{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-tusk","slug":"tusk","name":"Tusk","type":"agent","url":"https://usetusk.ai/","page_url":"https://unfragile.ai/tusk","categories":["automation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-tusk__cap_0","uri":"capability://code.generation.editing.autonomous.code.generation.and.implementation","name":"autonomous code generation and implementation","description":"Tusk generates code implementations by analyzing requirements and context, then automatically commits changes to version control. The system likely uses LLM-based code synthesis with repository context awareness to understand existing patterns and conventions, enabling it to produce code that integrates seamlessly with the existing codebase rather than generating isolated snippets.","intents":["I need an AI to write feature implementations without manual coding","I want to delegate routine coding tasks to an automated system","I need code generated that follows my project's existing patterns and style"],"best_for":["development teams with well-structured codebases and clear conventions","projects with high-velocity feature development where manual coding is a bottleneck","teams comfortable with AI-generated code in their primary workflow"],"limitations":["Likely struggles with complex architectural decisions requiring domain expertise","May generate code that passes tests but violates non-obvious project constraints","No visibility into how it selects between multiple valid implementation approaches","Requires sufficient codebase context to learn patterns — small or inconsistent projects may see lower quality"],"requires":["Git repository with commit access","Codebase with sufficient examples for pattern learning","API credentials for underlying LLM provider"],"input_types":["natural language requirements","code context from repository","existing codebase files"],"output_types":["source code files","git commits"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_1","uri":"capability://automation.workflow.automated.test.execution.and.validation","name":"automated test execution and validation","description":"Tusk runs test suites against generated code to validate correctness before committing. This likely involves invoking the project's native test runner (pytest, Jest, etc.) in the repository environment, parsing test output, and using results as feedback to either accept or reject generated code. The system may iterate on code generation if tests fail, creating a feedback loop.","intents":["I want generated code to be validated automatically before it reaches main","I need confidence that AI-generated code doesn't break existing functionality","I want to catch test failures immediately rather than in CI/CD"],"best_for":["teams with comprehensive test coverage (>70%)","projects where test execution is fast (<5 minutes)","organizations that require automated validation before human review"],"limitations":["Only validates against existing test suite — cannot catch issues not covered by tests","Test execution time becomes a bottleneck for large test suites","May fail on tests with flaky behavior or environment-dependent assertions","Cannot validate non-functional requirements (performance, security, accessibility)"],"requires":["Functional test suite in the repository","Test runner executable in the project environment","Ability to execute arbitrary commands in the repository context"],"input_types":["generated code","test suite files","project configuration"],"output_types":["test results (pass/fail)","test output logs","validation decision (accept/reject)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_2","uri":"capability://memory.knowledge.repository.context.extraction.and.codebase.indexing","name":"repository context extraction and codebase indexing","description":"Tusk analyzes the target repository to understand its structure, patterns, conventions, and existing implementations. This likely involves parsing project files, identifying language-specific patterns, extracting code style conventions, and building an internal representation of the codebase that can be used to inform code generation. The system may use AST parsing, semantic analysis, or embedding-based similarity to identify relevant code examples.","intents":["I want the AI to understand my project's architecture before generating code","I need generated code to follow my team's coding conventions automatically","I want the AI to reuse existing patterns and utilities from my codebase"],"best_for":["mature projects with established patterns and conventions","codebases with consistent style and architecture","teams that want generated code to feel native to their project"],"limitations":["Indexing time scales with repository size — very large monorepos may be slow","Cannot understand implicit conventions that aren't reflected in code","May misidentify patterns if codebase has inconsistent style","Requires sufficient code examples to learn from — small projects may not provide enough signal"],"requires":["Access to full repository files","Language-specific parsers for the project's primary languages","Sufficient disk space for codebase indexing"],"input_types":["repository file structure","source code files","configuration files"],"output_types":["codebase context representation","pattern library","style guide extraction"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_3","uri":"capability://automation.workflow.git.integration.and.automated.commit.management","name":"git integration and automated commit management","description":"Tusk integrates with git to create commits for generated code, likely using git command-line or library bindings to stage changes, create commits with descriptive messages, and push to branches. The system may handle branch creation, commit message generation based on code changes, and conflict resolution. This enables a fully automated workflow from code generation through version control.","intents":["I want generated code automatically committed with proper git history","I need the AI to create feature branches and manage git workflow","I want descriptive commit messages generated automatically for code changes"],"best_for":["teams using git-based workflows with branch protection rules","projects where audit trails and commit history are important","organizations comfortable with AI-generated commits in their repository"],"limitations":["Cannot handle complex merge conflicts — may fail on concurrent changes","Commit message generation may be generic or miss important context","Requires write access to repository — incompatible with read-only or fork-based workflows","No built-in support for commit signing or GPG verification"],"requires":["Git repository with write access","Git credentials configured (SSH key or token)","Git version 2.0+"],"input_types":["generated code files","change metadata","commit message context"],"output_types":["git commits","branch references","push confirmations"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_4","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.specific.patterns","name":"multi-language code generation with language-specific patterns","description":"Tusk generates code across multiple programming languages by understanding language-specific idioms, syntax, and conventions. The system likely uses language-specific parsers and code generators for each supported language, enabling it to produce idiomatic code rather than direct translations. This may involve separate LLM prompts or fine-tuning for each language, or a unified approach with language-aware context.","intents":["I need the AI to generate code