{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"augment-code","slug":"augment-code","name":"Augment Code","type":"agent","url":"https://www.augmentcode.com","page_url":"https://unfragile.ai/augment-code","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"augment-code__cap_0","uri":"capability://planning.reasoning.codebase.aware.task.decomposition.with.user.editable.plans","name":"codebase-aware task decomposition with user-editable plans","description":"Analyzes user requests against the entire codebase using semantic filtering (reducing 4,456+ sources to 682 relevant ones) and generates numbered, actionable task lists before any code execution. Users can add, skip, or modify steps before the agent proceeds. This plan-first approach enables structured multi-file changes while maintaining human oversight at the decision point, not just execution point.","intents":["I want the agent to show me its plan before touching my code so I can catch issues early","I need to implement a feature across 5 files but want to control the order of operations","I want to understand the agent's reasoning before it starts refactoring my codebase"],"best_for":["teams requiring explicit approval workflows before code changes","developers working on large, complex codebases with interdependencies","organizations with strict change management policies"],"limitations":["Planning phase adds latency before execution begins — no real-time streaming of changes","Semantic filtering mechanism is proprietary and not transparent — users cannot tune relevance thresholds","Maximum task depth and complexity limits unknown — unclear how agent handles deeply nested dependencies or circular references"],"requires":["VS Code 1.80+ or JetBrains IDE (2023.1+) or Augment CLI","Active internet connection (cloud-hosted agent)","Minimum 40,000 credits/month (Indie tier)"],"input_types":["natural language request","codebase context (automatically indexed)"],"output_types":["numbered task list (text)","estimated scope and file changes (structured)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_1","uri":"capability://automation.workflow.checkpoint.based.reversible.code.execution.with.step.by.step.approval","name":"checkpoint-based reversible code execution with step-by-step approval","description":"Executes planned tasks sequentially while creating checkpoints at each step, allowing users to accept changes, revert to any prior checkpoint, or redirect the agent mid-task without losing work. Each checkpoint captures file state and execution context, enabling granular rollback without manual version control. Integrates with Git for version tracking but provides finer-grained undo than traditional commits.","intents":["I want to revert just the last file change without undoing the entire task","I need to pause the agent mid-task, review what it did, and then redirect it","I want to accept some changes but reject others before committing to version control"],"best_for":["teams iterating on complex refactors where partial rollback is essential","developers who want fine-grained control over multi-file changes","organizations using Git but needing sub-commit-level granularity"],"limitations":["Checkpoint system requires agent to pause between steps — cannot stream continuous changes","Checkpoint storage mechanism unclear — unknown whether checkpoints persist across sessions or are ephemeral","No automatic error recovery — if a step fails (compilation error, test failure), agent escalates to user rather than attempting retry logic"],"requires":["VS Code or JetBrains IDE with Augment plugin","Git repository initialized (implied by checkpoint integration)","Active session with Augment Code agent"],"input_types":["user approval/rejection at checkpoint","redirect instructions (natural language)"],"output_types":["file changes (code)","checkpoint state (internal)","execution log (text)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_10","uri":"capability://memory.knowledge.workspace.rules.for.persistent.user.curated.memory","name":"workspace rules for persistent, user-curated memory","description":"Allows users to create and maintain workspace Rules — persistent, user-approved memory items that capture project-specific patterns, conventions, and decisions. Rules are stored in the workspace and applied across all agent sessions, enabling the agent to learn from user feedback without automatic memory accumulation. Users explicitly approve, edit, or discard each memory before it's saved.","intents":["I want to teach the agent about my team's coding standards once and have it remember them","I need to document architectural decisions that the agent should follow","I want to prevent the agent from making the same mistake twice by capturing the lesson as a Rule"],"best_for":["teams with consistent conventions and patterns","organizations that want explicit control over agent learning","developers who prefer curated memory over automatic