{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-qwibitai--nanoclaw","slug":"qwibitai--nanoclaw","name":"nanoclaw","type":"agent","url":"https://nanoclaw.dev","page_url":"https://unfragile.ai/qwibitai--nanoclaw","categories":["ai-agents","code-review-security"],"tags":["ai-agents","ai-assistant","claude-code","claude-skills","openclaw"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-qwibitai--nanoclaw__cap_0","uri":"capability://tool.use.integration.multi.platform.message.routing.with.self.registering.channel.adapters","name":"multi-platform message routing with self-registering channel adapters","description":"Routes incoming messages from WhatsApp, Telegram, Slack, Discord, and Gmail to Claude agents by maintaining a self-registering channel system that activates adapters at startup when credentials are present. Each channel adapter implements a standardized interface that the host process (src/index.ts) polls via a message processing pipeline, decoupling platform-specific authentication from core orchestration logic.","intents":["Connect a single Claude agent to multiple messaging platforms without rewriting integration code","Add support for new messaging platforms by implementing a channel adapter interface","Automatically enable/disable channels based on environment credentials without code changes"],"best_for":["Individual developers building personal AI assistants across multiple platforms","Teams deploying Claude agents to existing communication infrastructure (Slack workspaces, Discord servers)"],"limitations":["Channel adapters are synchronous and polled sequentially — high-latency platforms may block message processing for others","No built-in rate limiting per channel — platform API quotas must be managed externally","Message ordering guarantees only within a single channel; cross-channel ordering is undefined"],"requires":["Node.js 18+","Valid API credentials for at least one messaging platform (WhatsApp Business API, Telegram Bot Token, Slack App Token, Discord Bot Token, Gmail OAuth2)","Docker or Apple Container runtime for agent isolation"],"input_types":["text messages","media attachments (platform-dependent)","message metadata (sender, timestamp, thread context)"],"output_types":["text responses","structured message objects with platform-specific formatting","task queue entries for scheduled jobs"],"categories":["tool-use-integration","messaging-platforms"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_1","uri":"capability://automation.workflow.container.isolated.agent.execution.with.file.based.ipc","name":"container-isolated agent execution with file-based ipc","description":"Spawns isolated Linux container instances (via Docker or Apple Container) for each Claude Agent SDK session, with the host process communicating to agents through monitored file directories (src/ipc.ts 1-133) rather than direct process calls. This architecture ensures that agent code execution, filesystem access, and environment variables are sandboxed, preventing malicious or buggy agent code from affecting the host or other agents.","intents":["Run untrusted or user-provided agent code safely without risking host system compromise","Execute agents with different filesystem views and environment configurations in parallel","Isolate agent memory and session state so failures in one agent don't cascade to others"],"best_for":["Security-conscious deployments where agent code execution must be sandboxed","Multi-tenant scenarios where different users' agents must not access each other's data","Development environments where agent code is frequently modified and tested"],"limitations":["File-based IPC adds ~50-200ms latency per host-container round-trip compared to direct function calls","Container startup overhead (~2-5 seconds per agent) makes rapid agent spawning impractical","Mount security system (src/mount-security.ts) requires explicit allowlisting of filesystem paths — overly restrictive configs can break agent functionality","No native support for GPU acceleration or specialized hardware within containers"],"requires":["Docker daemon running (Linux/Mac) or Apple Container runtime (macOS)","Sufficient disk space for container images and agent filesystems","Linux kernel with cgroup and namespace support (or Docker Desktop on macOS/Windows)","Node.js 18+ on host process"],"input_types":["JSON-serialized messages written to monitored IPC directories","Environment variable configurations","Filesystem mount specifications"],"output_types":["JSON-serialized agent responses read from IPC directories","Container logs and stderr streams","Exit codes and error states"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_10","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Implements automatic retry logic with exponential backoff for transient failures (network timeouts, temporary API unavailability, container startup delays). Failed message processing is logged and retried with increasing delays, allowing the system to recover from temporary outages without manual intervention. Permanent failures (invalid credentials, malformed messages) are logged and skipped to prevent infinite retry loops.","intents":["Automatically recover from transient failures without losing messages","Reduce manual intervention when temporary outages occur (network blips, API rate limiting)","Distinguish between transient and permanent failures to avoid wasting resources on unrecoverable errors"],"best_for":["Production deployments with unreliable network connectivity","Scenarios