{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-coderluii--holyclaude","slug":"coderluii--holyclaude","name":"HolyClaude","type":"webapp","url":"https://holyclaude.coderluii.dev","page_url":"https://unfragile.ai/coderluii--holyclaude","categories":["code-editors"],"tags":["ai","ai-coding","anthropic","claude","claude-code","coding-agent","container","developer-tools","devtools","docker","docker-compose","gemini","headless-browser","openai","playwright"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-coderluii--holyclaude__cap_0","uri":"capability://tool.use.integration.containerized.claude.code.cli.execution.with.oauth.and.api.key.authentication","name":"containerized claude code cli execution with oauth and api key authentication","description":"Runs the official Anthropic Claude Code CLI inside a Docker container with pre-configured OAuth flow support for Claude Max/Pro plans and direct API key authentication. The container bootstraps the Claude Code environment during startup via s6-overlay service supervision, handling credential injection through environment variables and persistent configuration files mounted at runtime. This eliminates manual CLI setup, dependency resolution, and authentication friction while maintaining full feature parity with the native CLI.","intents":["I want to run Claude Code in a containerized environment without manual setup","I need to authenticate Claude Code with my Anthropic API key or OAuth credentials","I want Claude Code to persist its configuration and memory across container restarts"],"best_for":["DevOps engineers deploying AI coding agents in containerized infrastructure","Teams running Claude Code in CI/CD pipelines or headless environments","Developers who want reproducible Claude Code environments across machines"],"limitations":["Requires Docker 20.10.0+ and 2GB minimum RAM allocation","OAuth flow requires interactive browser access during first authentication","API key must be injected via environment variables or mounted .env files — no interactive prompts in headless mode","Container architecture limited to amd64 and arm64; no 32-bit support"],"requires":["Docker Engine 20.10.0+","Docker Compose V2","Anthropic API key OR OAuth-enabled Anthropic account","2GB RAM minimum (4GB+ recommended for concurrent operations)","Outbound HTTPS connectivity to Anthropic API endpoints"],"input_types":["environment variables (ANTHROPIC_API_KEY, OAuth tokens)","mounted configuration files (.env, claude settings)","code repository paths (mounted as volumes)"],"output_types":["CLI command execution results","generated code artifacts","persistent configuration state"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_1","uri":"capability://tool.use.integration.web.based.ui.gateway.for.ai.agent.interaction.on.port.3001","name":"web-based ui gateway for ai agent interaction on port 3001","description":"Exposes a CloudCLI web interface running on port 3001 that provides HTTP/WebSocket access to the containerized AI agents (Claude Code and alternative CLIs). The web server is managed by s6-overlay as a supervised service with automatic restart on failure, and traffic is routed through the container's network stack. This enables browser-based interaction with AI agents without direct CLI access, supporting real-time streaming responses and multi-user concurrent sessions.","intents":["I want to interact with Claude Code through a web browser instead of the command line","I need to expose AI agent capabilities to non-technical team members via a web UI","I want to run multiple concurrent AI agent sessions through a single web interface"],"best_for":["Teams deploying HolyClaude in shared environments or on remote servers","Non-technical stakeholders who need browser-based access to AI coding agents","DevOps teams exposing AI workstations through reverse proxies or load balancers"],"limitations":["Port 3001 must be exposed and accessible from client machines","No built-in authentication or authorization layer — relies on network isolation or reverse proxy auth","WebSocket connections require persistent network connectivity; mobile clients may experience disconnections","Single-instance web server; no built-in load balancing or clustering for high-concurrency scenarios"],"requires":["Port 3001 available and exposed via docker-compose port mapping","Network connectivity from client to container (localhost:3001 or remote IP:3001)","Modern web browser with WebSocket support","s6-overlay service supervision running (included in container)"],"input_types":["HTTP requests (REST API calls)","WebSocket frames (streaming responses)","form data (code input, configuration parameters)"],"output_types":["HTML/CSS/JavaScript UI","JSON API responses","streamed text/code output via WebSocket"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_10","uri":"capability://automation.workflow.docker.compose.orchestration.with.pre.configured.volume.and.network.setup","name":"docker