learning"],"limitations":["Memory curation requires manual user approval — does not scale automatically to very large codebases","Rule format and schema unknown — unclear how users define Rules or what expressiveness is supported","No automatic Rule suggestion — agent surfaces memories for approval but doesn't automatically generate Rules","Rule application mechanism undisclosed — unclear if Rules are applied via prompt injection, fine-tuning, or other means"],"requires":["Active Augment Code workspace","User approval workflow for each Rule"],"input_types":["memory item (text/structured)","user approval/edit/discard action"],"output_types":["workspace Rule (persistent, structured)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_11","uri":"capability://automation.workflow.credit.based.consumption.model.with.transparent.pricing","name":"credit-based consumption model with transparent pricing","description":"Uses a credit-based consumption model where tasks consume credits based on complexity and resource usage. Credits are purchased in tiers (Indie: 40k/month, Standard: 130k/month, Max: 450k/month) with auto top-up at $15 per 24k credits. Credits are consumed by agent execution and code review tasks. The exact credit-to-token mapping and per-task cost estimation are not published.","intents":["I want to understand how much my agent usage costs","I need to estimate credits required for a specific task","I want to optimize my subscription tier based on usage patterns"],"best_for":["teams with predictable usage patterns","organizations that want consumption-based pricing instead of per-seat licensing","developers who want to avoid overcommitting to fixed plans"],"limitations":["Credit-to-token conversion rate not published — users cannot calculate exact costs","Per-task cost estimation unavailable — unclear if a 5-file feature costs 100 or 10,000 credits","Planning phase credit consumption unknown — unclear if planning consumes credits or only execution","No cost forecasting tools — users cannot predict monthly spend based on task volume","Auto top-up at fixed rate ($15 per 24k credits) — no volume discounts or negotiated rates mentioned"],"requires":["Active Augment Code subscription","Payment method for auto top-up"],"input_types":["subscription tier selection","task execution"],"output_types":["credit consumption (numeric)","billing statement (text/structured)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_12","uri":"capability://tool.use.integration.ide.integration.with.vs.code.and.jetbrains.plugins","name":"ide integration with vs code and jetbrains plugins","description":"Provides native plugins for VS Code and JetBrains IDEs (IntelliJ, PyCharm, etc.) that embed the agent directly into the development environment. Users interact with the agent through IDE UI elements (sidebar, inline suggestions, context menus) without leaving their editor. The plugin architecture maintains local IDE state while communicating with the cloud-hosted agent.","intents":["I want to use the agent without switching away from my IDE","I need inline suggestions and code completions integrated into my editor","I want to see agent activity and checkpoints in my IDE UI"],"best_for":["developers who spend most time in VS Code or JetBrains IDEs","teams using JetBrains suite (IntelliJ, PyCharm, WebStorm, etc.)","developers who want minimal context-switching"],"limitations":["IDE plugin version compatibility unknown — unclear which IDE versions are supported","Plugin installation and update mechanism undisclosed","Offline mode not mentioned — plugin likely requires constant cloud connectivity","No mention of Vim, Emacs, or other editor support — limited to VS Code and JetBrains"],"requires":["VS Code 1.80+ or JetBrains IDE (2023.1+ estimated)","Augment Code plugin installed from marketplace","Active internet connection"],"input_types":["user interaction via IDE UI (clicks, text input)"],"output_types":["code suggestions (inline)","task plans (sidebar)","checkpoints (UI elements)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_13","uri":"capability://tool.use.integration.cli.based.agent.for.terminal.first.workflows","name":"cli-based agent for terminal-first workflows","description":"Provides Augment CLI, a terminal-based interface to the agent that uses the same Context Engine and planning logic as the IDE plugins. Enables developers who prefer terminal workflows to use the agent without opening an IDE. CLI supports piping, scripting, and CI/CD integration.","intents":["I want to use the agent from the terminal without opening an IDE","I need to integrate the agent into my CI/CD pipeline","I want to script agent tasks for batch processing"],"best_for":["developers with terminal-first workflows","CI/CD pipelines that need agent capabilities","teams automating code generation or refactoring tasks"],"limitations":["CLI feature set and command syntax unknown","CI/CD integration details undisclosed — unclear if CLI can be used in