{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"aiac","slug":"aiac","name":"aiac","type":"cli","url":"https://github.com/gofireflyio/aiac","page_url":"https://unfragile.ai/aiac","categories":["cli-tools"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"aiac__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.backend.abstraction.with.unified.interface","name":"multi-provider llm backend abstraction with unified interface","description":"AIAC implements a Backend interface abstraction layer that enables seamless switching between OpenAI, AWS Bedrock, and Ollama LLM providers through a single unified API. Each backend implementation satisfies the same interface contract, allowing the core library to invoke LLM calls interchangeably without provider-specific branching logic. This design pattern decouples the code generation logic from provider-specific API details, enabling new backends to be added by implementing the interface without modifying existing code.","intents":["Switch between different LLM providers without rewriting generation logic","Add support for a new LLM provider by implementing a single interface","Use multiple LLM backends in the same configuration for different use cases","Avoid vendor lock-in by maintaining provider abstraction"],"best_for":["DevOps teams evaluating multiple LLM providers for cost/performance","Organizations with multi-cloud or hybrid LLM strategies","Developers building extensible IaC generation tools"],"limitations":["Backend implementations must normalize provider-specific response formats, adding abstraction overhead","Provider-specific features (streaming, function calling, vision) may not be uniformly exposed across all backends","Configuration complexity increases with each additional backend provider"],"requires":["Valid API credentials for at least one configured backend (OpenAI API key, AWS credentials, or Ollama endpoint)","TOML configuration file with backend definitions"],"input_types":["text prompts","configuration"],"output_types":["generated code","structured responses from LLM"],"categories":["tool-use-integration","architecture-pattern"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_1","uri":"capability://automation.workflow.toml.based.multi.backend.configuration.with.environment.variable.override","name":"toml-based multi-backend configuration with environment variable override","description":"AIAC provides a hierarchical configuration system using TOML files stored in XDG_CONFIG_HOME (~/.config/aiac/aiac.toml by default) that defines multiple named backends, each with provider type, credentials, and model defaults. The system supports environment variable overrides for sensitive credentials, allowing users to define backends in configuration while injecting secrets at runtime. Configuration loading follows a precedence chain: CLI flags > environment variables > TOML file defaults, enabling flexible deployment across local development, CI/CD, and containerized environments.","intents":["Define multiple LLM backends (OpenAI, Bedrock, Ollama) in a single configuration file","Inject API credentials via environment variables for secure CI/CD integration","Override default backend or model selection per command invocation","Manage different configurations for different environments (dev, staging, prod)"],"best_for":["Teams deploying AIAC in CI/CD pipelines with secret management","Users managing multiple LLM provider accounts","Organizations requiring configuration-as-code for infrastructure generation"],"limitations":["TOML configuration file must be manually created and maintained; no built-in configuration wizard","Environment variable precedence may cause unexpected behavior if variables are set globally","No built-in validation of backend configuration until first use (errors surface at runtime)"],"requires":["TOML configuration file at ~/.config/aiac/aiac.toml or specified via --config flag","Valid credentials for configured backends (API keys, AWS credentials, or Ollama endpoint URL)","XDG_CONFIG_HOME environment variable set (defaults to ~/.config on Unix-like systems)"],"input_types":["TOML configuration","environment variables","CLI flags"],"output_types":["parsed backend configuration","selected backend instance"],"categories":["automation-workflow","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_10","uri":"capability://code.generation.editing.artifact.type.aware.prompt.engineering.with.domain.specific.system.messages","name":"artifact-type-aware prompt engineering with domain-specific system messages","description":"AIAC constructs domain-specific system prompts based on the target artifact type (Terraform, Dockerfile, Kubernetes, GitHub Actions, OPA, Bash, Python, SQL), guiding the LLM to generate syntactically correct and idiomatic code for each domain. Rather than using a generic system prompt, the tool embeds artifact-type-specific instructions that emphasize best practices, common patterns, and syntax requirements for each format. This enables a single LLM model to generate high-quality code across heterogeneous infrastructure domains without requiring separate specialized models.","intents":["Generate