{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-babycatagi","slug":"babycatagi","name":"BabyCatAGI","type":"agent","url":"https://replit.com/@YoheiNakajima/BabyCatAGI","page_url":"https://unfragile.ai/babycatagi","categories":["ai-agents"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-babycatagi__cap_0","uri":"capability://planning.reasoning.objective.to.task.list.decomposition.with.single.pass.planning","name":"objective-to-task-list decomposition with single-pass planning","description":"Converts a natural language objective into a discrete task list via a single LLM call to OpenAI API. The Task Creation Agent parses the objective once at initialization, generating a flat task sequence without iterative refinement or user feedback loops. Tasks are stored in-memory and executed sequentially, with no dynamic reordering or priority adjustment based on intermediate results.","intents":["break down a complex research goal into actionable steps without manual planning","automatically generate a task list from a vague objective statement","avoid manual task decomposition for repetitive workflows"],"best_for":["developers prototyping lightweight agentic systems","automation enthusiasts wanting minimal overhead task planning","researchers testing BabyAGI-pattern implementations"],"limitations":["single-pass decomposition — no iterative refinement if initial task list is suboptimal","no task prioritization or reordering based on dependencies discovered during execution","task list explosion risk for complex objectives exceeding context window","no validation that generated tasks are actually executable or well-formed"],"requires":["OpenAI API key with access to gpt-3.5-turbo or gpt-4 (model selection unknown)","Python 3.7+ runtime environment","Replit account or local Python environment"],"input_types":["natural language objective (text string, 10-500 words typical)"],"output_types":["task list (array of task descriptions, format unspecified)","task metadata (dependencies, execution order)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_1","uri":"capability://automation.workflow.sequential.task.execution.with.tool.based.action.dispatch","name":"sequential task execution with tool-based action dispatch","description":"Executes tasks one-at-a-time in order through a synchronous loop that dispatches each task to available tools (search_tool or text_completion). The Execution Agent maintains task context by pulling relevant outputs from previously completed tasks and passing them as input to downstream tasks. No parallelization, checkpointing, or mid-execution recovery — if execution fails, the entire workflow must restart.","intents":["run a multi-step research or content generation workflow end-to-end","chain task outputs as inputs to dependent tasks automatically","execute tasks with access to external tools (web search, LLM completion)"],"best_for":["solo developers building lightweight research automation","teams prototyping agentic workflows before scaling to production","users with simple, linear task dependencies"],"limitations":["sequential execution only — no parallel task processing, creating bottleneck for large task lists","single-threaded — cannot make concurrent API calls to OpenAI or SerpAPI","no checkpointing — failure mid-execution requires full restart from task 1","no task timeout or resource limits — long-running tasks block entire workflow","cumulative latency scales linearly with task count (typical: 30-60 seconds per task)"],"requires":["OpenAI API key (required)","SerpAPI key (optional, required only if search_tool is used)","Python 3.7+ with requests library for HTTP calls","Replit environment or local Python runtime"],"input_types":["task description (text string)","task context (outputs from previous tasks, format unspecified)","tool selection (implicit based on task content)"],"output_types":["task result (text, format varies by tool used)","task metadata (execution status, timestamp, tool used)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_10","uri":"capability://automation.workflow.synchronous.single.threaded.execution.with.cumulative.latency","name":"synchronous single-threaded execution with cumulative latency","description":"Tasks execute sequentially in a single-threaded loop with no parallelization or concurrent API calls. Each task waits for completion before the next task starts. Latency accumulates linearly with task count (typical: 30-60 seconds per task). No timeout mechanism or resource limits per task. Entire workflow blocks until completion or failure.","intents":["run multi-step workflows without managing concurrency or async code","execute tasks in strict order with clear dependencies","keep execution logic simple and deterministic"],"best_for":["developers wanting simple, deterministic execution without async complexity","workflows with strict task ordering and dependencies","teams prototyping agentic systems with small task lists (< 10 tasks)"],"limitations":["sequential execution only — no parallel task processing, creating bottleneck","single-threaded — cannot make concurrent API calls, increasing total latency","cumulative latency — scales linearly with task count (10 tasks ≈ 5-10 minutes)","no timeout per task — long-running tasks block entire workflow","no resource limits — tasks can consume unbounded memory or API quota","blocking I/O — network calls to OpenAI/SerpAPI block entire execution","no interruptibility — cannot pause or cancel mid-execution without killing process"],"requires":["Python 3.7+ with synchronous execution model","Replit environment or local Python runtime","Patience for cumulative latency (typical: 