where messaging platforms have occasional availability issues","Agents that call external APIs that may be temporarily unavailable"],"limitations":["Exponential backoff can delay message processing by minutes or hours for repeated failures, impacting user experience","No configurable retry policies — backoff strategy is hardcoded and cannot be customized per agent or message type","Retry logic is at the host level — agents cannot implement custom retry strategies for specific operations","Failed messages are logged but not automatically moved to a dead-letter queue for manual inspection"],"requires":["Host process running continuously to perform retries","Logging infrastructure to track retry attempts","Messaging platform support for idempotent message processing"],"input_types":["Failed message processing attempts","Error codes and exception types"],"output_types":["Retry scheduling with exponential backoff","Error logs and retry metrics","Dead-letter queue entries for permanent failures"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_11","uri":"capability://memory.knowledge.multi.turn.conversation.state.management.with.session.persistence","name":"multi-turn conversation state management with session persistence","description":"Maintains conversation state across multiple message turns by persisting session metadata (conversation ID, participant list, last message timestamp) in SQLite and passing this context to agents on each invocation. Agents can access conversation history through the message archive and maintain turn-by-turn context without requiring external session management systems. Session state is automatically cleaned up after inactivity to prevent unbounded growth.","intents":["Enable agents to maintain context across multiple message turns without losing conversation state","Allow agents to reference previous messages in the same conversation without querying external systems","Implement conversation timeouts and cleanup to prevent resource exhaustion"],"best_for":["Multi-turn conversational agents where context persistence is critical","Scenarios where agents need to reference previous turns without external session stores","Deployments with limited infrastructure where external session management is overkill"],"limitations":["Session state is stored in SQLite — no distributed session sharing across multiple hosts","No automatic session expiration — inactive sessions must be manually cleaned up or configured with TTLs","Session context is limited to what fits in SQLite — very long conversations may exceed practical query sizes","No session encryption — session data is stored in plaintext in SQLite"],"requires":["SQLite database with session schema","Message archive for conversation history retrieval","Host process that manages session lifecycle"],"input_types":["Conversation IDs and participant metadata","Message history and timestamps"],"output_types":["Session state records in SQLite","Conversation context passed to agents","Session cleanup logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_12","uri":"capability://tool.use.integration.skills.system.with.custom.agent.capability.extensions","name":"skills system with custom agent capability extensions","description":"Provides a skills framework where developers can create custom agent capabilities by implementing a standardized skill interface (documented in .claude/skills/debug/SKILL.md). Skills are discovered and loaded at agent startup, allowing agents to extend their functionality without modifying core agent code. Each skill declares its inputs, outputs, and dependencies, enabling the system to validate skill compatibility and manage skill lifecycle.","intents":["Extend agent capabilities with custom logic without modifying core agent code","Share reusable skills across multiple agents through a standardized interface","Implement domain-specific functionality (e.g., data analysis, report generation) as pluggable skills"],"best_for":["Developers building extensible agents with domain-specific capabilities","Teams that want to share skills across multiple agents without code duplication","Scenarios where agent capabilities are frequently added or modified"],"limitations":["Skills are loaded at agent startup — dynamic skill loading is not supported","No built-in skill versioning — multiple versions of the same skill cannot coexist","Skill dependencies are not automatically resolved — circular dependencies or missing dependencies can break agent startup","No skill marketplace or discovery mechanism — skills must be manually integrated into the agent codebase"],"requires":["Skill implementation following the standardized interface","Agent code that loads and invokes skills","Documentation of skill inputs, outputs, and dependencies"],"input_types":["Skill definitions (interface, inputs, outputs)","Skill implementation code"],"output_types":["Loaded skills available to agents","Skill execution results","Skill metadata and documentation"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_13","uri":"capability://automation.workflow.output.streaming.and.real.time.response.delivery","name":"output streaming and real-time response delivery","description":"Streams agent responses back to messaging platforms in real-time as they are generated, rather than waiting for the entire response to complete before sending. This is implemented through the container runner's output streaming mechanism, which monitors agent output and forwards it to the host process, which then sends it to the