intents":["I want to generate realistic code examples that show how to use my library or API","I need to create usage patterns that demonstrate best practices and common workflows","I want examples that are immediately runnable and follow language-specific conventions"],"best_for":["library and SDK maintainers needing comprehensive usage examples","API developers wanting to demonstrate integration patterns","teams building developer-facing products with complex APIs"],"limitations":["Generated examples may not reflect real-world performance characteristics or edge cases","Cannot infer domain-specific workflows without explicit context or examples","Examples may contain subtle bugs or anti-patterns if source code itself has issues","Limited to common patterns — niche or advanced use cases require manual authoring"],"requires":["Function or class definitions with type signatures","Optional: existing code examples to learn from","Language and framework context"],"input_types":["source code (functions, classes, modules)","type signatures and annotations","existing example code (optional)"],"output_types":["code snippets (Python, JavaScript, Go, etc.)","runnable examples with imports and error handling","usage pattern documentation"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_4","uri":"capability://text.generation.language.contributing.guidelines.and.community.documentation.generation","name":"contributing guidelines and community documentation generation","description":"Generates CONTRIBUTING.md, CODE_OF_CONDUCT.md, and community guidelines based on project type, license, and development practices. Uses templates adapted to the detected project maturity and community size. Includes sections for development setup, testing requirements, pull request process, and code style guidelines. Can infer some conventions from existing code (linting config, test structure) to make guidelines more specific.","intents":["I want to create contributing guidelines that match my project's actual development workflow","I need to establish clear expectations for pull requests, code review, and testing","I want to generate a code of conduct and community standards without starting from scratch"],"best_for":["open-source maintainers launching community projects","teams wanting to formalize contribution processes","projects seeking to attract external contributors"],"limitations":["Generated guidelines are generic templates — require customization for project-specific workflows","Cannot capture unwritten cultural norms or team-specific practices","May not align with existing community expectations if project already has contributors","Requires manual updates as project governance evolves"],"requires":["Project type and maturity level","License information","Optional: existing development practices or style guides"],"input_types":["project metadata","license type","development configuration (linting, testing setup)"],"output_types":["markdown (CONTRIBUTING.md, CODE_OF_CONDUCT.md)","structured guidelines with sections"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_5","uri":"capability://planning.reasoning.documentation.structure.and.outline.generation","name":"documentation structure and outline generation","description":"Analyzes project scope, feature set, and complexity to generate a hierarchical documentation outline with recommended sections, subsections, and content priorities. Uses heuristics based on project type (library, framework, tool, service) to suggest documentation structure (getting started, core concepts, API reference, examples, troubleshooting, FAQ). Adapts outline depth based on detected project complexity and target audience.","intents":["I want to know what sections my documentation should include before writing","I need to organize documentation for a complex project with multiple audiences","I want to ensure I'm not missing important documentation topics"],"best_for":["documentation planners and technical writers organizing large projects","teams building comprehensive documentation from scratch","projects with multiple audiences (developers, end-users, operators)"],"limitations":["Generated outlines are based on common patterns — may not fit unique project structures","Cannot prioritize sections based on actual user needs without usage data","Suggests generic section names that require customization for domain specificity","May over-suggest documentation for simple projects or under-suggest for complex ones"],"requires":["Project description and scope","Feature list or capability overview","Target audience information"],"input_types":["project metadata","feature descriptions","audience information"],"output_types":["hierarchical outline (markdown or JSON)","section recommendations with descriptions","content priority suggestions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_6","uri":"capability://text.generation.language.changelog.and.release.notes.generation","name":"changelog and release notes generation","description":"Generates structured changelog and release notes from git commit history, pull request titles, and version tags. Parses conventional commit messages (feat:, fix:, breaking:) to categorize changes automatically. Groups commits by type (features, bug fixes, breaking changes, documentation) and generates human-readable summaries. Can infer semantic versioning implications from commit types.","intents":["I want to generate release notes automatically from my commit history without manual summarization","I need to create changelogs that categorize changes by type (features, fixes, breaking changes)","I want to track what changed between versions without manually reviewing every commit"],"best_for":["maintainers of frequently-released projects needing rapid release note generation","teams using conventional commits or semantic versioning","open-source projects with active release cycles"],"limitations":["Depends on well-formatted commit messages — poor commit hygiene produces poor release notes","Cannot infer user-facing impact from technical commit descriptions","May miss important changes if commits lack proper conventional commit prefixes","Requires git history access and proper tagging discipline"],"requires":["Git repository with commit history","Version tags (semantic versioning recommended)","Conventional commit messages (feat:, fix:, etc.) for best results"],"input_types":["git commit history","version tags","pull request titles (optional)"],"output_types":["markdown changelog","categorized release notes","structured change summaries"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_7","uri":"capability://text.generation.language.troubleshooting.and.faq.generation","name":"troubleshooting and faq generation","description":"Generates troubleshooting guides and FAQ sections by analyzing common error messages, edge cases, and known limitations in code. Uses pattern matching to identify error handling paths and exception types, then generates solutions based on error context. Infers FAQ topics from code complexity, feature interactions, and common integration patterns. Adapts explanations to different expertise