GitHub Actions, GitLab CI, etc.","Scripting capabilities unknown — unclear if CLI supports batch task files or only interactive commands","No mention of CLI output formats (JSON, YAML, etc.) for parsing"],"requires":["Augment CLI installed (installation method unknown)","API key or authentication token for CLI","Terminal/shell environment"],"input_types":["CLI commands (text)","task specifications (text/structured)"],"output_types":["code changes (files)","execution logs (text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_14","uri":"capability://safety.moderation.enterprise.security.and.compliance.features","name":"enterprise security and compliance features","description":"Provides enterprise-grade security features including SOC 2 Type II compliance, CMEK (Customer-Managed Encryption Keys), ISO 42001 compliance, SIEM integration, data residency options, granular access controls, comprehensive audit trails, and enterprise SSO (OIDC, SCIM). These features are available on Enterprise tier and ensure data protection, regulatory compliance, and organizational control.","intents":["I need to ensure my codebase data is encrypted with my own keys","I need to comply with SOC 2 or ISO 42001 regulations","I need to integrate Augment Code with my organization's identity provider","I need detailed audit trails for compliance and security monitoring"],"best_for":["enterprises with strict data protection requirements","organizations subject to SOC 2, ISO 42001, or similar compliance frameworks","teams using SIEM systems for security monitoring","organizations with centralized identity management (OIDC, SCIM)"],"limitations":["Enterprise tier pricing not published — unclear cost vs. Standard/Max tiers","Data residency options scope unknown — unclear which regions are supported","SIEM integration details undisclosed — unclear which SIEM platforms are supported","Audit trail retention period unknown","No mention of data deletion policies or GDPR compliance"],"requires":["Enterprise tier subscription","CMEK setup (if using customer-managed encryption)","SIEM system (if using SIEM integration)","Identity provider supporting OIDC or SCIM (if using enterprise SSO)"],"input_types":["security configuration (setup)"],"output_types":["audit logs (structured)","compliance reports (text/structured)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_2","uri":"capability://memory.knowledge.semantic.codebase.context.filtering.and.live.understanding","name":"semantic codebase context filtering and live understanding","description":"Maintains a 'live understanding' of the entire codebase by indexing code, dependencies, architecture, and history, then performs semantic filtering to surface only relevant context (reducing 4,456+ sources to 682 relevant ones per example). Uses a proprietary Context Engine to determine relevance without exposing the filtering mechanism. Stores user-approved memories as workspace Rules that persist across sessions.","intents":["I want the agent to understand my entire codebase without me explaining the architecture each time","I need the agent to know about my project's patterns and conventions automatically","I want to teach the agent about my team's coding standards once and have it remember them"],"best_for":["large codebases (100k+ LOC) where full context is infeasible","teams with consistent architectural patterns and conventions","developers who want persistent, session-spanning context"],"limitations":["Semantic filtering mechanism is proprietary — users cannot inspect or tune relevance thresholds","Context window size unknown — unclear how much filtered context is actually passed to the LLM per request","Memory curation requires explicit user approval — does not scale automatically to very large codebases without manual overhead","Underperforms on 'Best Practice' metric (-4.4 vs. human baseline on Elasticsearch benchmark) — suggests struggles with project-specific conventions despite claims of pattern learning"],"requires":["Codebase indexed by Augment (automatic on first connection)","Active workspace Rules (optional but recommended)","Cloud connectivity (context engine is cloud-hosted)"],"input_types":["codebase files (auto-indexed)","user-approved memory items (text/structured)"],"output_types":["filtered context (passed to LLM internally)","workspace Rules (structured, persistent)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_3","uri":"capability://tool.use.integration.multi.model.llm.backend.with.transparent.model.selection","name":"multi-model llm backend with transparent model selection","description":"Supports multiple LLM backends (Claude Opus 4.5, Opus 4.6, Gemini 3.1 Pro) with user-selectable model configuration. The agent's planning and execution logic is model-agnostic, allowing users to choose models based on task complexity, cost, or latency requirements. Model selection mechanism and whether users can bring custom models remain undisclosed.","intents":["I want to use