messaging platform. This creates a more responsive user experience for long-running agent operations.","intents":["Provide real-time feedback to users while agents are processing long-running operations","Reduce perceived latency by showing partial results as they become available","Enable agents to send multiple messages in sequence without waiting for the entire operation to complete"],"best_for":["Agents that perform long-running operations (data analysis, report generation, code execution)","Scenarios where user experience is critical and perceived latency must be minimized","Deployments where agents need to provide incremental results"],"limitations":["Streaming adds complexity to error handling — if an error occurs mid-stream, partial results may have already been sent","Messaging platforms have rate limits on message sending — streaming too many partial results can trigger rate limiting","No built-in buffering or batching — each streamed chunk becomes a separate message, potentially overwhelming users","Streaming is not supported by all messaging platforms (e.g., some platforms require complete messages before sending)"],"requires":["Container runner with output streaming support","Messaging platform support for sending multiple messages in sequence","Agent code that produces output incrementally (e.g., using generators or streaming APIs)"],"input_types":["Agent output streams (stdout, stderr)","Partial response data"],"output_types":["Streamed messages sent to messaging platforms","Real-time response delivery"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_14","uri":"capability://data.processing.analysis.token.counting.and.cost.estimation.for.api.usage","name":"token counting and cost estimation for api usage","description":"Implements a token counting system (referenced in DeepWiki as 'Token Counting System') that estimates the number of tokens consumed by messages and agent responses, enabling cost tracking and budget enforcement. The system counts tokens for both input (messages sent to Claude) and output (responses from Claude), allowing operators to monitor API costs and implement per-agent or per-user spending limits.","intents":["Track API costs for each agent invocation and conversation","Implement spending limits to prevent runaway costs from buggy or malicious agents","Provide cost visibility to users so they understand the resource consumption of their agents"],"best_for":["Deployments with multiple agents where cost attribution is important","Scenarios where API budgets must be enforced to prevent unexpected charges","Operators who need visibility into token consumption patterns"],"limitations":["Token counting is approximate — actual token counts from Claude API may differ from estimates","No fine-grained cost tracking per skill or operation — costs are aggregated at the agent level","Cost limits are enforced at the host level — agents cannot implement custom spending policies","No cost forecasting or trend analysis — historical cost data is not automatically analyzed"],"requires":["Token counting implementation (likely using tiktoken or similar library)","Cost tracking database schema","Agent metadata for cost attribution"],"input_types":["Messages and responses","Token count estimates"],"output_types":["Token counts per message","Cost estimates and actual costs","Cost reports and analytics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_2","uri":"capability://memory.knowledge.persistent.agent.memory.with.claude.md.file.based.context","name":"persistent agent memory with claude.md file-based context","description":"Each container agent maintains a CLAUDE.md file that persists across conversation turns, allowing the agent to accumulate facts, preferences, and task state without requiring external vector databases or RAG systems. The host process manages this file as part of the agent's isolated filesystem, and the Claude Agent SDK reads/updates it during each invocation, creating a lightweight long-term memory mechanism.","intents":["Give agents persistent memory of user preferences and historical context without external dependencies","Allow agents to maintain internal state (task lists, learned facts) across multiple conversation turns","Enable agents to reference their own past decisions and reasoning in future interactions"],"best_for":["Personal AI assistants where memory persistence is critical but infrastructure simplicity is valued","Scenarios where external vector databases or RAG systems are overkill for the amount of context needed","Developers who want to inspect and manually edit agent memory for debugging"],"limitations":["CLAUDE.md file size grows unbounded — no automatic pruning or summarization, leading to token bloat over time","No semantic search or retrieval — agents must parse the entire file to find relevant context","Concurrent writes to CLAUDE.md from multiple agent invocations can cause corruption; requires external locking","File-based storage is slower than in-memory caches for high-frequency context lookups"],"requires":["Claude Agent SDK integration (Anthropic SDK)","Writable filesystem within container","Agent code that explicitly reads and updates CLAUDE.md (not automatic)"],"input_types":["Markdown-formatted text","Structured data embedded in markdown (YAML frontmatter, code blocks)"],"output_types":["Updated CLAUDE.md file","Agent responses that reference memory contents"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_3","uri":"capability://automation.workflow.task.scheduling.and.delayed.execution.with.sqlite.persistence","name":"task