compose orchestration with pre-configured volume and network setup","description":"Provides a production-ready docker-compose.yaml template that orchestrates the HolyClaude container with pre-configured volume mounts (workspace, configuration), network exposure (port 3001 for web UI), shared memory allocation (shm_size: 2g for headless browser), and resource limits. The compose file includes environment variable references (.env file) for credentials and identity mapping (PUID/PGID), enabling users to deploy HolyClaude with a single docker-compose up command without manual configuration. The template handles common Docker pitfalls (shared memory exhaustion, permission mismatches, port conflicts) automatically.","intents":["I want to deploy HolyClaude with a single command without manual Docker configuration","I need to ensure the container has sufficient shared memory for headless browser operations","I want to persist workspace files and configuration across container restarts"],"best_for":["Teams new to Docker who want a working HolyClaude deployment without Docker expertise","DevOps engineers using docker-compose for local development or small-scale deployments","Organizations migrating from bare-metal AI tools to containerized environments"],"limitations":["docker-compose is designed for single-host deployments; not suitable for multi-node clusters (use Kubernetes instead)","No built-in load balancing or auto-scaling; horizontal scaling requires manual container replication","Shared memory (shm_size: 2g) is per-container; concurrent containers may exhaust host shared memory","Port 3001 must be available on the host; conflicts with other services require manual port remapping","No built-in backup or disaster recovery; data loss if volumes are deleted"],"requires":["Docker Engine 20.10.0+","Docker Compose V2","docker-compose.yaml file (provided in repository)",".env file with PUID, PGID, and API key configuration","2GB+ available shared memory on host (shm_size: 2g)"],"input_types":["docker-compose.yaml configuration file",".env file with environment variables","volume mount paths (workspace directory)"],"output_types":["running Docker container with exposed port 3001","mounted volumes for persistence","container logs and service status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_11","uri":"capability://automation.workflow.container.bootstrap.system.with.automatic.service.initialization","name":"container bootstrap system with automatic service initialization","description":"Implements a multi-stage bootstrap system that runs at container startup to initialize services, validate configuration, set up user identity (UID/GID), and prepare the environment for AI agent execution. The bootstrap process uses shell scripts executed before s6-overlay starts supervised services, performing tasks like creating workspace directories, validating API keys, initializing Claude Code settings, and installing on-demand packages (Slim variant). This ensures the container reaches a ready state without manual post-startup configuration, enabling immediate use after docker-compose up.","intents":["I want the container to be fully initialized and ready to use immediately after startup","I need automatic validation of configuration (API keys, URLs) before services start","I want workspace directories and configuration files created automatically if missing"],"best_for":["Teams deploying HolyClaude in CI/CD pipelines where manual post-startup steps are not feasible","Developers who want zero-configuration deployments with sensible defaults","Organizations running HolyClaude as a managed service with automated deployments"],"limitations":["Bootstrap adds 30-60 seconds to container startup time (on-demand package installation in Slim variant adds more)","Bootstrap errors are logged but may not prevent container startup; failed initialization may be silent","No built-in rollback mechanism; failed bootstrap leaves container in partially-initialized state","Bootstrap logic is shell-script-based; complex initialization requires shell scripting expertise","No idempotency guarantees; running bootstrap multiple times may cause unexpected state changes"],"requires":["Bootstrap scripts in /etc/s6-overlay/s6-rc.d/ or similar initialization directories","Shell interpreter (bash/sh) available in container","Environment variables set before bootstrap runs (.env file or docker-compose env_file)"],"input_types":["environment variables (ANTHROPIC_API_KEY, PUID, PGID, etc.)","configuration files (.env, claude settings)","workspace directory paths"],"output_types":["initialized container state","created directories and configuration files","bootstrap logs and validation results"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_12","uri":"capability://code.generation.editing.codebase.aware.code.generation.with.workspace.context.injection","name":"codebase-aware