5-10 minutes for 10-task workflow)"],"input_types":["task queue (ordered list of tasks)","task context (previous results)"],"output_types":["task result (output from tool execution)","execution status (success/failure)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_11","uri":"capability://safety.moderation.unknown.error.handling.and.failure.recovery","name":"unknown error handling and failure recovery","description":"Error handling strategy is not documented. Unknown behavior when OpenAI API fails, SerpAPI quota exceeded, network timeout occurs, or task execution fails. No retry logic, fallback mechanisms, or graceful degradation mentioned. Likely causes entire workflow to fail with unknown error message.","intents":["understand what happens when API calls fail or timeout","know whether workflows can recover from transient failures","predict workflow behavior in unreliable network conditions"],"best_for":["developers building experimental prototypes where failure is acceptable","teams testing agentic patterns in controlled environments","users with reliable network and API quota"],"limitations":["error handling unknown — no documentation on failure modes","no retry logic — transient failures likely cause workflow failure","no fallback mechanisms — no alternative tools or models if primary fails","no graceful degradation — unknown if partial results returned on failure","no error messages — unclear what information is provided on failure","no timeout handling — unknown behavior if API calls hang","no rate limit handling — unknown behavior if API quota exceeded"],"requires":["OpenAI API availability and quota","SerpAPI availability and quota (if search_tool used)","Network connectivity without interruptions"],"input_types":["API responses (success or failure)","network conditions (latency, packet loss)"],"output_types":["error message (format unknown)","execution status (success/failure, unknown detail)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_12","uri":"capability://automation.workflow.openai.api.cost.exposure.with.unknown.per.execution.pricing","name":"openai api cost exposure with unknown per-execution pricing","description":"BabyCatAGI incurs per-token charges from OpenAI API for Task Creation Agent, task execution completions, and mini-agent calls. Exact cost per execution is unknown because model selection (gpt-3.5-turbo vs gpt-4), token counting, and prompt engineering are not documented. SerpAPI charges apply if search_tool is used (unknown search frequency per execution). Replit hosting adds additional costs (free tier has unknown daily credit limits; paid tiers: $20-95/month).","intents":["understand cost structure and budget implications of running BabyCatAGI","estimate total cost of ownership for agentic workflows","compare cost vs alternatives (manual research, other automation tools)"],"best_for":["developers evaluating cost-benefit of agentic automation","teams budgeting for AI infrastructure and API usage","users with cost-sensitive applications"],"limitations":["model selection unknown — cannot estimate cost without knowing gpt-3.5-turbo vs gpt-4","token counting not exposed — users cannot predict cost before execution","search frequency unknown — SerpAPI cost depends on unknown search count per execution","Replit free tier limits unknown — daily credit limits not specified","no cost estimation tool — users cannot forecast budget impact","no cost controls — no way to set spending limits or alerts","cumulative cost risk — long task lists or complex objectives may incur unexpected charges"],"requires":["OpenAI API account with payment method","SerpAPI account with payment method (if search_tool used)","Replit account (free or paid tier)"],"input_types":["task count (affects OpenAI API calls)","task complexity (affects token usage)","search frequency (affects SerpAPI charges)"],"output_types":["OpenAI API charges (per-token, unknown rate)","SerpAPI charges (per-search, unknown rate)","Replit hosting charges (monthly subscription or free tier)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_2","uri":"capability://search.retrieval.web.search.with.integrated.scraping.and.chunking.pipeline","name":"web search with integrated scraping and chunking pipeline","description":"The search_tool combines three operations into a single pipeline: (1) query SerpAPI to retrieve search results, (2) scrape web content from top results, (3) chunk text into segments for LLM processing. Chunks are extracted and passed to the text_completion tool for information synthesis. Implementation details of scraping library, chunk size, and overlap strategy are unknown; likely uses simple HTTP requests + regex or BeautifulSoup for parsing.","intents":["search the web for information relevant to a task without manual query crafting","automatically extract and process web content without user intervention","synthesize information from multiple web sources into task results"],"best_for":["developers building research automation workflows","users automating information gathering for content creation","teams prototyping search-augmented LLM applications"],"limitations":["SerpAPI dependency — no alternative search providers (Google, Bing, DuckDuckGo not supported)","unknown chunk size and overlap strategy — may lose context at chunk boundaries","no deduplication of search results — may process duplicate content from multiple sources","no content quality filtering — scrapes all results equally regardless of relevance or reliability","rate limiting unknown — behavior when SerpAPI quota exceeded is unspecified","scraping robustness unknown — may fail on JavaScript-heavy or anti-scraping