syntactically correct Terraform modules with proper resource structure","Create secure Dockerfiles following container best practices","Build Kubernetes manifests with proper API versions and resource definitions","Generate GitHub Actions workflows with correct YAML syntax and step definitions","Create OPA policies with proper Rego syntax and policy logic"],"best_for":["Teams generating multiple artifact types from a single LLM backend","Organizations standardizing infrastructure patterns through LLM guidance","Users wanting artifact-specific best practices embedded in generated code","Teams reducing manual code review overhead by guiding LLM toward idiomatic patterns"],"limitations":["System prompts are static; they cannot adapt to organization-specific conventions or standards","LLM may still generate incorrect syntax despite artifact-specific guidance; manual review required","Prompt engineering quality directly impacts output quality; poorly designed prompts lead to poor code","No mechanism to provide organization-specific context (e.g., naming conventions, approved modules) to the LLM"],"requires":["Artifact type specified (terraform, dockerfile, kubernetes, github-actions, opa, bash, python, sql)","LLM backend capable of following detailed system prompts"],"input_types":["artifact type specification","natural language requirements"],"output_types":["artifact-specific code (Terraform, Dockerfile, YAML, etc.)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_11","uri":"capability://tool.use.integration.aws.bedrock.backend.integration.with.cross.region.model.access","name":"aws bedrock backend integration with cross-region model access","description":"AIAC integrates with AWS Bedrock by implementing the Backend interface for Bedrock's managed LLM service. The backend handles AWS authentication via IAM credentials, request formatting for Bedrock's API, and response parsing. Users can access multiple LLM providers (Anthropic Claude, Cohere, etc.) through Bedrock's unified API. This enables organizations with existing AWS infrastructure to leverage Bedrock without managing separate API accounts.","intents":["Use AWS Bedrock for code generation within existing AWS environments","Leverage Anthropic Claude models through Bedrock's managed service","Avoid managing separate API accounts by using AWS IAM for authentication","Use Bedrock's cross-region capabilities for high availability"],"best_for":["Organizations with existing AWS infrastructure and IAM setup","Teams preferring managed services over self-hosted or third-party APIs","Enterprises with AWS-centric deployments"],"limitations":["Requires AWS account with Bedrock service access (may require service quota increase)","Bedrock model availability varies by region — may require cross-region API calls","AWS IAM credential management adds complexity compared to simple API keys","Bedrock pricing model differs from direct API access — may be more expensive for low-volume usage"],"requires":["AWS account with Bedrock service enabled","AWS IAM credentials (access key, secret key, or assumed role)","Bedrock model access enabled in desired region","Network connectivity to AWS Bedrock endpoints"],"input_types":["natural language prompts","Bedrock model selection (claude-2, cohere-command, etc.)"],"output_types":["generated code from Bedrock models","responses from selected LLM provider"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_12","uri":"capability://tool.use.integration.ollama.local.llm.backend.for.privacy.preserving.code.generation","name":"ollama local llm backend for privacy-preserving code generation","description":"AIAC integrates with Ollama, an open-source tool for running LLMs locally. The Ollama backend implementation communicates with a local Ollama instance via HTTP API, enabling code generation without sending prompts to external services. Users can run open-source models (Llama 2, Mistral, etc.) locally, providing complete data privacy and no API costs. This backend is ideal for organizations with strict data governance requirements or offline environments.","intents":["Generate infrastructure code without sending prompts to external APIs","Run code generation in air-gapped or offline environments","Avoid API costs by using local open-source models","Maintain complete data privacy for sensitive infrastructure code"],"best_for":["Organizations with strict data governance or compliance requirements","Teams in regulated industries (healthcare, finance) requiring data privacy","Developers working in offline or air-gapped environments","Cost-conscious teams willing to trade code quality for zero API costs"],"limitations":["Local model quality is generally lower than cloud-based models (GPT-4, Claude)","Requires significant local compute resources (GPU recommended) for reasonable performance","Model selection is limited to open-source models available in Ollama","Ollama instance must be running and accessible — adds operational overhead","No automatic model updates — users must manually pull new model versions"],"requires":["Ollama installed and running (https://ollama.ai)","At least one LLM model pulled in Ollama (e.g., ollama pull llama2)","Sufficient local compute resources (GPU recommended for performance)","Network connectivity to local Ollama instance (typically localhost:11434)"],"input_types":["natural language prompts","Ollama model selection (llama2, mistral, etc.)"],"output_types":["generated code from local Ollama models","responses from selected open-source model"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_2","uri":"capability://code.generation.editing.natural.language.to.infrastructure.as.code.generation.with.llm.prompting","name":"natural language to infrastructure-as-code generation with llm prompting","description":"AIAC accepts free-form natural language prompts describing infrastructure requirements and sends them to configured LLM backends with system prompts optimized for IaC generation. The system constructs prompts that guide the LLM to generate specific artifact types (Terraform, CloudFormation, Pulumi, Dockerfile, Kubernetes manifests, GitHub Actions, OPA policies, Bash/Python scripts, SQL queries). The LLM response is streamed back to the user and optionally formatted or saved to files, enabling rapid prototyping of infrastructure code without manual template writing.","intents":["Generate Terraform modules from English descriptions of infrastructure requirements","Create Dockerfiles with security best practices from simple descriptions","Build Kubernetes manifests for complex deployments by describing desired state","Generate GitHub Actions CI/CD pipelines from workflow descriptions","Create OPA policies that enforce infrastructure compliance rules"],"best_for":["Infrastructure engineers prototyping IaC quickly without memorizing syntax","Teams onboarding new members who lack deep IaC expertise","Organizations standardizing infrastructure patterns through LLM-guided generation","DevOps teams reducing boilerplate code writing for repetitive infrastructure"],"limitations":["Generated code quality depends entirely on LLM model capability and prompt engineering; no validation that output is syntactically correct or follows best practices","LLM may hallucinate provider-specific features or deprecated syntax; generated code requires manual review before production use","Prompts are optimized for general IaC but may not capture domain-specific requirements without additional context","No built-in version control or diff comparison for iterative refinement of generated code"],"requires":["Configured LLM backend with valid credentials (OpenAI API key, AWS Bedrock access, or Ollama instance)","Natural language description of infrastructure requirements","Sufficient LLM context window to accommodate prompt + response (varies by model)"],"input_types":["natural language text prompts"],"output_types":["infrastructure-as-code (Terraform, CloudFormation, Pulumi)","configuration files (Dockerfile, Kubernetes YAML)","CI/CD pipelines (GitHub Actions, Jenkins)","policy-as-code (OPA)","utility scripts (Bash, Python, SQL)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_3","uri":"capability://automation.workflow.interactive.code.generation.with.refinement.and.export.options","name":"interactive code generation with refinement and export options","description":"After generating initial code, AIAC enters an interactive mode where users can refine, regenerate, or export the output. The CLI presents options to regenerate with the same prompt, modify the prompt and regenerate, save output to a file, copy to clipboard, or exit. This interactive loop enables iterative refinement of generated code without re-invoking the CLI, reducing context switching and allowing users to converge on acceptable output through multiple LLM invocations within a single session.","intents":["Refine generated infrastructure code through multiple iterations without restarting the CLI","Modify prompts and regenerate code to explore alternative implementations","Save generated code directly to files with automatic naming","Copy generated code to clipboard for pasting into editors or terminals"],"best_for":["Interactive users exploring infrastructure options and iterating on designs","Teams prototyping infrastructure changes before committing to version control","Users without direct file system access who need clipboard-based workflows"],"limitations":["Interactive mode requires terminal/TTY; not suitable for non-interactive CI/CD pipelines","Each regeneration incurs a new LLM API call, increasing costs for exploratory workflows","No built-in diff or comparison between iterations; users must manually track changes","Session state is not persisted; closing the terminal loses all generated code unless explicitly saved"],"requires":["Interactive terminal (TTY) with stdin/stdout available","Sufficient LLM API quota for multiple generation attempts"],"input_types":["user selections from interactive menu","modified prompts"],"output_types":["generated code","saved files","clipboard content"],"categories":["automation-workflow","user-interaction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_4","uri":"capability://tool.use.integration.openai.backend.with.streaming.response.handling","name":"openai backend