Claude Opus 4.6 for complex tasks but Gemini 3.1 Pro for simpler ones to save costs","I need to switch models mid-session if one is underperforming","I want to use a custom fine-tuned model for my domain-specific code"],"best_for":["teams with varying task complexity who want cost optimization","organizations evaluating multiple LLM providers","developers who want model flexibility without tool switching"],"limitations":["Model selection API/UI mechanism unknown — unclear if selection is per-task, per-session, or global","Custom model support not mentioned — users likely cannot bring their own models","Benchmark results (SWE-Bench Pro 51.80%) use Opus 4.5 — unclear if Opus 4.6 or Gemini 3.1 Pro performance differs","No published per-model cost breakdown — credit consumption rates for different models unknown"],"requires":["API keys or credentials for selected model (if required by provider)","Active Augment Code subscription with sufficient credits"],"input_types":["model selection preference (configuration)"],"output_types":["code, plans, and analysis (model-dependent quality)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_4","uri":"capability://tool.use.integration.terminal.command.execution.with.external.tool.invocation","name":"terminal command execution with external tool invocation","description":"Executes arbitrary terminal commands and invokes external tools (e.g., linters, test runners, build systems) as part of task execution. The agent can run commands, capture output, and use results to inform subsequent steps. Integrates with MCP (Model Context Protocol) for custom tool definitions, allowing teams to extend the agent with domain-specific tools.","intents":["I want the agent to run tests after making changes and fix failures automatically","I need the agent to invoke my custom build system or deployment scripts","I want to add my team's proprietary linting or analysis tools to the agent"],"best_for":["teams with complex build/test/deploy pipelines","developers who want the agent to validate changes automatically","organizations with custom tooling that needs agent integration"],"limitations":["Code execution sandboxing details unknown — unclear if terminal commands are restricted or have direct filesystem access","No explicit error handling for failed commands — agent escalates to user rather than attempting recovery","MCP integration mechanism undisclosed — unclear how custom tools are registered or what tool schema is required","Timeout behavior for long-running commands unknown","No mention of environment variable management or secrets handling"],"requires":["Terminal/shell access (local or remote)","Required tools installed in execution environment (e.g., Node.js, Python, Docker)","MCP server running (for custom tool integration)"],"input_types":["terminal command (text)","MCP tool schema (JSON)"],"output_types":["command output (text)","exit code (integer)","tool invocation results (structured)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_5","uri":"capability://tool.use.integration.github.integration.with.pr.review.and.multi.org.support","name":"github integration with pr review and multi-org support","description":"Integrates with GitHub to read repository context, create pull requests, and provide inline code review comments. Supports multi-organization repositories, allowing the agent to work across different GitHub orgs without reconfiguration. PR summaries and inline comments are generated by the agent, providing context-aware review feedback.","intents":["I want the agent to create a PR for the changes it made and summarize what it did","I need the agent to review PRs from my team and provide inline feedback","I want to use the agent across multiple GitHub organizations without switching credentials"],"best_for":["teams using GitHub for version control and code review","organizations with multiple GitHub orgs or repositories","developers who want automated PR creation and review"],"limitations":["GitLab, Bitbucket, and other VCS platforms not mentioned — GitHub-only integration","PR review capability is separate product (Augment Intent) — unclear if included in base subscription","Inline comment generation mechanism undisclosed — unclear if comments are rule-based or LLM-generated","Multi-org support scope unknown — unclear if there are limits on number of orgs or repositories"],"requires":["GitHub account with repository access","GitHub API token with appropriate permissions (repo, pull_request scopes)","Repository must be connected to Augment Code workspace"],"input_types":["GitHub repository context (auto-fetched)","PR creation request (implicit from task completion)"],"output_types":["pull request (GitHub PR object)","PR summary (text)","inline review comments (text)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_6","uri":"capability://tool.use.integration.slack.integration.for.async.notifications.and.team.collaboration","name":"slack integration for async