sites"],"requires":["SerpAPI API key (free tier: 100 searches/month; paid tiers available)","OpenAI API key (for text_completion tool to process chunks)","Python 3.7+ with requests library","Network connectivity to SerpAPI and target websites"],"input_types":["search query (text string, auto-generated from task description)","number of results to process (unknown default, likely 5-10)"],"output_types":["extracted text chunks (array of text segments)","chunk metadata (source URL, position in document, unknown other fields)","synthesized result (text summary from text_completion tool)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_3","uri":"capability://memory.knowledge.task.output.context.chaining.for.downstream.task.input","name":"task-output context chaining for downstream task input","description":"Maintains an in-memory task result store and automatically retrieves relevant outputs from completed tasks to pass as context to downstream tasks. The system tracks which tasks have executed and pulls their results based on task dependencies (mechanism for determining relevance unknown — likely keyword matching or explicit dependency declarations). No explicit dependency graph — relies on task ordering from initial decomposition.","intents":["automatically pass results from one task as input to the next without manual data wrangling","build multi-step workflows where each task builds on previous results","avoid re-querying or re-processing information already gathered in earlier tasks"],"best_for":["developers building linear research workflows with clear task dependencies","automation enthusiasts wanting minimal manual context management","teams prototyping agentic workflows with simple task chains"],"limitations":["dependency detection mechanism unknown — may miss relevant context or include irrelevant results","no explicit dependency declaration — users cannot specify which tasks feed into which","in-memory only — no persistence across execution sessions, results lost if process crashes","no circular dependency detection — unknown behavior if tasks reference each other","context window overflow risk — accumulating task outputs may exceed LLM context limits for long chains","no context pruning — all previous results passed to each task regardless of relevance"],"requires":["Python 3.7+ with in-memory data structures (dict/list)","Replit environment or local Python runtime","OpenAI API key (for downstream task execution)"],"input_types":["task description (text string)","task index (position in execution sequence)","previous task results (text, format varies)"],"output_types":["augmented task input (original task description + relevant context from previous tasks)","task result (output from tool execution)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_4","uri":"capability://text.generation.language.direct.llm.text.completion.with.openai.api.integration","name":"direct llm text completion with openai api integration","description":"Provides a text_completion tool that sends task descriptions and context to OpenAI API for generation of task results. Tool wraps OpenAI API calls with implicit prompt engineering (exact prompts unknown) and returns raw LLM output. No output validation, fact-checking, or structured extraction — results are passed directly to task result store or final summary.","intents":["generate text completions for tasks that don't require external information (synthesis, writing, analysis)","leverage LLM reasoning for task execution without custom prompt engineering","integrate LLM capabilities into agentic workflows without manual API calls"],"best_for":["developers building content generation workflows","teams prototyping LLM-based automation without custom prompt engineering","users wanting simple text generation without structured output requirements"],"limitations":["model selection unknown — no control over gpt-3.5-turbo vs gpt-4 selection","no output validation — hallucinations, factual errors, or malformed output passed through unchanged","no structured extraction — results are raw text, not JSON or other structured formats","no temperature or sampling control exposed — uses OpenAI defaults (unknown values)","no token counting — users cannot estimate cost or context window usage","no retry logic — API failures cause task failure with unknown error handling","prompt engineering hidden — users cannot inspect or customize prompts used"],"requires":["OpenAI API key with access to gpt-3.5-turbo or gpt-4 (model unknown)","Python 3.7+ with openai library (version unknown)","Network connectivity to OpenAI API"],"input_types":["task description (text string)","task context (previous task outputs, format unspecified)","implicit system prompt (unknown content)"],"output_types":["generated text (raw LLM output, no structure)","token usage metadata (unknown if exposed)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_5","uri":"capability://text.generation.language.final.summary.report.generation.from.task.results","name":"final summary report generation from task results","description":"Aggregates all task results from the execution loop into a single summary_report output. Mechanism for aggregation unknown — likely concatenation or simple templating. Report is returned to user as final artifact without further processing, validation, or formatting. No structured output format specified; likely plain text or markdown.","intents":["collect all task results into a single deliverable for user review","present workflow results in a readable format without manual aggregation","provide a final artifact that summarizes the entire execution"],"best_for":["users wanting a single output