code generation with workspace context injection","description":"Enables AI agents (Claude Code, alternative CLIs) to access the full workspace directory and inject codebase context into prompts, allowing models to generate code that is aware of existing project structure, dependencies, and coding patterns. The workspace is mounted as a Docker volume and accessible to all AI CLIs via a shared directory path. AI agents can read project files, analyze imports and dependencies, and generate code that integrates seamlessly with the existing codebase. This differs from stateless code generation by providing architectural context and reducing the need for manual context specification.","intents":["I want Claude Code to understand my project structure and generate code that fits my existing architecture","I need AI agents to analyze my codebase and suggest improvements or refactorings","I want to generate code that respects my project's coding standards and dependencies"],"best_for":["Teams working on large codebases where architectural context is critical for code quality","Developers using AI agents for refactoring or code review within existing projects","Organizations standardizing on specific frameworks or libraries that AI agents should understand"],"limitations":["Large codebases (>100MB) may exceed context window limits of AI models; requires selective file inclusion","No automatic dependency analysis; AI agents may generate code using outdated or incompatible libraries","Workspace context is read-only from AI agent perspective; generated code must be manually integrated","No built-in codebase indexing; AI agents must parse files on-demand, adding latency","Private or sensitive code in workspace is exposed to AI models; requires careful credential management"],"requires":["Workspace directory mounted as Docker volume","Project files accessible to AI CLI processes (read permissions)","AI agents configured to read workspace context (environment variable or CLI flag)","Codebase size within AI model context window limits (typically 100K-200K tokens)"],"input_types":["project files and directory structure","code prompts with workspace context","dependency files (package.json, requirements.txt, etc.)"],"output_types":["context-aware generated code","codebase analysis and suggestions","integrated code artifacts"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_2","uri":"capability://tool.use.integration.multi.provider.ai.cli.orchestration.with.7.integrated.agents","name":"multi-provider ai cli orchestration with 7 integrated agents","description":"Bundles 7 distinct AI CLI tools (Claude Code, Gemini CLI, OpenAI Codex, Cursor, TaskMaster, Junie, OpenCode) into a single container with unified environment variable configuration and shared tool dependencies. Each CLI is pre-installed with its runtime dependencies and configured to use a common workspace directory. The container's bootstrap system detects which CLIs are enabled via environment variables and initializes only the necessary services, reducing startup time and memory overhead for users who only need a subset of providers.","intents":["I want to compare code generation quality across multiple AI providers without installing 7 separate tools","I need to switch between different AI CLIs for different coding tasks without environment reconfiguration","I want to run multiple AI agents in parallel and aggregate their outputs"],"best_for":["Researchers benchmarking AI code generation across providers","Teams evaluating multiple AI coding tools before standardizing on one","Developers who need provider-specific features (e.g., Cursor's IDE integration, Gemini's multimodal capabilities)"],"limitations":["Full variant image is ~3GB; slim variant omits some CLIs and requires on-demand installation","Each CLI has distinct authentication requirements and API key formats — no unified credential management","No built-in orchestration layer to coordinate outputs across CLIs; users must manually invoke each CLI","Some CLIs may have conflicting dependencies or version requirements, requiring careful environment isolation","API rate limits are per-provider and not aggregated; concurrent usage across CLIs may hit limits faster"],"requires":["Docker image variant selection (full ~3GB or slim ~1.5GB)","Individual API keys or OAuth credentials for each enabled CLI provider","Environment variables for each provider (ANTHROPIC_API_KEY, GOOGLE_API_KEY, OPENAI_API_KEY, etc.)","Sufficient disk space for full variant (~3GB) or network access for slim variant package installation"],"input_types":["CLI arguments and flags (provider-specific)","code files and repository paths","environment variables (provider credentials)"],"output_types":["generated code (provider-specific formats)","CLI execution logs","structured metadata (model version, token