notifications and team collaboration","description":"Sends task completion notifications, status updates, and approval requests to Slack channels. Enables asynchronous team collaboration by surfacing agent activity in team communication channels. Available on Standard tier and above, allowing teams to stay informed without constant IDE monitoring.","intents":["I want my team to be notified when the agent completes a task","I need to request approval from teammates via Slack before the agent proceeds","I want to see agent activity in our team Slack channel without checking the IDE"],"best_for":["distributed teams using Slack for communication","organizations wanting async approval workflows","teams that want centralized visibility into agent activity"],"limitations":["Slack integration available only on Standard tier and above — not included in Indie plan","Notification types and customization options unknown","No mention of Slack command integration (e.g., /augment commands) — likely one-way notifications only","Approval workflow via Slack not detailed — unclear if users can approve/reject tasks from Slack or must use IDE"],"requires":["Slack workspace with Augment Code app installed","Standard tier subscription or higher (130,000+ credits/month)","Slack channel configured for notifications"],"input_types":["task completion event (internal)","approval request (internal)"],"output_types":["Slack message (text/rich formatting)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_7","uri":"capability://code.generation.editing.feature.implementation.across.multi.file.codebases.with.dependency.awareness","name":"feature implementation across multi-file codebases with dependency awareness","description":"Implements complete features (e.g., OAuth flows, JWT token rotation) across multiple files while maintaining awareness of dependencies and cross-file references. The agent reads existing patterns, creates necessary files, modifies related code, and ensures consistency across the codebase. Uses codebase-aware context to understand existing architecture and apply similar patterns.","intents":["I want to implement OAuth with refresh tokens across my auth, session, and middleware files","I need to add a new API endpoint that requires changes to routes, controllers, and database models","I want the agent to implement a feature while following my existing code patterns"],"best_for":["teams implementing medium-to-large features requiring multi-file changes","developers who want the agent to understand and apply existing patterns","codebases with clear architectural layers (auth, models, routes, etc.)"],"limitations":["Underperforms on 'Best Practice' metric (-4.4 vs. human baseline) — suggests struggles with project-specific conventions despite pattern learning","Maximum feature complexity unknown — unclear how agent handles deeply nested dependencies or circular references","Pattern learning is implicit — no explicit mechanism to teach the agent about custom patterns beyond workspace Rules","No quantified latency for multi-file features — unclear if 5-file feature takes 10 seconds or 10 minutes"],"requires":["Codebase indexed by Augment (automatic)","Clear architectural structure (agent performs better with organized codebases)","Sufficient credits for multi-step task execution"],"input_types":["natural language feature request","codebase context (auto-indexed)"],"output_types":["new files (code)","modified files (code)","task plan (text)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_8","uri":"capability://code.generation.editing.bug.fixing.with.root.cause.analysis.and.test.driven.validation","name":"bug fixing with root cause analysis and test-driven validation","description":"Identifies and fixes bugs by analyzing error messages, stack traces, and test failures, then validates fixes by running tests. The agent can execute terminal commands to run test suites, interpret results, and iterate on fixes. Uses codebase context to understand the bug's scope and potential side effects.","intents":["I have a failing test — I want the agent to fix the code and verify the test passes","I have a bug report with a stack trace — I want the agent to find and fix the root cause","I want the agent to fix a bug without introducing regressions in other tests"],"best_for":["teams with comprehensive test suites","developers who want automated bug fixes with validation","codebases where test execution is fast (< 1 minute)"],"limitations":["No automatic error recovery — if a fix fails tests, agent escalates to user rather than attempting multiple iterations","Test execution timeout behavior unknown — unclear how agent handles slow test suites","Root cause analysis mechanism undisclosed — unclear if agent uses static analysis, dynamic debugging, or heuristics","No mention of debugging tools (debugger, profiler) integration — likely limited to test-based validation"],"requires":["Test suite configured and runnable via terminal