document from multi-task workflows","teams prototyping agentic systems and needing quick result review","developers building research automation where final output is text-based"],"limitations":["aggregation strategy unknown — may produce poorly formatted or redundant output","no deduplication — if multiple tasks produce similar results, all are included","no ranking or filtering — all task results included regardless of relevance or quality","no structured output — cannot be parsed programmatically (e.g., JSON, XML)","no formatting control — users cannot customize report structure or style","no metadata — unknown if report includes task names, timestamps, or execution details"],"requires":["completed execution of all tasks in the workflow","Python 3.7+ runtime","Replit environment or local Python environment"],"input_types":["task results array (text outputs from all executed tasks)","task metadata (unknown fields)"],"output_types":["summary_report (text, format unspecified — likely plain text or markdown)","report metadata (unknown fields)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_6","uri":"capability://automation.workflow.replit.cloud.execution.environment.with.api.key.management","name":"replit cloud execution environment with api key management","description":"Runs BabyCatAGI in a user's private Replit environment, handling Python runtime, dependency installation, and API key storage. Users fork the public Replit, set environment variables for OpenAI and SerpAPI keys, and execute the workflow via the Replit IDE. Execution is synchronous and single-threaded; state is in-memory and lost on process termination. No persistent storage, logging, or monitoring beyond Replit's built-in console output.","intents":["run agentic workflows without local Python setup or infrastructure provisioning","quickly fork and customize BabyCatAGI for personal use without cloning/deploying","access cloud execution environment with minimal DevOps overhead"],"best_for":["developers wanting quick experimentation without local setup","non-technical users comfortable with Replit IDE but not command-line tools","teams prototyping agentic systems in a shared cloud environment"],"limitations":["Replit free tier has unknown daily credit limits — may be insufficient for regular use","no persistent state — results lost if Replit session terminates or times out","no logging or audit trail — execution details not saved for debugging or compliance","Replit-specific pricing — costs include both Replit credits and OpenAI/SerpAPI charges","no monitoring or alerting — users cannot track execution status or failures in real-time","execution timeout unknown — long-running workflows may be terminated by Replit","no horizontal scaling — cannot parallelize across multiple Repls without manual setup"],"requires":["Replit account (free or paid tier)","OpenAI API key (stored as Replit environment variable)","SerpAPI key (optional, stored as Replit environment variable)","Web browser with access to Replit.com"],"input_types":["OBJECTIVE variable (text string, set in Replit environment)","API keys (environment variables: OPENAI_API_KEY, SERPAPI_API_KEY)"],"output_types":["console output (task execution logs, format unspecified)","summary_report (final aggregated results)","execution status (success/failure, unknown detail level)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_7","uri":"capability://planning.reasoning.mini.agent.tool.for.nested.task.execution","name":"mini-agent tool for nested task execution","description":"A tool available within the task execution loop that allows tasks to spawn sub-agents for nested task decomposition and execution. Implementation details are unknown — unclear whether mini-agents use the same Task Creation + Execution pattern, how they communicate results back to parent tasks, or whether they can spawn further nested agents. Likely used for complex tasks that benefit from sub-decomposition.","intents":["decompose complex tasks into sub-tasks without modifying the main task list","execute nested workflows for tasks requiring multi-step reasoning","handle task complexity that exceeds single LLM completion capability"],"best_for":["developers building workflows with complex, multi-step tasks","teams needing hierarchical task decomposition without explicit DAG management","users automating workflows where some tasks require sub-planning"],"limitations":["implementation unknown — no documentation on mini-agent behavior, API, or limitations","nesting depth unknown — unclear if mini-agents can spawn further mini-agents","communication protocol unknown — how results flow back to parent task is unspecified","error handling unknown — behavior if mini-agent fails is unspecified","cost implications unknown — unclear if mini-agent execution incurs additional OpenAI API calls","context isolation unknown — whether mini-agents have access to parent task context","no examples provided — users must reverse-engineer usage from code"],"requires":["OpenAI API key (for mini-agent LLM calls)","SerpAPI key (optional, if mini-agent uses search_tool)","Python 3.7+ runtime","Replit environment or local Python environment"],"input_types":["task description (text string)","parent task context (unknown format)","mini-agent configuration (unknown parameters)"],"output_types":["mini-agent result (format unknown)","sub-task results (unknown structure)","execution metadata (unknown fields)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_8","uri":"capability://automation.workflow.variable.based.objective.input.with.no.ui.abstraction","name":"variable-based objective input with no ui abstraction","description":"Users