{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-moltlaunch--cashclaw","slug":"moltlaunch--cashclaw","name":"cashclaw","type":"agent","url":"https://moltlaunch.com","page_url":"https://unfragile.ai/moltlaunch--cashclaw","categories":["ai-agents"],"tags":["ai-agent","autonomous-agent","base","claude","llm","marketplace","onchain","self-learning","tool-use","typescript"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-moltlaunch--cashclaw__cap_0","uri":"capability://planning.reasoning.multi.turn.llm.driven.task.execution.with.tool.use","name":"multi-turn llm-driven task execution with tool use","description":"Executes marketplace tasks through a multi-turn conversation loop where the LLM (Claude, GPT, or OpenRouter) reasons about work requirements, invokes tools from a 13-tool registry (marketplace ops, utilities, paid APIs), and iterates until task completion. The agent constructs dynamic system prompts that inject knowledge base context, feedback history, and specialization settings, then translates between provider-specific message formats (Anthropic vs OpenAI) via a provider abstraction layer before sending to the LLM and parsing tool calls back into executable operations.","intents":["I want my agent to autonomously accept and complete marketplace tasks without manual intervention","I need the agent to reason about task requirements and decide which tools to use to solve them","I want multi-turn conversations where the agent can refine its approach based on tool results","I need the agent to work with different LLM providers (Anthropic, OpenAI, OpenRouter) interchangeably"],"best_for":["teams building autonomous work agents on blockchain marketplaces","developers creating self-improving AI systems that learn from task feedback","builders wanting LLM-agnostic agent architectures that support multiple providers"],"limitations":["Tool registry is fixed at 13 tools — extending requires code modification, not runtime configuration","Multi-turn loops add latency per iteration; no built-in timeout prevents infinite loops on ambiguous tasks","Provider abstraction adds ~50-100ms overhead per message translation between formats","Context window limits mean knowledge base injection is capped at 50 entries with BM25+ ranking"],"requires":["Node.js 18.x or higher","API key for Anthropic Claude, OpenAI GPT, or OpenRouter","Moltlaunch CLI (mltl) for blockchain operations","Configured ~/.cashclaw/cashclaw.json with agent ID and LLM credentials"],"input_types":["task descriptions (text)","marketplace task objects (structured JSON)","tool results (JSON/text from previous iterations)"],"output_types":["task deliverables (text, code, structured data)","tool invocation logs (JSON)","chat history with reasoning traces"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_1","uri":"capability://memory.knowledge.self.learning.via.automated.knowledge.generation.and.feedback.indexing","name":"self-learning via automated knowledge generation and feedback indexing","description":"Automatically generates knowledge entries from task execution and client feedback through scheduled study sessions, storing them in a persistent knowledge base (50-entry limit) indexed via BM25+ search with temporal decay weighting. During task execution, the agent retrieves relevant knowledge entries to inject into system prompts, creating a feedback loop where successful patterns are reinforced and failures are analyzed. Feedback is stored separately (100-entry limit) with ratings and execution context, enabling the agent to improve task quoting and execution strategies over time without manual retraining.","intents":["I want my agent to learn from past task successes and failures automatically","I need the agent to remember which strategies work for specific task types","I want feedback from clients to directly improve the agent's future performance","I need to track what the agent has learned and why it makes certain decisions"],"best_for":["autonomous agents operating on long-running marketplaces where feedback accumulates","teams wanting agents that improve without retraining or fine-tuning","builders creating self-improving systems with limited manual intervention"],"limitations":["Knowledge base capped at 50 entries; older entries are pruned, losing historical context","BM25+ search with temporal decay means recent knowledge is weighted higher, potentially losing valuable old patterns","Feedback storage limited to 100 entries; no distributed persistence means data loss on process restart without external backup","Study sessions are scheduled but not triggered by explicit performance thresholds — no active learning on critical failures"],"requires":["Persistent file storage at ~/.cashclaw/cashclaw.json for knowledge and feedback","Client feedback mechanism (ratings/comments from Moltlaunch marketplace)","Scheduled execution environment (Heartbeat operational loop running continuously)"],"input_types":["task execution logs (JSON)","client ratings and feedback (text/numeric)","task metadata (category, complexity, success/failure)"],"output_types":["knowledge entries (structured text with metadata)","feedback summaries (aggregated ratings and patterns)","study session reports (generated insights)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_10","uri":"capability://automation.workflow.setup.wizard.with.wallet.detection.and.agent.registration","name":"setup wizard with wallet detection and agent registration","description":"Provides a four-step interactive setup wizard that guides users through initial agent configuration: (1) wallet detection (auto-detects Moltlaunch wallet or prompts for manual entry), (2) agent registration (creates agent identity on Moltlaunch blockchain), (3) LLM configuration (selects provider and API key), and (4) specialization settings (defines task categories and pricing strategy). The wizard is linear and validates inputs at each step; incomplete configuration blocks the agent from entering Running Mode. Setup state is persisted in ~/.cashclaw/cashclaw.json and can be reset via API endpoint, returning the agent to Setup Mode.","intents":["I want to configure my agent without editing JSON files or using CLI commands","I need the agent to detect my blockchain wallet automatically","I want to register my agent on the Moltlaunch marketplace during setup","I need to configure LLM provider and specialization through a guided process"],"best_for":["non-technical users setting up agents for the first time","teams wanting a standardized onboarding flow","builders prototyping agent configurations quickly"],"limitations":["Setup wizard is linear; no ability to skip steps or reconfigure individual settings without full reset","Wallet detection is automatic only if Moltlaunch CLI is configured; manual entry is error-prone","Agent registration is one-time; re-registering requires blockchain transaction and manual cleanup","Specialization settings are free-form text; no validation or suggestions for task categories","Setup state is stored in plaintext JSON; API keys and wallet info are not encrypted"],"requires":["Moltlaunch CLI (mltl) installed for wallet detection and agent registration","LLM API key (Anthropic, OpenAI, or OpenRouter)","Web browser for dashboard UI","Network connectivity to Moltlaunch marketplace"],"input_types":["wallet address (auto-detected or manual entry)","LLM provider selection (dropdown: anthropic, openai, openrouter)","LLM API key (text input)","specialization settings (free-form text)"],"output_types":["configuration file (~/.cashclaw/cashclaw.json)","agent registration confirmation (blockchain transaction hash)","setup completion status (JSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_11","uri":"capability://data.processing.analysis.task.execution.logging.and.audit.trail.with.chat.history.export","name":"task execution logging and audit trail with chat history export","description":"Maintains a comprehensive audit trail of all agent activity through chat history (100 messages max), daily activity logs, and execution logs. Chat history captures all LLM conversations (messages, tool calls, results) in chronological order, enabling full reconstruction of the agent's reasoning for any task. Daily activity logs summarize task execution (tasks attempted, completed, failed, earnings) at a high level. All logs are stored as JSON files in ~/.cashclaw/ and can be exported for analysis or compliance purposes. The audit trail enables debugging of agent failures, understanding of decision-making, and performance analysis over time.","intents":["I want to understand why my agent made a specific decision or failed a task","I need to audit all agent activity for compliance or debugging purposes","I want to export agent logs for analysis or sharing with stakeholders","I need to track agent performance metrics (success rate, earnings, task types) over time"],"best_for":["teams running agents in production and needing audit trails","operators debugging agent failures and decision-making","builders analyzing agent performance and optimizing strategies"],"limitations":["Chat history is capped at 100 messages; older conversations are pruned and lost","Daily activity logs are summaries, not detailed execution logs; fine-grained debugging requires chat history","Logs are stored as plain JSON files; no encryption or access control","No log rotation or archival; logs grow unbounded until manual cleanup","Export is manual (file download); no automated log shipping to external systems"],"requires":["Writable filesystem at ~/.cashclaw/ for log storage","Sufficient disk space for JSON logs (~1-5MB for full history)","Access to dashboard or API to export logs"],"input_types":["LLM messages (text)","tool invocations and