scheduling and delayed execution with sqlite persistence","description":"Stores scheduled tasks in SQLite (src/db.ts 1-48) with execution timestamps, allowing agents to schedule work for future execution. The host process polls the task table at regular intervals and invokes agents when task deadlines are reached, enabling agents to create reminders, recurring jobs, and time-delayed actions without external job schedulers like cron or Celery.","intents":["Allow agents to schedule messages or actions to be executed at specific times without external infrastructure","Implement recurring tasks (daily reports, periodic checks) that persist across host restarts","Enable agents to defer expensive operations (like long-running analyses) to off-peak times"],"best_for":["Personal assistants where simple scheduling (reminders, daily summaries) is needed","Deployments that want to avoid external job queue infrastructure (Redis, RabbitMQ)","Single-host deployments where distributed scheduling is unnecessary"],"limitations":["Polling-based execution introduces scheduling jitter — tasks may execute 30-60 seconds after their scheduled time depending on poll interval","No distributed scheduling — tasks are tied to a single host and cannot be load-balanced across multiple instances","SQLite write contention if many agents attempt to schedule tasks simultaneously","No built-in retry logic for failed task execution — failed tasks must be manually re-queued"],"requires":["SQLite database (included with better-sqlite3 dependency)","Host process running continuously (no serverless/FaaS support)","Agent code that explicitly calls task scheduling APIs"],"input_types":["Task metadata (execution timestamp, agent ID, payload)","Cron-like schedule specifications (if supported)"],"output_types":["Task queue entries in SQLite","Execution logs and completion status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_4","uri":"capability://automation.workflow.group.based.message.batching.and.sequential.processing.with.queue.management","name":"group-based message batching and sequential processing with queue management","description":"Implements a group queue system (referenced in architecture as 'Group Queue') that batches messages from the same conversation group and processes them sequentially to maintain conversation coherence. Messages are tagged with a group ID (derived from channel and sender), queued in order, and dispatched to the agent one group at a time, preventing race conditions where multiple messages from the same user arrive simultaneously.","intents":["Maintain conversation context and ordering when multiple messages arrive from the same user in quick succession","Prevent agents from processing out-of-order messages that would break multi-turn conversation logic","Ensure that agent state updates from one message are visible to the next message in the same conversation"],"best_for":["Multi-turn conversational agents where message ordering is critical","High-traffic deployments where burst message arrivals are common","Scenarios where agents maintain conversation-specific state that must be updated atomically"],"limitations":["Sequential processing within a group introduces latency — if one message takes 10 seconds, the next message waits 10 seconds","No priority queuing — important messages are processed in FIFO order regardless of urgency","Group ID derivation logic must be carefully designed to avoid false grouping (e.g., group chats vs. 1-on-1 conversations)"],"requires":["SQLite database for queue persistence","Message metadata that includes group/conversation identifiers","Host process running the polling loop"],"input_types":["Messages with group/conversation IDs","Message timestamps and ordering metadata"],"output_types":["Ordered message dispatch to agents","Queue status and processing logs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_5","uri":"capability://tool.use.integration.mcp.server.integration.and.tool.registration.with.schema.based.function.calling","name":"mcp server integration and tool registration with schema-based function calling","description":"Integrates with Model Context Protocol (MCP) servers to expose tools and resources to agents through a standardized schema-based function calling interface. Agents can discover available tools from MCP servers, invoke them with validated arguments, and receive structured responses, enabling extensibility without modifying core agent code. The system manages MCP server lifecycle (startup, shutdown) and handles tool schema validation.","intents":["Extend agent capabilities with external tools (APIs, databases, file systems) without hardcoding integrations","Allow agents to discover and use tools dynamically based on MCP server definitions","Implement tool calling with argument validation and error handling"],"best_for":["Developers building extensible agent systems where tools are frequently added or modified","Deployments that want to leverage existing MCP server implementations (GitHub, Slack, etc.)","Teams that need standardized tool interfaces across multiple agents"],"limitations":["MCP server startup adds latency to agent initialization — each new tool requires a new server process","No built-in caching of tool schemas — agents must query MCP servers for tool definitions on each invocation","Error handling in MCP servers is opaque — tool failures may not provide actionable error messages to agents","Limited to tools that implement the MCP protocol — legacy