in multiple languages used by my project","I want generated code to follow language-specific best practices","I need the AI to understand language-specific patterns and idioms"],"best_for":["polyglot projects with multiple programming languages","teams that want consistent code generation across their tech stack","organizations with language-specific conventions and patterns"],"limitations":["Quality may vary significantly across languages — likely better for popular languages","Language-specific edge cases and idioms may not be handled correctly","Requires language-specific test runners and validation for each language","Unknown which languages are supported — likely limited to mainstream languages"],"requires":["Language-specific parsers for supported languages","Language-specific test runners and build tools","Training data or fine-tuning for each supported language"],"input_types":["natural language requirements","target language specification","language-specific codebase context"],"output_types":["source code in target language","language-specific syntax"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_5","uri":"capability://automation.workflow.iterative.code.refinement.based.on.test.feedback","name":"iterative code refinement based on test feedback","description":"When generated code fails tests, Tusk likely analyzes test failures and automatically attempts to refine the code to fix issues. This creates a feedback loop where the system learns from test results and iterates on implementations. The approach may involve parsing test output, identifying failure reasons, and using that information to guide subsequent code generation attempts.","intents":["I want the AI to fix code automatically when tests fail","I need the AI to learn from test failures and improve implementations","I want to reduce manual debugging of AI-generated code"],"best_for":["projects with clear, deterministic test failures","teams comfortable with iterative AI-driven development","codebases where test output clearly indicates failure reasons"],"limitations":["May get stuck in infinite loops on ambiguous test failures","Cannot fix issues that require architectural changes","Iteration count is likely limited to prevent runaway execution","May produce increasingly complex code trying to satisfy conflicting test constraints"],"requires":["Clear, deterministic test suite","Test output that indicates failure reasons","Iteration limit configuration"],"input_types":["test failure output","generated code","test suite"],"output_types":["refined code","iteration history","final test results"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_6","uri":"capability://planning.reasoning.natural.language.requirement.interpretation.and.task.decomposition","name":"natural language requirement interpretation and task decomposition","description":"Tusk converts natural language requirements into actionable code generation tasks by parsing intent, identifying scope, and potentially decomposing complex requirements into smaller implementation steps. This likely involves prompt engineering, structured parsing of requirements, and mapping requirements to codebase context to determine what needs to be implemented.","intents":["I want to describe features in plain English and have them implemented","I need the AI to understand complex requirements and break them into code","I want to avoid writing detailed technical specifications"],"best_for":["teams with clear, well-articulated requirements","projects where requirements map clearly to code changes","organizations that want to reduce specification overhead"],"limitations":["Ambiguous or vague requirements may result in incorrect implementations","Cannot handle requirements that need clarification or stakeholder discussion","May misinterpret domain-specific terminology","Complex requirements spanning multiple systems may be decomposed incorrectly"],"requires":["Clear natural language requirements","Sufficient codebase context for interpretation"],"input_types":["natural language requirements","requirement metadata"],"output_types":["interpreted requirements","task decomposition","implementation plan"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-tusk__cap_7","uri":"capability://automation.workflow.pull.request.creation.and.code.review.integration","name":"pull request creation and code review integration","description":"Tusk likely creates pull requests for generated code rather than committing directly to main, enabling human review before merge. This may involve creating branches, generating PR descriptions, and integrating with code review platforms. The system may also handle review feedback, though this is uncertain from available information.","intents":["I want generated code to go through code review before merging","I need pull requests created automatically with descriptive information","I want to maintain human oversight over AI-generated code"],"best_for":["teams that require code review for all changes","organizations with governance requirements for automated changes","projects where human oversight is non-negotiable"],"limitations":["PR descriptions may be generic or miss important context","Cannot handle review feedback automatically — requires manual iteration","May create excessive PRs if requirements are decomposed too granularly","Unknown if system integrates with code review tools or just creates raw PRs"],"requires":["Git repository with PR support (GitHub, GitLab, Bitbucket, etc.)","API access to PR creation endpoints","Branch creation permissions"],"input_types":["generated code","change metadata","PR description context"],"output_types":["pull request","PR description","branch reference"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Git repository with commit access","Codebase with sufficient examples for pattern learning","API credentials for underlying LLM provider","Functional test suite in the repository","Test runner executable in the project environment","Ability to execute arbitrary commands in the repository context","Access to full repository files","Language-specific parsers for the project's primary languages","Sufficient disk space for codebase indexing","Git repository with write access"],"failure_modes":["Likely struggles with complex architectural decisions requiring domain expertise","May generate code that passes tests but violates non-obvious project constraints","No visibility into how it selects between multiple valid implementation approaches","Requires sufficient codebase context to learn patterns — small or inconsistent projects may see lower quality","Only validates against existing test suite — cannot catch issues not covered by tests","Test execution time becomes a bottleneck for large test suites","May fail on tests with flaky behavior or environment-dependent assertions","Cannot validate non-functional requirements (performance, security, accessibility)","Indexing time scales with repository size — very large monorepos may be slow","Cannot understand implicit conventions that aren't reflected in code","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.9,"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":"2026-05-24T12:16:21.012Z","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=tusk","compare_url":"https://unfragile.ai/compare?artifact=tusk"}},"signature":"vSFPMfuvnsJ3t5KLXqH6Bq15SW6uogmqeLFoMa5yuiz+wN9wsTjlVFqYM3xRn4TO72CmUUUfA3nqOfVvlykFDQ==","signedAt":"2026-06-15T11:30:47.431Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tusk","artifact":"https://unfragile.ai/tusk","verify":"https://unfragile.ai/api/v1/verify?slug=tusk","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"}}