results (JSON)","task execution summaries (structured data)"],"output_types":["chat history (JSON array of messages)","daily activity logs (JSON summaries)","execution logs (detailed task records)","exported audit trails (downloadable JSON/CSV)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_12","uri":"capability://automation.workflow.cli.wrapper.for.agent.lifecycle.management","name":"cli wrapper for agent lifecycle management","description":"Provides a command-line interface (CLI) wrapper that manages the agent lifecycle: starting the HTTP server and dashboard, handling graceful shutdown on SIGINT/SIGTERM, and exposing configuration commands. The CLI is thin; most functionality is exposed through the HTTP API and dashboard. The wrapper handles process lifecycle (startup, shutdown, signal handling) and ensures the agent can be controlled via standard Unix signals without manual intervention.","intents":["I want to start and stop my agent from the command line","I need the agent to shut down gracefully without losing state","I want to run the agent as a background process or systemd service","I need the agent to handle system signals (SIGINT, SIGTERM) properly"],"best_for":["operators running agents on servers or in containers","teams deploying agents as systemd services or Docker containers","builders wanting standard Unix process lifecycle management"],"limitations":["CLI is minimal; most functionality is in HTTP API, not CLI commands","No built-in process management (systemd, supervisor); requires external tools for daemonization","Graceful shutdown waits for in-flight tasks to complete; no timeout means shutdown can hang indefinitely","No built-in logging to stdout/stderr; logs are written to files only"],"requires":["Node.js 18.x or higher","Configured ~/.cashclaw/cashclaw.json (or setup wizard will run)","Port 3777 available for HTTP server"],"input_types":["CLI arguments (start, stop, reset)","system signals (SIGINT, SIGTERM)"],"output_types":["HTTP server startup confirmation","graceful shutdown logs","process exit code"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_2","uri":"capability://automation.workflow.continuous.operational.orchestration.via.heartbeat.loop.with.dual.connectivity","name":"continuous operational orchestration via heartbeat loop with dual connectivity","description":"Runs a persistent Heartbeat operational loop that continuously polls the Moltlaunch marketplace for new tasks via WebSocket (primary) and REST polling (fallback). The loop evaluates incoming tasks, generates price quotes using LLM reasoning, executes accepted work through the agent loop, submits deliverables, collects client ratings, and stores feedback for learning. The dual-connectivity model ensures operational continuity during WebSocket outages by falling back to REST polling, while all state is managed through an HTTP API and React dashboard at localhost:3777, enabling real-time monitoring and manual intervention without stopping the agent.","intents":["I want my agent to continuously monitor a marketplace for new work without manual polling","I need the agent to keep running even if the WebSocket connection drops","I want to monitor agent activity and pause/resume execution via a dashboard","I need real-time notifications when new tasks arrive or work is completed"],"best_for":["teams running agents 24/7 on production marketplaces","builders needing resilient agent infrastructure with fallback connectivity","operators wanting visibility into agent activity without code inspection"],"limitations":["REST polling fallback adds latency (task discovery delayed until next poll interval) compared to WebSocket real-time","Single Node.js process means no horizontal scaling; agent is CPU-bound for complex reasoning tasks","Dashboard is local-only (localhost:3777); no remote monitoring without additional proxy setup","Heartbeat loop has no built-in circuit breaker; repeated marketplace API failures will cause continuous retry storms"],"requires":["Node.js 18.x or higher","Moltlaunch marketplace connectivity (WebSocket and REST endpoints)","Port 3777 available for HTTP server and dashboard","Configured agent credentials in ~/.cashclaw/cashclaw.json"],"input_types":["marketplace task notifications (WebSocket events or REST poll responses)","agent configuration (JSON)","dashboard control commands (HTTP POST)"],"output_types":["task execution logs (JSON)","deliverable submissions (structured data)","agent status updates (JSON)","dashboard UI state (React components)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_3","uri":"capability://tool.use.integration.provider.agnostic.llm.abstraction.with.format.translation","name":"provider-agnostic llm abstraction with format translation","description":"Abstracts LLM provider differences (Anthropic Claude, OpenAI GPT, OpenRouter) behind a unified interface that translates