APIs require wrapper servers"],"requires":["MCP server implementations (can be custom or third-party)","Claude Agent SDK with MCP support","Network connectivity between host and MCP servers (if remote)"],"input_types":["MCP server configurations (URLs, credentials)","Tool invocation requests with arguments"],"output_types":["Tool schemas and metadata","Tool execution results","Error messages and status codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_6","uri":"capability://safety.moderation.sender.allowlisting.and.privilege.separation.for.access.control","name":"sender allowlisting and privilege separation for access control","description":"Implements sender-based access control where agents can be configured to only respond to messages from whitelisted senders, enforced at the host level before messages are dispatched to containers. This prevents unauthorized users from invoking agents and enables privilege separation where different agents have different sender allowlists, creating a lightweight authorization system without external identity providers.","intents":["Restrict agent access to specific users or groups without implementing full authentication systems","Prevent unauthorized users from invoking sensitive agents (e.g., agents that execute code or modify data)","Implement role-based access where different agents are accessible to different user groups"],"best_for":["Personal AI assistants where access control is based on known user identities","Multi-user deployments where different agents serve different teams or departments","Security-conscious deployments that want to minimize the attack surface"],"limitations":["Allowlists are static and must be manually updated — no dynamic permission management","Sender identity is platform-dependent and may not be reliable (e.g., phone numbers can be spoofed in SMS)","No fine-grained permissions — allowlisting is all-or-nothing per agent","Allowlist configuration must be managed in code or configuration files — no UI for managing permissions"],"requires":["Sender identity information from messaging platforms (user IDs, phone numbers, email addresses)","Configuration mechanism to specify allowlists per agent","Host process that enforces allowlist checks before dispatching messages"],"input_types":["Sender identifiers from messaging platforms","Allowlist configurations (arrays of sender IDs)"],"output_types":["Access granted/denied decisions","Audit logs of access attempts"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_7","uri":"capability://automation.workflow.message.cursor.tracking.and.recovery.for.fault.tolerance","name":"message cursor tracking and recovery for fault tolerance","description":"Maintains message cursors in SQLite for each channel, tracking the last successfully processed message ID. On host restart or after failures, the system resumes from the last cursor position rather than reprocessing all historical messages or losing messages. This enables graceful recovery from transient failures (network outages, agent crashes) without message loss or duplication.","intents":["Resume message processing from the last known good state after host restarts","Prevent message loss when the host crashes or is forcefully terminated","Avoid reprocessing messages that were already handled, reducing duplicate responses"],"best_for":["Production deployments where message loss is unacceptable","Scenarios with unreliable network connectivity where transient failures are common","Agents that perform side effects (sending emails, updating databases) where duplication is costly"],"limitations":["Cursor tracking is per-channel — cross-channel message ordering is not guaranteed after recovery","If a message is partially processed (agent crashes mid-execution), the cursor may be updated before the message is fully handled, leading to message loss","Cursor recovery assumes messaging platforms provide message IDs and support cursor-based retrieval — not all platforms support this","No automatic cleanup of old cursors — the database can grow unbounded if many channels are used"],"requires":["SQLite database with cursor tracking schema","Messaging platform support for message IDs and cursor-based retrieval","Host process that persists cursors after successful message processing"],"input_types":["Message IDs from messaging platforms","Cursor positions (last processed message ID)"],"output_types":["Cursor updates in SQLite","Recovery logs indicating resume points"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_8","uri":"capability://memory.knowledge.transcript.archiving.and.conversation.history.persistence","name":"transcript archiving and conversation history persistence","description":"Automatically archives conversation transcripts to SQLite, storing messages, agent responses, and metadata (timestamps, sender, group) for each conversation. This creates a queryable audit trail and enables agents to reference past conversations, implement conversation search, and provide users with conversation history without requiring external logging systems.","intents":["Maintain a complete audit trail of all conversations for compliance and debugging","Allow agents to search and reference past conversations within the same group","Enable users to retrieve conversation history without relying on messaging platform retention policies"],"best_for":["Regulated industries where conversation audit trails are required (finance, healthcare)","Agents that need to reference past conversations to maintain context","Deployments