command","Test results parseable by agent (standard formats like JUnit, TAP, etc.)","Sufficient credits for iterative fix attempts"],"input_types":["error message or failing test (text)","stack trace (text)"],"output_types":["fixed code (code)","test results (text/structured)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__cap_9","uri":"capability://code.generation.editing.codebase.aware.refactoring.with.consistency.preservation","name":"codebase-aware refactoring with consistency preservation","description":"Refactors code across multiple files while preserving consistency with existing patterns and architecture. The agent understands the codebase structure, identifies refactoring opportunities, and applies changes consistently across all affected files. Uses semantic context to ensure refactored code aligns with project conventions.","intents":["I want to rename a function across my entire codebase and update all references","I need to extract a common pattern into a shared utility and update all usages","I want to refactor a module while maintaining backward compatibility"],"best_for":["large codebases with many cross-references","teams with consistent architectural patterns","developers who want refactoring with automated consistency checks"],"limitations":["Refactoring scope limited by codebase indexing — unclear if agent can refactor across monorepos or multiple repositories","No explicit mention of backward compatibility validation — unclear if agent checks for breaking changes","Refactoring complexity limits unknown — unclear how agent handles circular dependencies or complex inheritance hierarchies"],"requires":["Codebase indexed by Augment","Clear architectural structure for pattern detection","Test suite to validate refactoring (recommended)"],"input_types":["refactoring request (natural language)","codebase context (auto-indexed)"],"output_types":["refactored code (code)","change summary (text)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"augment-code__headline","uri":"capability://automation.workflow.ai.coding.agent.for.software.teams","name":"ai coding agent for software teams","description":"An AI coding agent designed specifically for professional software teams, capable of understanding entire codebases and assisting with complex engineering tasks such as architecture, debugging, and code review.","intents":["best AI coding agent","AI agent for software development","AI tool for code review","AI assistant for debugging","AI coding tool for professional teams"],"best_for":["professional software teams","complex engineering tasks"],"limitations":["requires human oversight for critical decisions"],"requires":[],"input_types":["task descriptions","codebases"],"output_types":["code changes","review comments"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.80+ or JetBrains IDE (2023.1+) or Augment CLI","Active internet connection (cloud-hosted agent)","Minimum 40,000 credits/month (Indie tier)","VS Code or JetBrains IDE with Augment plugin","Git repository initialized (implied by checkpoint integration)","Active session with Augment Code agent","Active Augment Code workspace","User approval workflow for each Rule","Active Augment Code subscription","Payment method for auto top-up"],"failure_modes":["Planning phase adds latency before execution begins — no real-time streaming of changes","Semantic filtering mechanism is proprietary and not transparent — users cannot tune relevance thresholds","Maximum task depth and complexity limits unknown — unclear how agent handles deeply nested dependencies or circular references","Checkpoint system requires agent to pause between steps — cannot stream continuous changes","Checkpoint storage mechanism unclear — unknown whether checkpoints persist across sessions or are ephemeral","No automatic error recovery — if a step fails (compilation error, test failure), agent escalates to user rather than attempting retry logic","Memory curation requires manual user approval — does not scale automatically to very large codebases","Rule format and schema unknown — unclear how users define Rules or what expressiveness is supported","No automatic Rule suggestion — agent surfaces memories for approval but doesn't automatically generate Rules","Rule application mechanism undisclosed — unclear if Rules are applied via prompt injection, fine-tuning, or other means","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:19.836Z","last_scraped_at":null,"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=augment-code","compare_url":"https://unfragile.ai/compare?artifact=augment-code"}},"signature":"2WvEI7UvwQ1ILPiS1++hd/7RwMZXP44+dp3pfXrMTwuvuJKhv3ArdH3T82DA30kqshgjaxS5Ix9k0E53Dpm2DA==","signedAt":"2026-06-20T11:39:59.489Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/augment-code","artifact":"https://unfragile.ai/augment-code","verify":"https://unfragile.ai/api/v1/verify?slug=augment-code","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"}}