with streaming response handling","description":"AIAC implements a dedicated OpenAI backend that communicates with OpenAI's API using the official Go SDK, supporting both GPT-3.5-turbo and GPT-4 models. The backend handles streaming responses, allowing real-time display of generated code as it's produced by the LLM rather than waiting for complete generation. It manages API authentication via OPENAI_API_KEY environment variable or configuration file, constructs system and user messages for IaC generation, and handles rate limiting and error responses from OpenAI's API.","intents":["Use OpenAI's GPT-4 or GPT-3.5-turbo models for high-quality infrastructure code generation","Stream generated code in real-time for faster user feedback","Leverage OpenAI's latest model capabilities for complex infrastructure scenarios","Integrate with OpenAI's API while managing costs through model selection"],"best_for":["Teams with OpenAI API access and budget for high-quality LLM inference","Users requiring state-of-the-art code generation quality","Organizations already standardized on OpenAI for other LLM workloads"],"limitations":["Requires valid OpenAI API key and active account with available credits","API calls incur per-token costs; high-volume usage can become expensive","Dependent on OpenAI API availability and rate limits","Streaming responses require network connectivity; no offline fallback"],"requires":["OpenAI API key (set via OPENAI_API_KEY environment variable or configuration)","Network connectivity to OpenAI API endpoints","Active OpenAI account with available API credits"],"input_types":["natural language prompts"],"output_types":["streamed text responses","complete generated code"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_5","uri":"capability://tool.use.integration.aws.bedrock.backend.with.multi.model.provider.support","name":"aws bedrock backend with multi-model provider support","description":"AIAC implements an AWS Bedrock backend that abstracts multiple foundational models (Claude, Llama, Mistral, etc.) available through Bedrock's unified API. The backend handles AWS authentication via IAM credentials or assumed roles, constructs requests compatible with Bedrock's InvokeModel API, and manages model-specific request/response formats. This enables users to leverage Bedrock's managed model hosting without managing separate API keys for each provider, and to switch between models by changing configuration without code changes.","intents":["Use AWS Bedrock's managed models (Claude, Llama, Mistral) for infrastructure generation","Leverage AWS IAM authentication instead of managing separate API keys","Switch between different foundational models by configuration change","Integrate infrastructure generation into AWS-native workflows and cost tracking"],"best_for":["AWS-native organizations with existing IAM infrastructure and Bedrock access","Teams wanting to avoid managing multiple LLM provider API keys","Organizations preferring managed model hosting over direct API calls","Users exploring multiple foundational models within a single platform"],"limitations":["Requires AWS account with Bedrock service enabled and model access provisioned","Model availability varies by AWS region; not all models available in all regions","AWS IAM configuration complexity may exceed direct API key management","Bedrock pricing model differs from direct provider APIs; cost optimization requires understanding Bedrock's token pricing"],"requires":["AWS credentials (IAM user/role with bedrock:InvokeModel permission)","AWS Bedrock service enabled in target region","Model access provisioned for desired foundational models (Claude, Llama, Mistral, etc.)","AWS region configured in AIAC configuration or AWS_REGION environment variable"],"input_types":["natural language prompts"],"output_types":["generated code from Bedrock models"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_6","uri":"capability://tool.use.integration.ollama.backend.with.local.model.execution","name":"ollama backend with local model execution","description":"AIAC implements an Ollama backend that communicates with locally-running Ollama instances via HTTP API, enabling infrastructure code generation using open-source models (Llama 2, Mistral, etc.) without cloud API dependencies. The backend constructs HTTP requests to the Ollama API endpoint (default localhost:11434), handles model selection and parameter configuration, and streams responses from local models. This enables offline-capable infrastructure generation and eliminates per-token API costs, though at the cost of requiring local compute resources and managing model downloads.","intents":["Generate infrastructure code using open-source models without cloud API costs","Run infrastructure generation offline or in air-gapped environments","Avoid sending infrastructure specifications to external APIs for privacy/compliance","Experiment with different open-source models locally before committing to cloud providers"],"best_for":["Organizations with privacy/compliance requirements preventing cloud LLM usage","Teams operating in air-gapped or offline environments","Cost-conscious