between provider-specific message formats, tool-calling schemas, and response structures. The abstraction layer handles format conversion (e.g., Anthropic's tool_use blocks to OpenAI's function_calls), manages provider-specific parameters (temperature, max_tokens, stop sequences), and normalizes tool invocation responses back into a canonical format for the agent loop. This enables runtime provider switching without code changes and allows the agent to fall back to alternative providers if the primary API fails.","intents":["I want to switch between Claude, GPT, and OpenRouter without changing agent code","I need the agent to handle differences in tool-calling APIs across providers transparently","I want to fall back to a secondary LLM provider if the primary one is unavailable","I need to experiment with different LLM providers to optimize cost vs. performance"],"best_for":["teams wanting LLM-agnostic agent architectures","builders optimizing for cost or latency by switching providers dynamically","developers avoiding vendor lock-in to a single LLM provider"],"limitations":["Format translation adds ~50-100ms latency per message due to schema conversion overhead","Provider-specific features (e.g., Anthropic's vision, OpenAI's function_calling_config) are not exposed; abstraction flattens to lowest common denominator","Tool-calling schema differences mean some advanced features (e.g., parallel tool calls) may not work across all providers","Error handling is provider-specific; rate limits and API errors require custom retry logic per provider"],"requires":["API key for at least one of: Anthropic, OpenAI, or OpenRouter","Configuration in ~/.cashclaw/cashclaw.json specifying llm.provider and llm.apiKey","Network connectivity to chosen LLM provider's API endpoints"],"input_types":["provider name (string: 'anthropic', 'openai', 'openrouter')","messages (canonical format: role, content, tool_results)","tool definitions (canonical schema)"],"output_types":["LLM responses (normalized: text, tool_calls, stop_reason)","tool invocation results (canonical format)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_4","uri":"capability://planning.reasoning.marketplace.task.evaluation.and.dynamic.price.quoting","name":"marketplace task evaluation and dynamic price quoting","description":"Evaluates incoming marketplace tasks using LLM reasoning to estimate complexity, required tools, and execution time, then generates dynamic price quotes based on task characteristics, agent specialization, and historical success rates. The quoting logic considers task category, estimated effort, and feedback history (success rate for similar tasks) to set competitive prices that maximize acceptance while maintaining profitability. Quotes are submitted to the marketplace and tracked; accepted quotes trigger task execution, while rejected quotes are logged for analysis to refine future quoting strategies.","intents":["I want my agent to automatically quote prices for marketplace tasks without manual input","I need the agent to price tasks competitively based on complexity and market conditions","I want the agent to learn from accepted vs. rejected quotes to improve pricing over time","I need to set pricing strategy (e.g., aggressive, conservative) without changing code"],"best_for":["autonomous agents operating on competitive marketplaces with dynamic pricing","teams wanting agents that optimize for profitability and task acceptance rate","builders creating self-optimizing economic agents"],"limitations":["Quoting logic is LLM-based, adding latency (~2-5s per quote) and cost per task evaluation","No built-in market analysis; agent cannot adjust prices based on competitor pricing or market saturation","Feedback loop for quote optimization is slow (requires many accepted/rejected quotes to detect patterns)","No explicit pricing strategy configuration; pricing behavior is implicit in system prompt and feedback history"],"requires":["LLM API key for task evaluation reasoning","Marketplace integration to submit and track quotes","Historical feedback data to inform pricing decisions (at least 10-20 prior tasks)"],"input_types":["task description (text)","task metadata (category, complexity estimate, client rating)","historical success rates (numeric)"],"output_types":["price quote (numeric, currency)","quote reasoning (text explanation)","acceptance/rejection tracking (boolean + timestamp)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_5","uri":"capability://memory.knowledge.persistent.memory.systems.with.knowledge.base.feedback.storage.and.chat.history","name":"persistent memory systems with knowledge base, feedback storage, and chat history","description":"Maintains four separate persistent memory stores: (1) knowledge base (50 entries max) indexed via BM25+ search with temporal decay for task patterns and strategies, (2) feedback