Bojie Li

Find facts and data with web search, prioritizing primary or authoritative sources. Record exact figures, units, time periods, and source URLs in shared history so later calculation, coding, or writing can use traceable evidence.

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• Finding facts backed by primary or authoritative sources
• Recording figures with units, time periods, and source URLs
• Collecting evidence before calculation or writing

Design how an AI agent finds and loads tools as needed. Compare discovery approaches, organize large tool collections, and assess tool selection accuracy, token costs, and cache reuse without placing every tool definition in the initial context.

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• Designing on-demand loading for many MCP tools
• Comparing tool discovery approaches
• Investigating cache misses after loading tools

Design a running status summary that keeps an agent aware of tasks, tool call counts, time, errors, and its environment. The skill covers updating that summary and balancing cache reuse against the buildup of outdated information.

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• Addressing repeated tool calls and retry loops
• Keeping TODO progress and original goals visible
• Choosing how to update runtime status messages

Design how an AI agent trims long conversations and large tool outputs while retaining decisions, constraints, failed approaches, and sources. Compare summarization strategies, choose compression triggers, and consider separate subagents to keep intermediate information out of the main conversation.

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• Managing large search results and tool outputs
• Preserving decisions and constraints during summarization
• Choosing compression triggers and batch processing

Turn verified facts and calculation results from the shared conversation into a finished draft. Follow the requested language, audience, structure, and length while retaining necessary sources and qualifications without inventing citations or numbers.

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• Turning verified research into a report
• Writing an explanation for a specific audience
• Drafting within a requested structure and length

Estimate cross-server traffic, network bottlenecks, and synchronization waits for AI training or inference. Map communication onto physical network paths and assess how topology or communication changes affect the time needed to complete the job.

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• Find communication bottlenecks in distributed AI workloads
• Compare network topologies and communication paths
• Estimate synchronization waits under concurrent traffic

Analyze GPU operation timings and execution traces to find costly data access, repeated computation, launch overhead, and synchronization waits. Check whether improving an individual operation actually reduces the time needed to complete a full request.

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• Investigating slow GPU operations
• Assessing whether combining operations reduces data access
• Checking pipeline dependencies and buffer reuse

Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.

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• Comparing batching and request scheduling options
• Checking memory capacity for concurrent requests
• Assessing cache reuse and offloading tradeoffs

Estimate how model architecture, precision, and context length affect weight storage, generation state, computation, and memory traffic. Compare models and attention designs with separate resource estimates for input processing and token generation.

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• Compare memory needs across model architectures
• Estimate resource costs of longer contexts
• Compare attention designs and expert-based models

Plan when an agent’s tool environments are created, kept available, released, and restored. Estimate peak concurrency, resource use during work and waiting, total task duration, and cost across model calls, tool execution, validation, and retries.

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• Estimating peak concurrent agent environments
• Comparing environment retention and recreation
• Assessing cold starts and shared environment pools

Model ongoing requests and multi-step agent work to estimate capacity, latency, and cost per completed task. Break execution into stages, account for queues and retries, and produce workload tables with average and peak resource budgets.

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• Estimate capacity for ongoing AI requests
• Analyze queues and tool waits in agent workflows
• Compare resource costs per completed task

Estimate whether a model execution fits the available hardware and identify likely performance limits. Separate memory capacity, computation, data movement, and sequential waiting to produce a table of assumptions, ideal minimum times, unknowns, and measurement priorities.

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• Checking whether a model execution fits the hardware
• Estimating ideal computation and data transfer times
• Assessing the overall benefit of a local optimization

Compare computing devices for a defined AI model workload using memory capacity, computing speed, data transfer, energy use, and cost. Build a comparison that separates predicted performance from measurements and identifies the constraints that determine the choice.

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• Choosing hardware for a specific AI model
• Checking whether a model fits in device memory
• Explaining gaps between advertised and measured performance

Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.

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• Compare replicas with separate inference stages
• Plan routing around compute capacity and cached state
• Evaluate expert placement and load imbalance

Estimate GPU memory, training step time, checkpoint costs, and recovery overhead for training or reinforcement learning. Compare state partitioning, recomputation, offloading, and device counts to assess whether a job can finish within its deadline.

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• Check whether training can finish by a deadline
• Compare memory-saving methods and their time costs
• Plan checkpoint intervals and recovery overhead

Plan how to split AI models and workloads across multiple GPUs. Compare memory needs, computation, communication, and waiting time to estimate completion times and choose a layout suited to response time, throughput, or cost goals.

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• Running models that exceed a single GPU's memory
• Comparing ways to split work across GPUs
• Estimating communication costs within and between servers

Compare running models or tasks on a device, nearby edge infrastructure, or in the cloud. Break down interaction delays, data transfers, energy use, costs, and failure recovery to assess deployment choices for multimodal and real-time agents.

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• Comparing device, edge, and cloud execution
• Estimating interaction delays on unstable networks
• Assessing battery use and sustainable processing speed

Find chats by name, read or search WeChat messages with the local GreenBubbles CLI. Use for bounded live or snapshot queries; route setup problems to greenbubbles-setup and maintained notes to greenbubbles-personal-memory.

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Build or revise private, cited Markdown notes from the user's WeChat history in the current agent session. Use for a maintained personal wiki or incremental notes; use greenbubbles-context for a one-off lookup or summary.

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Install or diagnose the local GreenBubbles CLI and read-only WeChat access. Use for a missing CLI, failed database access, source selection, or initial owner-operated key capture.

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