usage)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_3","uri":"capability://image.visual.headless.browser.automation.stack.with.chromium.xvfb.and.playwright","name":"headless browser automation stack with chromium, xvfb, and playwright","description":"Provides a pre-configured headless browser environment combining Chromium, Xvfb (X11 virtual framebuffer), and Playwright for automated web interaction, screenshot capture, and testing. The container allocates shared memory (shm_size: 2g) to prevent Chromium crashes during concurrent browser operations, and Playwright is pre-installed with bindings for Node.js. The browser stack is managed by s6-overlay as a supervised service, enabling AI agents to programmatically navigate websites, extract data, and generate visual artifacts without requiring a display server.","intents":["I want Claude Code to take screenshots of web applications and analyze them visually","I need to automate web scraping or form filling as part of AI-driven workflows","I want to run end-to-end web testing driven by AI agents without manual browser setup"],"best_for":["AI agents that need visual perception of web UIs (screenshot analysis, OCR)","Teams automating web scraping or data extraction workflows","QA engineers building AI-driven end-to-end testing pipelines"],"limitations":["Requires 2GB minimum shared memory (shm_size: 2g in docker-compose) — insufficient allocation causes Chromium crashes","Xvfb adds ~50-100ms latency per browser operation compared to native headless mode","No GPU acceleration; rendering is CPU-bound and slower for complex JavaScript-heavy sites","Playwright Node.js bindings only; no Python or Go support without additional installation","Concurrent browser instances compete for shared memory; more than 3-4 simultaneous browsers may exhaust shm"],"requires":["Docker shm_size: 2g (or higher) in docker-compose configuration","4GB+ RAM recommended (2GB minimum, but insufficient for concurrent operations)","Node.js 14+ (pre-installed in container)","Playwright npm package (pre-installed in full variant)"],"input_types":["URLs (for browser navigation)","CSS/XPath selectors (for element interaction)","JavaScript code (for custom page interactions)","form data (for automated input)"],"output_types":["PNG/JPEG screenshots","extracted HTML/text content","structured data (JSON from page parsing)","browser console logs and network traces"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_4","uri":"capability://memory.knowledge.persistent.configuration.and.memory.state.management.across.container.restarts","name":"persistent configuration and memory state management across container restarts","description":"Implements a volume-based persistence strategy using Docker named volumes and bind mounts to preserve Claude Code settings, AI CLI configurations, workspace files, and memory state across container lifecycle events. Configuration files (e.g., Claude settings, .env credentials) are mounted at container startup, and the bootstrap system initializes user identity (UID/GID) to match the host to prevent permission mismatches. SQLite databases used by AI CLIs are stored on local volumes rather than network-attached storage (NAS) to avoid locking issues, and a dedicated workspace directory persists generated code artifacts.","intents":["I want Claude Code to remember my settings and authentication across container restarts","I need to share code artifacts and workspace files between the host and container without permission errors","I want to avoid losing AI agent memory or conversation history when the container is stopped and restarted"],"best_for":["Teams running HolyClaude as a long-lived service with frequent restarts","Developers who need seamless host-container file synchronization","Organizations using NAS or network storage and experiencing SQLite locking issues"],"limitations":["UID/GID mismatch between host and container can cause permission errors; requires explicit configuration in docker-compose","SQLite databases on NAS/network storage experience locking contention; local volumes required for reliability","Volume mount performance varies by storage backend (native ext4 faster than NFS or SMB)","No built-in backup or snapshot mechanism; users must implement external volume backup strategies","Bind mounts to host directories may expose sensitive files (API keys, credentials) if not properly secured"],"requires":["Docker volume support (named volumes or bind mounts)","Host directory with write permissions for bind mounts","UID/GID configuration in docker-compose (user: \"${UID}:${GID}\")",".env file with PUID and PGID environment variables matching host user","Local storage for SQLite databases (not NAS/network storage)"],"input_types":["configuration files (.env, claude settings JSON)","API credentials and authentication tokens","code repository files and workspace directories"],"output_types":["persisted