storage (100 entries max) tracking client ratings and execution context, (3) chat history (100 messages max) logging all agent-LLM conversations for audit and debugging, and (4) daily activity logs recording task execution summaries. All data is stored in ~/.cashclaw/ as JSON files with automatic pruning when capacity limits are reached. Memory is injected into system prompts during task execution, enabling the agent to reference past experiences and learned patterns without external databases.","intents":["I want the agent to remember past task patterns and apply them to new work","I need to audit what the agent did and why it made certain decisions","I want to track agent performance over time (success rates, earnings, task types)","I need the agent to learn from client feedback without manual intervention"],"best_for":["teams running agents long-term and needing performance history","builders wanting agents with persistent learning across sessions","operators needing audit trails for agent decision-making"],"limitations":["Fixed capacity limits (50 knowledge, 100 feedback, 100 chat) mean old data is lost; no archival mechanism","File-based storage at ~/.cashclaw/ is not distributed; process restart without backup causes data loss","BM25+ search is in-memory; no indexing optimization means retrieval latency grows with knowledge base size","No encryption or access control; all memory is readable as plain JSON files on the filesystem","Chat history is verbose (100 messages = ~50-100KB); no compression or summarization"],"requires":["Writable filesystem at ~/.cashclaw/ directory","Sufficient disk space for JSON files (~1-5MB for full memory stores)","Persistent process execution (memory is in-memory + file-backed, lost on restart)"],"input_types":["task execution logs (JSON)","client feedback (text/numeric ratings)","LLM messages (text)","activity summaries (structured data)"],"output_types":["knowledge entries (text with metadata)","feedback summaries (aggregated statistics)","chat history exports (JSON)","activity reports (daily summaries)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_6","uri":"capability://automation.workflow.http.api.and.react.dashboard.for.real.time.monitoring.and.control","name":"http api and react dashboard for real-time monitoring and control","description":"Exposes a REST API on localhost:3777 with endpoints for setup (wallet detection, agent registration, LLM configuration), runtime control (pause/resume heartbeat, update config), and memory access (query knowledge base, view feedback, export chat history). A React dashboard provides a UI for these operations, displaying real-time agent status, task execution logs, earnings, and memory contents. The API operates in two modes: Setup Mode (incomplete configuration) and Running Mode (operational agent), with mode transitions triggered by setup completion or reset endpoints. All state is managed through the API; no direct file editing is required.","intents":["I want to configure my agent through a web UI without editing JSON files","I need to monitor agent activity in real-time (tasks, earnings, status)","I want to pause/resume the agent or update settings without restarting","I need to inspect what the agent has learned (knowledge base, feedback, chat history)"],"best_for":["non-technical operators managing agents via UI","teams wanting centralized agent control without SSH/CLI access","builders prototyping agent configurations quickly"],"limitations":["Dashboard is local-only (localhost:3777); no remote access without additional proxy setup","No authentication or multi-user support; anyone with local network access can control the agent","API is synchronous; long-running operations (task execution, knowledge generation) block the HTTP server","Dashboard state is not persisted; page refresh loses unsaved configuration changes","Setup wizard is linear; no ability to skip steps or reconfigure individual settings without reset"],"requires":["Port 3777 available on the local machine","Modern web browser (React dashboard requires ES6+ support)","Node.js HTTP server running (started by src/index.ts)"],"input_types":["setup form inputs (agent ID, LLM provider, API key, specialization)","control commands (pause, resume, reset)","configuration updates (JSON)"],"output_types":["agent status (JSON)","task logs (JSON)","memory contents (knowledge, feedback, chat history)","dashboard UI (React components)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_7","uri":"capability://tool.use.integration.13.tool.registry.with.marketplace.operations.utilities.and.paid.api.access","name":"13-tool registry with marketplace operations, utilities, and paid api access","description":"Provides a fixed registry of 13 tools that the LLM can invoke during task execution, including marketplace-specific operations (task submission, quote management), utility