teams willing to manage local compute for model inference","Developers experimenting with open-source models before production deployment"],"limitations":["Requires local Ollama installation and sufficient compute resources (GPU recommended for reasonable latency)","Model quality generally lower than proprietary models (GPT-4, Claude); generated code may require more manual refinement","User responsible for downloading and managing model files (often multi-GB)","Latency depends on local hardware; inference may be significantly slower than cloud APIs on modest hardware","No built-in model versioning or automatic updates; users must manually manage model versions"],"requires":["Ollama installed and running locally (https://ollama.ai)","At least one model downloaded via ollama pull (e.g., ollama pull mistral)","Ollama API endpoint accessible (default http://localhost:11434)","Sufficient local compute resources (GPU recommended, CPU fallback available)"],"input_types":["natural language prompts"],"output_types":["generated code from local Ollama models"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_7","uri":"capability://code.generation.editing.cli.driven.code.generation.with.artifact.type.specification","name":"cli-driven code generation with artifact type specification","description":"AIAC provides a command-line interface that accepts natural language prompts and optional artifact type specifications (terraform, dockerfile, kubernetes, github-actions, opa, bash, python, sql, etc.) to guide code generation. The CLI parses arguments, loads configuration, selects the appropriate backend, constructs prompts with artifact-type context, and invokes the LLM. Users can specify artifact type explicitly or let the system infer from the prompt, enabling both guided and exploratory workflows from the terminal.","intents":["Generate specific artifact types (Terraform, Dockerfile, Kubernetes) from command line","Quickly prototype infrastructure code without leaving the terminal","Integrate infrastructure generation into shell scripts and automation workflows","Specify artifact type to guide LLM generation toward desired output format"],"best_for":["DevOps engineers and infrastructure teams comfortable with CLI workflows","Automation engineers integrating code generation into scripts","Users preferring terminal-based tools over GUI interfaces","CI/CD pipelines requiring non-interactive infrastructure generation"],"limitations":["Requires familiarity with CLI argument syntax and configuration setup","Non-interactive mode (for CI/CD) provides no feedback loop for refinement","Long prompts may be difficult to pass via command line; consider using stdin or files for complex requirements","Error messages may be cryptic without verbose logging enabled"],"requires":["AIAC binary installed and in PATH","Configured backend with valid credentials","Natural language prompt describing infrastructure requirements"],"input_types":["CLI arguments","natural language text","stdin"],"output_types":["generated code","saved files","stdout"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_8","uri":"capability://automation.workflow.docker.containerized.deployment.with.pre.configured.backends","name":"docker containerized deployment with pre-configured backends","description":"AIAC provides Docker image distribution enabling deployment as a containerized service without requiring local Go installation or configuration. The Docker image includes the compiled AIAC binary and can be invoked with environment variables for backend configuration, allowing infrastructure generation from containers in CI/CD pipelines, Kubernetes clusters, or other containerized environments. This enables AIAC integration into container-native workflows and eliminates dependency management for users.","intents":["Run AIAC in Docker containers for CI/CD pipeline integration","Deploy AIAC as a containerized service in Kubernetes clusters","Avoid installing Go and dependencies on local machines","Standardize AIAC environment across development and CI/CD systems"],"best_for":["CI/CD pipelines using Docker or container orchestration","Kubernetes-native teams deploying AIAC as a sidecar or job","Organizations standardizing on containerized tooling","Teams avoiding local tool installation and dependency management"],"limitations":["Requires Docker or container runtime installed","Container image size may be larger than binary-only distribution","Configuration must be passed via environment variables or mounted volumes","Debugging container-based execution may be more complex than local CLI"],"requires":["Docker or compatible container runtime installed","Docker image pulled from registry (e.g., docker pull gofireflyio/aiac)","Backend credentials passed via environment variables or mounted configuration files"],"input_types":["environment variables","mounted configuration files","stdin"],"output_types":["generated code","stdout","mounted output files"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__cap_9","uri":"capability://tool.use.integration.go.library.api.for.programmatic.code.generation","name":"go library api for programmatic code