configuration state","workspace artifacts and generated code","SQLite database files (memory, conversation history)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_5","uri":"capability://automation.workflow.process.supervision.and.graceful.shutdown.with.s6.overlay","name":"process supervision and graceful shutdown with s6-overlay","description":"Uses s6-overlay as the container's PID 1 process manager to supervise multiple services (Claude Code CLI, web UI, headless browser, notification daemon) with automatic restart on failure and coordinated graceful shutdown. Each service is defined as an s6 service directory with run scripts and optional finish scripts for cleanup. When the container receives a SIGTERM signal, s6 orchestrates shutdown of all supervised services in dependency order, preventing orphaned processes and ensuring data consistency. This replaces the typical single-process Docker pattern with a robust multi-service architecture.","intents":["I want multiple AI services to run concurrently in the container with automatic restart if one crashes","I need graceful shutdown of all services when the container stops, not abrupt process termination","I want to monitor service health and automatically recover from transient failures"],"best_for":["Teams running HolyClaude as a production service requiring high availability","Deployments where service crashes must be automatically recovered without manual intervention","Environments where graceful shutdown is critical (e.g., saving state, closing database connections)"],"limitations":["s6-overlay adds ~100-200ms startup overhead compared to single-process containers","Service restart logic is basic (fixed retry count); no exponential backoff or circuit breaker patterns","Debugging multi-service failures requires understanding s6 service directories and run scripts","No built-in health check aggregation; individual service health must be monitored externally","Service dependencies must be manually specified in s6 service definitions; no automatic dependency resolution"],"requires":["s6-overlay package (pre-installed in HolyClaude image)","Service run scripts in /etc/s6-overlay/s6-rc.d/ directories","Understanding of s6 service directory structure and run script format","Proper signal handling in supervised services (SIGTERM for graceful shutdown)"],"input_types":["service run scripts (shell scripts defining service startup)","finish scripts (cleanup logic on service stop)","service dependency declarations"],"output_types":["supervised process execution","automatic restart on failure","coordinated shutdown signals"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_6","uri":"capability://tool.use.integration.notification.system.with.apprise.supporting.100.delivery.channels","name":"notification system with apprise supporting 100+ delivery channels","description":"Integrates Apprise (a unified notification library) to send alerts and status updates to 100+ services including Discord, Telegram, Slack, email, webhooks, and custom endpoints. The notify.py script is invoked by AI agents or container services to dispatch notifications with configurable templates and delivery channels. Apprise abstracts provider-specific API differences, allowing a single notification call to fan out to multiple channels simultaneously. Configuration is managed via environment variables (APPRISE_URLS) or configuration files, enabling flexible notification routing without code changes.","intents":["I want Claude Code to notify me on Discord or Slack when a long-running task completes","I need to send alerts to multiple channels (email, Telegram, webhook) from a single notification call","I want to configure notification destinations without modifying code or container images"],"best_for":["Teams running long-running AI coding tasks that need completion notifications","DevOps engineers integrating HolyClaude into monitoring and alerting pipelines","Organizations with multi-channel notification requirements (Slack, email, SMS, etc.)"],"limitations":["Apprise adds ~500ms-1s latency per notification (network I/O to external services)","No built-in retry logic for failed deliveries; notifications may be silently dropped if provider is unavailable","Notification templates are basic; complex formatting requires custom notify.py modifications","Rate limiting is per-provider; high-frequency notifications may exceed provider limits","No persistent queue; notifications in-flight when container stops are lost"],"requires":["Apprise Python package (pre-installed in container)","APPRISE_URLS environment variable with provider URLs (e.g., discord://webhook-id/token)","Network connectivity to notification providers (Discord, Slack, email servers, etc.)","notify.py script (included in container) or custom notification wrapper"],"input_types":["notification message text","provider URLs (Apprise format)","optional