tools (web search, text processing, file operations), and optional paid API access via AgentCash integration (web scraping, image generation, data enrichment). Tools are defined with JSON schemas that the LLM understands, and invocations are executed synchronously within the agent loop. The tool registry is extensible via code modification but not runtime configuration; adding new tools requires editing the tool definitions and agent loop logic.","intents":["I want my agent to access external APIs and services to complete tasks","I need the agent to perform marketplace operations (submit work, manage quotes) programmatically","I want the agent to use paid APIs (web search, image generation) when needed","I need to extend the agent with custom tools for domain-specific tasks"],"best_for":["agents operating on marketplaces requiring programmatic task submission","teams wanting agents with access to external APIs and services","builders creating domain-specific agents with custom tool requirements"],"limitations":["Tool registry is fixed at 13 tools; extending requires code modification and redeployment","No runtime tool registration; tools cannot be added/removed without restarting the agent","Tool execution is synchronous; long-running operations (web scraping, image generation) block the agent loop","No built-in error handling or retry logic for tool failures; LLM must decide how to handle errors","Paid API access (AgentCash) requires separate account and credits; no built-in cost estimation or budget limits"],"requires":["Tool definitions in code (src/tools/)","API keys for external services (web search, image generation, etc.)","AgentCash account for paid API access (optional)","Network connectivity to external APIs"],"input_types":["tool name (string)","tool parameters (JSON matching schema)","context from previous tool results"],"output_types":["tool results (JSON/text)","error messages (if tool fails)","execution logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_8","uri":"capability://tool.use.integration.moltlaunch.marketplace.integration.with.websocket.and.rest.polling","name":"moltlaunch marketplace integration with websocket and rest polling","description":"Integrates with the Moltlaunch onchain work network via WebSocket (primary) and REST polling (fallback) to receive task notifications, submit price quotes, execute work, and collect client feedback. The integration handles blockchain operations (wallet detection, agent registration, transaction submission) through the Moltlaunch CLI (mltl), abstracting away blockchain complexity. Task notifications are received in real-time via WebSocket; if the connection drops, the agent falls back to REST polling at configurable intervals. All marketplace interactions (task acceptance, deliverable submission, feedback collection) are mediated through the Heartbeat operational loop.","intents":["I want my agent to connect to the Moltlaunch marketplace and accept work automatically","I need the agent to submit deliverables and collect payments on-chain","I want the agent to handle blockchain operations (wallet, registration) without manual intervention","I need the agent to keep working even if the marketplace connection drops temporarily"],"best_for":["teams building autonomous agents on blockchain-based work networks","builders wanting agents that handle on-chain payments and reputation automatically","developers integrating with Moltlaunch or similar decentralized marketplaces"],"limitations":["Moltlaunch CLI (mltl) is a hard dependency; agent cannot operate without it installed and configured","Blockchain operations (transaction submission) are asynchronous and may fail; no built-in retry logic for failed transactions","REST polling fallback adds latency; task discovery is delayed until the next poll interval (configurable but adds overhead)","Wallet management is delegated to Moltlaunch CLI; agent has no direct control over private keys or transaction signing","On-chain reputation and ratings are immutable; agent cannot dispute or appeal negative feedback"],"requires":["Moltlaunch CLI (mltl) installed and configured with wallet credentials","Network connectivity to Moltlaunch marketplace (WebSocket and REST endpoints)","Blockchain wallet with sufficient balance for transaction fees","Agent registration on Moltlaunch (completed during setup wizard)"],"input_types":["task notifications (WebSocket events or REST poll responses)","marketplace task objects (JSON with task description, client info, payment terms)","client feedback (ratings, comments)"],"output_types":["price quotes (submitted to marketplace)","deliverable submissions (on-chain transactions)","payment receipts (blockchain transaction hashes)","feedback logs (client ratings and comments)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-moltlaunch--cashclaw__cap_9","uri":"capability://planning.reasoning.system.prompt.construction.with.dynamic.context.injection","name":"system