levels.","intents":["I want to create a troubleshooting guide that addresses common errors users encounter","I need to generate FAQ content based on my code's error handling and edge cases","I want to anticipate user questions and provide solutions before they ask"],"best_for":["library and tool maintainers wanting to reduce support burden","teams with complex APIs or configuration-heavy systems","projects with known gotchas or common integration issues"],"limitations":["Cannot infer user-facing problems from code alone — requires actual error reports or usage data","Generated solutions may be technically correct but not address root causes","Struggles with domain-specific troubleshooting that requires deep contextual knowledge","May miss edge cases that aren't represented in code error handling"],"requires":["Source code with error handling and exception types","Optional: known issues or bug reports","Optional: user feedback or support tickets"],"input_types":["source code (error handling, exceptions)","error messages and stack traces","known issues or limitations"],"output_types":["markdown troubleshooting guide","FAQ sections with Q&A format","error-specific solutions"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_8","uri":"capability://text.generation.language.multi.language.documentation.generation","name":"multi-language documentation generation","description":"Generates documentation in multiple languages (English, Spanish, French, German, Chinese, Japanese, etc.) from a single source document or codebase. Uses translation APIs combined with localization-aware formatting to adapt documentation for different regions. Handles code examples, technical terms, and formatting conventions appropriately for each language. Maintains consistency across language versions through shared terminology databases.","intents":["I want to create documentation in multiple languages without manually translating each section","I need to localize documentation for international audiences while maintaining technical accuracy","I want to ensure code examples and technical terms are properly adapted for each language"],"best_for":["open-source projects targeting international developers","companies building products for global markets","teams lacking multilingual technical writers"],"limitations":["Machine translation may lose nuance or introduce technical inaccuracies","Requires manual review by native speakers for production-quality localization","Cultural and regional conventions may not be automatically adapted","Maintaining consistency across language versions requires additional tooling"],"requires":["Source documentation in English or primary language","Target language specifications","Optional: terminology glossary for consistent translation"],"input_types":["markdown documentation","source code with comments","terminology glossaries"],"output_types":["localized markdown documentation","language-specific formatting","translated code comments"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_docudo__cap_9","uri":"capability://text.generation.language.architecture.and.system.design.documentation.generation","name":"architecture and system design documentation generation","description":"Generates architecture documentation, system design diagrams, and component interaction descriptions from code structure analysis and metadata. Parses module dependencies, class hierarchies, and service boundaries to infer system architecture. Generates narrative descriptions of design patterns, data flow, and component responsibilities. Can create ASCII diagrams or suggest diagram structures for visualization tools.","intents":["I want to document my system architecture without manually creating diagrams and descriptions","I need to explain how components interact and what design patterns are used","I want to generate architecture documentation that stays in sync with code structure"],"best_for":["teams building complex systems needing architecture documentation","maintainers of large codebases wanting to document design decisions","onboarding new team members who need to understand system structure"],"limitations":["Infers architecture from code structure — cannot capture design intent or strategic decisions","Struggles with distributed systems or microservices without explicit service definitions","Generated diagrams are text-based or structural — lack visual polish of hand-drawn diagrams","Cannot explain why certain architectural choices were made without explicit documentation"],"requires":["Source code with clear module/package structure","Optional: architecture decision records or design documents","Optional: service definitions or deployment configuration"],"input_types":["source code structure","module dependencies","class hierarchies and interfaces","configuration files"],"output_types":["markdown architecture documentation","ASCII or text-based diagrams","component interaction descriptions","data flow narratives"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Code snippets or file paths (minimum 100 lines recommended for context)","Project name and basic description","Internet connection for API calls to underlying LLM","Source code with function/method signatures (Python, JavaScript, Go, Java, etc.)","Optional: existing docstrings or comments for richer output","Language parser compatible with the codebase","Dependency manifest files (package.json, requirements.txt, go.mod, Gemfile, etc.)","Build configuration (Dockerfile, Makefile, setup.py, etc.)","Optional: known platform-specific requirements or constraints","Function or class definitions with type signatures"],"failure_modes":["Struggles with domain-specific terminology and architectural nuance — outputs generic explanations for complex systems","Cannot infer undocumented design decisions or non-obvious code patterns without explicit hints","Generates boilerplate that often requires 30-50% human refinement for production-quality docs","No awareness of project-specific conventions or style guides unless explicitly provided","Relies on existing docstrings or comments — generates placeholder descriptions if source lacks documentation","Cannot infer business logic or domain-specific constraints from signatures alone","Example generation is pattern-based and may not reflect real-world usage for complex APIs","Limited support for advanced type systems (generics, union types, conditional types) depending on language","Cannot detect environment-specific issues that aren't encoded in dependency files","May miss platform-specific gotchas for niche operating systems or architectures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.283Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=docudo","compare_url":"https://unfragile.ai/compare?artifact=docudo"}},"signature":"hpmGnjkMX5uW8RqGDlh6PEjBSIYxNzpTFvWRD4HhKqvPv2lfJjIsh0PhEi6J2ey5j/Xh3r5JIqyABdbJUccNBA==","signedAt":"2026-06-22T21:14:46.515Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/docudo","artifact":"https://unfragile.ai/docudo","verify":"https://unfragile.ai/api/v1/verify?slug=docudo","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"}}