metadata (task name, status code)"],"output_types":["HTTP requests to notification providers","delivery status (success/failure)","provider-specific response metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_7","uri":"capability://tool.use.integration.ollama.integration.for.local.and.cloud.hosted.language.models","name":"ollama integration for local and cloud-hosted language models","description":"Enables integration with Ollama (a local LLM runtime) to run open-source models (Llama 2, Mistral, etc.) locally or connect to cloud-hosted Ollama instances. The container can be configured to use Ollama as a fallback provider when Anthropic/OpenAI APIs are unavailable or rate-limited, or as a primary provider for privacy-sensitive workloads. Configuration is managed via OLLAMA_BASE_URL environment variable, and AI CLIs are configured to route requests to the Ollama endpoint. This enables cost-effective, privacy-preserving AI coding without external API dependencies.","intents":["I want to run Claude Code with local open-source models to avoid API costs and external dependencies","I need a fallback AI provider when Anthropic/OpenAI APIs are rate-limited or unavailable","I want to keep code and prompts private by running models locally instead of sending to external APIs"],"best_for":["Organizations with privacy requirements prohibiting external API usage","Cost-conscious teams running high-volume AI coding tasks","Researchers experimenting with open-source models without API costs"],"limitations":["Local Ollama requires significant GPU memory (8GB+ for Llama 2 7B, 16GB+ for larger models)","Open-source models have lower code generation quality than Claude/GPT-4; expect 20-40% lower accuracy","Ollama integration requires manual configuration of model endpoints; no automatic provider selection","Cloud-hosted Ollama adds network latency (100-500ms) compared to local instances","No built-in load balancing or failover between multiple Ollama instances"],"requires":["Ollama installed locally (ollama.ai) or access to cloud-hosted Ollama instance","OLLAMA_BASE_URL environment variable (e.g., http://localhost:11434)","GPU with sufficient VRAM for target model (8GB+ recommended)","Network connectivity to Ollama endpoint (localhost or remote IP)"],"input_types":["code prompts and context","Ollama model names (llama2, mistral, etc.)","OLLAMA_BASE_URL configuration"],"output_types":["generated code from local models","token usage statistics","model inference logs"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_8","uri":"capability://automation.workflow.image.variant.selection.full.vs.slim.with.on.demand.package.installation","name":"image variant selection (full vs. slim) with on-demand package installation","description":"Distributes HolyClaude in two Docker image variants: Full (~3GB, includes all 7 AI CLIs and 50+ dev tools pre-installed) and Slim (~1.5GB, core tools only with on-demand installation). Users select the variant via docker-compose image tag (latest for Full, slim for Slim). The Slim variant uses a package manager (apt, npm, pip) to install additional tools at container startup if requested via environment variables, reducing initial image size and download time while maintaining flexibility. This enables users to optimize for startup speed (Full) or image size (Slim) based on their deployment constraints.","intents":["I want a minimal HolyClaude image that downloads quickly and uses less disk space","I need all 7 AI CLIs and 50+ dev tools pre-installed without waiting for on-demand installation","I want to customize which tools are installed based on my specific use case"],"best_for":["Teams with limited disk space or slow container registry bandwidth (Slim variant)","Organizations standardizing on a single AI provider and not needing all 7 CLIs (Slim variant)","Production deployments where startup time is critical and pre-installation is acceptable (Full variant)"],"limitations":["Full variant requires 3GB+ disk space and 5-10 minutes to download/extract","Slim variant requires network access to package repositories (apt, npm, pip) at startup for on-demand installation","On-demand installation adds 2-5 minutes to container startup time vs. pre-installed Full variant","Slim variant may fail to install packages if package repositories are unavailable or deprecated","No granular control over which tools are installed; Full includes all, Slim installs all requested tools"],"requires":["Docker image tag selection (latest for Full, slim for Slim)","3GB+ disk space for Full variant, 1.5GB+ for Slim variant","Network access to package repositories (apt.ubuntu.com, npm registry, PyPI) for Slim variant","Environment variables specifying optional packages for Slim variant"],"input_types":["docker-compose image tag (latest or slim)","environment variables for optional package selection"],"output_types":["Docker image with pre-installed or