set the OBJECTIVE variable directly in Replit code (or environment variables) to specify the workflow goal. No web UI, form, or interactive prompt — requires code modification or environment variable setting. Objective is passed to Task Creation Agent as-is without validation, parsing, or clarification. Single objective per execution; no multi-objective support.","intents":["specify a workflow goal without interactive prompts or UI forms","quickly modify objectives by editing code or environment variables","integrate BabyCatAGI into scripts or automation pipelines"],"best_for":["developers comfortable with code modification and environment variables","teams integrating BabyCatAGI into existing automation scripts","users wanting lightweight input mechanism without UI overhead"],"limitations":["no input validation — malformed or ambiguous objectives passed to LLM unchanged","no interactive clarification — users cannot refine objective after seeing initial task list","code modification required — non-technical users cannot change objectives without developer help","no multi-objective support — single objective per execution","no objective history — previous objectives not tracked or reusable","no objective templates — users must write objectives from scratch each time"],"requires":["Replit account with write access to code","Python 3.7+ knowledge (to modify OBJECTIVE variable)","Understanding of environment variable syntax (if using env vars)"],"input_types":["OBJECTIVE string (text, 10-500 words typical)","environment variables (OPENAI_API_KEY, SERPAPI_API_KEY)"],"output_types":["parsed objective (passed to Task Creation Agent)","task list (generated from objective)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-babycatagi__cap_9","uri":"capability://automation.workflow.in.memory.execution.state.with.no.persistence.or.checkpointing","name":"in-memory execution state with no persistence or checkpointing","description":"All task results, execution state, and intermediate outputs are stored in Python memory (dict/list data structures) during execution. No database, file storage, or external state management. If execution is interrupted (process crash, timeout, user termination), all state is lost and workflow must restart from task 1. No checkpointing mechanism to resume from failure point.","intents":["run lightweight workflows without database or persistence infrastructure","keep execution state in-memory for fast access during task chaining","avoid persistence overhead for short-lived, experimental workflows"],"best_for":["developers running short-lived experiments (< 10 minutes)","teams prototyping agentic systems without production requirements","users with simple workflows where restart cost is acceptable"],"limitations":["no persistence — results lost if process terminates unexpectedly","no checkpointing — failure mid-execution requires full restart","no recovery mechanism — no way to resume from failure point","memory constraints — large task lists or long execution may exhaust available memory","no audit trail — execution history not saved for debugging or compliance","no state inspection — users cannot query execution state during or after run","no distributed execution — state cannot be shared across multiple processes"],"requires":["Python 3.7+ with sufficient available memory (unknown minimum)","Replit environment or local Python runtime","Continuous process execution (no interruptions)"],"input_types":["task results (text, format varies)","task metadata (execution order, status)"],"output_types":["in-memory state dict (not exposed to users)","summary_report (final aggregated results)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["OpenAI API key with access to gpt-3.5-turbo or gpt-4 (model selection unknown)","Python 3.7+ runtime environment","Replit account or local Python environment","OpenAI API key (required)","SerpAPI key (optional, required only if search_tool is used)","Python 3.7+ with requests library for HTTP calls","Replit environment or local Python runtime","Python 3.7+ with synchronous execution model","Patience for cumulative latency (typical: 5-10 minutes for 10-task workflow)","OpenAI API availability and quota"],"failure_modes":["single-pass decomposition — no iterative refinement if initial task list is suboptimal","no task prioritization or reordering based on dependencies discovered during execution","task list explosion risk for complex objectives exceeding context window","no validation that generated tasks are actually executable or well-formed","sequential execution only — no parallel task processing, creating bottleneck for large task lists","single-threaded — cannot make concurrent API calls to OpenAI or SerpAPI","no checkpointing — failure mid-execution requires full restart from task 1","no task timeout or resource limits — long-running tasks block entire workflow","cumulative latency scales linearly with task count (typical: 30-60 seconds per task)","sequential execution only — no parallel task processing, creating bottleneck","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.25,"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":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=babycatagi","compare_url":"https://unfragile.ai/compare?artifact=babycatagi"}},"signature":"8fx/eohxnwKhH/rT79e1eT1Eutd+UFJiK/aOuxfWucWFn0NnLo8iHamSCatLebGl9mEII0Xl1qdejogzDctaDw==","signedAt":"2026-06-20T09:39:08.452Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/babycatagi","artifact":"https://unfragile.ai/babycatagi","verify":"https://unfragile.ai/api/v1/verify?slug=babycatagi","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"}}