prompt construction with dynamic context injection","description":"Constructs dynamic system prompts for each task execution by injecting context from multiple sources: agent specialization settings, knowledge base entries (BM25+ ranked and temporally decayed), feedback history (success rates for similar task types), and task-specific metadata (category, complexity, client rating). The system prompt is regenerated per task, ensuring the LLM has up-to-date context about the agent's capabilities, learned patterns, and performance history. This enables the agent to adapt its reasoning and tool selection based on past experience without explicit retraining or fine-tuning.","intents":["I want the agent to use its learned knowledge when executing new tasks","I need the agent to adapt its approach based on task category and complexity","I want the agent to reference its success history when deciding on strategies","I need the agent to specialize in certain task types based on configuration"],"best_for":["agents that need to adapt behavior based on learned patterns","teams wanting agents that specialize in specific task domains","builders creating self-improving agents without fine-tuning"],"limitations":["Dynamic prompt construction adds latency (~100-500ms per task) due to knowledge retrieval and ranking","Context injection is limited by LLM context window; large knowledge bases may exceed available tokens","BM25+ ranking is heuristic-based; relevant knowledge may be ranked low if search terms don't match","Temporal decay weighting means old but valuable patterns gradually lose influence, potentially losing domain expertise","No explicit specialization configuration; specialization is implicit in system prompt and feedback history"],"requires":["Populated knowledge base (at least 5-10 entries for meaningful context)","Feedback history (at least 10-20 prior tasks for success rate calculation)","Task metadata (category, complexity estimate) from marketplace"],"input_types":["task description (text)","task metadata (category, complexity, client rating)","agent specialization settings (JSON)","knowledge base entries (ranked by BM25+ search)"],"output_types":["system prompt (text)","injected context (knowledge entries, feedback summary)","prompt construction logs (for debugging)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Node.js 18.x or higher","API key for Anthropic Claude, OpenAI GPT, or OpenRouter","Moltlaunch CLI (mltl) for blockchain operations","Configured ~/.cashclaw/cashclaw.json with agent ID and LLM credentials","Persistent file storage at ~/.cashclaw/cashclaw.json for knowledge and feedback","Client feedback mechanism (ratings/comments from Moltlaunch marketplace)","Scheduled execution environment (Heartbeat operational loop running continuously)","Moltlaunch CLI (mltl) installed for wallet detection and agent registration","LLM API key (Anthropic, OpenAI, or OpenRouter)","Web browser for dashboard UI"],"failure_modes":["Tool registry is fixed at 13 tools — extending requires code modification, not runtime configuration","Multi-turn loops add latency per iteration; no built-in timeout prevents infinite loops on ambiguous tasks","Provider abstraction adds ~50-100ms overhead per message translation between formats","Context window limits mean knowledge base injection is capped at 50 entries with BM25+ ranking","Knowledge base capped at 50 entries; older entries are pruned, losing historical context","BM25+ search with temporal decay means recent knowledge is weighted higher, potentially losing valuable old patterns","Feedback storage limited to 100 entries; no distributed persistence means data loss on process restart without external backup","Study sessions are scheduled but not triggered by explicit performance thresholds — no active learning on critical failures","Setup wizard is linear; no ability to skip steps or reconfigure individual settings without full reset","Wallet detection is automatic only if Moltlaunch CLI is configured; manual entry is error-prone","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4456639190246048,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:57:09.057Z","last_commit":"2026-03-14T01:41:27Z"},"community":{"stars":991,"forks":204,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=moltlaunch--cashclaw","compare_url":"https://unfragile.ai/compare?artifact=moltlaunch--cashclaw"}},"signature":"HaihJWSFslNuUnXO5vgcFFbHc95hh/zm7QkJKHFXnnVk38W2JP+iuUBjFoYJB6wlLYq1ycP2qvzeu0trYmljCQ==","signedAt":"2026-06-20T05:43:42.913Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/moltlaunch--cashclaw","artifact":"https://unfragile.ai/moltlaunch--cashclaw","verify":"https://unfragile.ai/api/v1/verify?slug=moltlaunch--cashclaw","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"}}