on-demand-installed tools","container startup logs showing installation progress"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-coderluii--holyclaude__cap_9","uri":"capability://automation.workflow.environment.variable.driven.configuration.with.env.file.support","name":"environment variable-driven configuration with .env file support","description":"Implements configuration management via environment variables and .env files, enabling users to customize Claude Code settings, AI provider credentials, notification endpoints, and container behavior without modifying Dockerfile or docker-compose.yaml. The bootstrap system reads .env files at container startup and injects variables into supervised services. Common variables include ANTHROPIC_API_KEY, OLLAMA_BASE_URL, APPRISE_URLS, PUID/PGID for identity mapping, and feature flags for enabling/disabling specific services. This enables reproducible, version-controlled configuration across deployments.","intents":["I want to configure HolyClaude with my API keys and settings without modifying the Dockerfile","I need to use different configurations for development, staging, and production environments","I want to version-control my HolyClaude configuration in git without exposing secrets"],"best_for":["Teams managing multiple HolyClaude deployments with different configurations","DevOps engineers using infrastructure-as-code (Terraform, Ansible) to deploy HolyClaude","Developers who want reproducible local environments matching production configuration"],"limitations":["Secrets (API keys) in .env files are stored in plaintext; requires file-level access control","No built-in secret rotation; API key changes require container restart","Environment variable names are not validated; typos silently result in missing configuration","No schema validation for configuration values; invalid URLs or malformed JSON are not caught until runtime",".env files are not automatically excluded from git; requires .gitignore discipline to prevent secret leaks"],"requires":[".env file in the same directory as docker-compose.yaml","Environment variable names matching HolyClaude's expected schema (ANTHROPIC_API_KEY, PUID, PGID, etc.)","docker-compose.yaml with env_file: .env directive","File permissions restricting .env access (chmod 600) to prevent secret exposure"],"input_types":[".env file with KEY=VALUE pairs","environment variables passed via docker-compose or docker run -e"],"output_types":["injected environment variables in container processes","configured service behavior based on variable values"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":34,"verified":false,"data_access_risk":"high","permissions":["Docker Engine 20.10.0+","Docker Compose V2","Anthropic API key OR OAuth-enabled Anthropic account","2GB RAM minimum (4GB+ recommended for concurrent operations)","Outbound HTTPS connectivity to Anthropic API endpoints","Port 3001 available and exposed via docker-compose port mapping","Network connectivity from client to container (localhost:3001 or remote IP:3001)","Modern web browser with WebSocket support","s6-overlay service supervision running (included in container)","docker-compose.yaml file (provided in repository)"],"failure_modes":["Requires Docker 20.10.0+ and 2GB minimum RAM allocation","OAuth flow requires interactive browser access during first authentication","API key must be injected via environment variables or mounted .env files — no interactive prompts in headless mode","Container architecture limited to amd64 and arm64; no 32-bit support","Port 3001 must be exposed and accessible from client machines","No built-in authentication or authorization layer — relies on network isolation or reverse proxy auth","WebSocket connections require persistent network connectivity; mobile clients may experience disconnections","Single-instance web server; no built-in load balancing or clustering for high-concurrency scenarios","docker-compose is designed for single-host deployments; not suitable for multi-node clusters (use Kubernetes instead)","No built-in load balancing or auto-scaling; horizontal scaling requires manual container replication","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2716860529543617,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:34.540Z","last_commit":"2026-04-10T09:58:01Z"},"community":{"stars":2173,"forks":231,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=coderluii--holyclaude","compare_url":"https://unfragile.ai/compare?artifact=coderluii--holyclaude"}},"signature":"7szQ6tpDcARrROaPukVcbJojtU0f1wi6ZAiQn3OiQt2yjJxnFb1I7bRh+lh/zemE7f2Xh6au1oEnEPAvPWebBQ==","signedAt":"2026-06-22T21:14:56.581Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/coderluii--holyclaude","artifact":"https://unfragile.ai/coderluii--holyclaude","verify":"https://unfragile.ai/api/v1/verify?slug=coderluii--holyclaude","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"}}