generation","description":"AIAC exposes a Go library (libaiac package) enabling developers to embed infrastructure code generation directly into Go applications. The library provides functions to load configuration, select backends, and invoke code generation programmatically, allowing Go developers to build custom tools, web services, or automation that leverage AIAC's generation capabilities. This enables integration into larger Go-based infrastructure platforms without spawning CLI processes.","intents":["Embed infrastructure code generation into Go applications and services","Build custom tools that extend AIAC functionality with domain-specific logic","Create web services or APIs that expose infrastructure generation capabilities","Integrate AIAC into larger infrastructure platforms written in Go"],"best_for":["Go developers building infrastructure automation tools","Teams creating custom infrastructure platforms with generation capabilities","Organizations building web services that expose infrastructure generation","Developers extending AIAC with custom backends or generation logic"],"limitations":["Requires Go programming knowledge; not suitable for non-developers","Library API stability not guaranteed; breaking changes possible in minor versions","Debugging library integration requires understanding Go error handling and concurrency patterns","No built-in web service wrapper; developers must implement HTTP handlers and request/response serialization"],"requires":["Go 1.16+ installed","AIAC library imported in Go project (github.com/gofireflyio/aiac/libaiac)","Understanding of Go interfaces and error handling"],"input_types":["Go function calls","configuration objects","prompts as strings"],"output_types":["generated code as strings","Go error types"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"aiac__headline","uri":"capability://code.generation.editing.ai.infrastructure.as.code.generator","name":"ai infrastructure-as-code generator","description":"AIAC is a command-line tool that leverages AI to generate Infrastructure-as-Code templates and configurations for various platforms like Terraform, Pulumi, and Docker, allowing users to create infrastructure through natural language prompts.","intents":["best AI Infrastructure-as-Code tool","AI tool for generating Terraform templates","Infrastructure-as-Code generator for cloud configurations","best CLI for automated infrastructure setup","AI-driven IaC solutions for DevOps"],"best_for":["DevOps engineers","cloud architects"],"limitations":["requires familiarity with IaC concepts"],"requires":["command line access","internet connection for AI models"],"input_types":["natural language prompts"],"output_types":["IaC templates","configuration files"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Valid API credentials for at least one configured backend (OpenAI API key, AWS credentials, or Ollama endpoint)","TOML configuration file with backend definitions","TOML configuration file at ~/.config/aiac/aiac.toml or specified via --config flag","Valid credentials for configured backends (API keys, AWS credentials, or Ollama endpoint URL)","XDG_CONFIG_HOME environment variable set (defaults to ~/.config on Unix-like systems)","Artifact type specified (terraform, dockerfile, kubernetes, github-actions, opa, bash, python, sql)","LLM backend capable of following detailed system prompts","AWS account with Bedrock service enabled","AWS IAM credentials (access key, secret key, or assumed role)","Bedrock model access enabled in desired region"],"failure_modes":["Backend implementations must normalize provider-specific response formats, adding abstraction overhead","Provider-specific features (streaming, function calling, vision) may not be uniformly exposed across all backends","Configuration complexity increases with each additional backend provider","TOML configuration file must be manually created and maintained; no built-in configuration wizard","Environment variable precedence may cause unexpected behavior if variables are set globally","No built-in validation of backend configuration until first use (errors surface at runtime)","System prompts are static; they cannot adapt to organization-specific conventions or standards","LLM may still generate incorrect syntax despite artifact-specific guidance; manual review required","Prompt engineering quality directly impacts output quality; poorly designed prompts lead to poor code","No mechanism to provide organization-specific context (e.g., naming conventions, approved modules) to the LLM","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"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":"2026-06-17T09:51:02.370Z","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=aiac","compare_url":"https://unfragile.ai/compare?artifact=aiac"}},"signature":"n4XvJyifO88Go97Bz96LG2TvlUIy+n8cdhKMxshvh53Fzxua1YDa4Em4O2jwExeRivx78sef1j2O9YZS7WB9DA==","signedAt":"2026-06-20T11:25:50.865Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/aiac","artifact":"https://unfragile.ai/aiac","verify":"https://unfragile.ai/api/v1/verify?slug=aiac","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"}}