where messaging platform history is unreliable or limited"],"limitations":["SQLite database size grows unbounded with conversation volume — no automatic archival or pruning","Transcript search is limited to SQL queries — no full-text search or semantic search without additional indexing","Storing sensitive data (PII, credentials) in transcripts creates security and compliance risks","No built-in encryption — transcripts are stored in plaintext in SQLite"],"requires":["SQLite database with transcript schema","Host process that persists messages and responses after each agent invocation","Sufficient disk space for conversation volume"],"input_types":["Messages from messaging platforms","Agent responses","Metadata (timestamps, sender, group)"],"output_types":["Transcript records in SQLite","Query results for conversation search","Exported conversation files (JSON, CSV)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-qwibitai--nanoclaw__cap_9","uri":"capability://safety.moderation.environment.variable.and.secrets.management.with.container.isolation","name":"environment variable and secrets management with container isolation","description":"Manages environment variables and secrets for agents through the container runtime, injecting credentials and configuration into isolated container environments without exposing them to the host process or other agents. Secrets are read from environment variables or configuration files and passed to containers at runtime, ensuring that sensitive data (API keys, database passwords) is compartmentalized and not accessible to other agents or the host.","intents":["Provide agents with API credentials and configuration without hardcoding secrets in agent code","Isolate secrets so that agents can only access credentials they need, not all system secrets","Rotate secrets without redeploying agents or modifying agent code"],"best_for":["Deployments with multiple agents that need different credentials","Security-conscious environments where secrets must be isolated per agent","Scenarios where agents are developed by untrusted parties and must not access other agents' secrets"],"limitations":["Secrets are passed as environment variables, which are visible in container process listings and logs if not carefully managed","No built-in secret rotation — secrets must be manually updated and containers restarted","No audit logging of secret access — agents can read secrets without leaving a trace","Secrets are stored in plaintext in configuration files or environment — no encryption at rest"],"requires":["Container runtime (Docker or Apple Container)","Environment variable configuration mechanism","Agent code that reads secrets from environment variables"],"input_types":["Environment variable names and values","Secret configuration files"],"output_types":["Injected environment variables in container","Agent access to secrets via process environment"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","Valid API credentials for at least one messaging platform (WhatsApp Business API, Telegram Bot Token, Slack App Token, Discord Bot Token, Gmail OAuth2)","Docker or Apple Container runtime for agent isolation","Docker daemon running (Linux/Mac) or Apple Container runtime (macOS)","Sufficient disk space for container images and agent filesystems","Linux kernel with cgroup and namespace support (or Docker Desktop on macOS/Windows)","Node.js 18+ on host process","Host process running continuously to perform retries","Logging infrastructure to track retry attempts","Messaging platform support for idempotent message processing"],"failure_modes":["Channel adapters are synchronous and polled sequentially — high-latency platforms may block message processing for others","No built-in rate limiting per channel — platform API quotas must be managed externally","Message ordering guarantees only within a single channel; cross-channel ordering is undefined","File-based IPC adds ~50-200ms latency per host-container round-trip compared to direct function calls","Container startup overhead (~2-5 seconds per agent) makes rapid agent spawning impractical","Mount security system (src/mount-security.ts) requires explicit allowlisting of filesystem paths — overly restrictive configs can break agent functionality","No native support for GPU acceleration or specialized hardware within containers","Exponential backoff can delay message processing by minutes or hours for repeated failures, impacting user experience","No configurable retry policies — backoff strategy is hardcoded and cannot be customized per agent or message type","Retry logic is at the host level — agents cannot implement custom retry strategies for specific operations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8178545358158997,"quality":0.5,"ecosystem":0.65,"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":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:57:06.483Z","last_commit":"2026-05-03T12:42:11Z"},"community":{"stars":28529,"forks":12754,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=qwibitai--nanoclaw","compare_url":"https://unfragile.ai/compare?artifact=qwibitai--nanoclaw"}},"signature":"XS6XfCje+5EoJjNJdqdy2x0Srx0ZPujpWJzraAgmVFnJVqqvEhiSeunMFrsIfZ3O88dNbppe1lIX3SCTjfK3AQ==","signedAt":"2026-06-21T20:16:23.317Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/qwibitai--nanoclaw","artifact":"https://unfragile.ai/qwibitai--nanoclaw","verify":"https://unfragile.ai/api/v1/verify?slug=qwibitai--nanoclaw","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"}}