Daniel Avila

Turn Figma designs into React and TypeScript components using coderio. The workflow uses design thumbnails to guide layout, extracts component structure and data, and generates code and assets for integration into an application.

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• Build a React interface from a Figma design
• Organize component structure and input data
• Generate components while checking the design thumbnail

Manage the limited information an LLM can process at once. Use importance-based summaries, selective trimming, token counting, and content placement to plan what stays in context and address information loss during long conversations.

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• Planning how to handle conversations near the token limit
• Summarizing a long dialogue while preserving key information
• Reviewing the placement of important instructions and facts

Send signed messages between AI agents, check inboxes, and reply to requests. Supports file attachments, message types, and priorities, with basic messaging running locally and separate identities for each agent.

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• Sending work requests to another agent
• Checking an agent inbox and replying
• Handing off context with attached files

Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.

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• Investigating errors in production
• Comparing error counts across time periods
• Following request traces across services

Get guidance on distributed training with PyTorch FSDP, which splits model parameters across devices. The skill covers implementation, mixed precision, moving data to CPU memory, communication setup, and debugging distributed training code.

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• Implement distributed training with FSDP
• Review mixed precision and CPU offloading settings
• Choose a distributed communication backend

Design collaborative AI teams with CrewAI by defining each agent’s role, goals, tasks, and expected results. Covers passing work between agents, manager-led coordination, memory settings, and execution planning for workflows such as research and writing.

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• Build a team that researches and writes articles
• Coordinate specialists through a manager agent
• Define agent roles and expected task outputs

Design memory systems that help AI agents retrieve relevant past information. Choose short-term and long-term memory structures, compare vector storage options, and review document splitting and retrieval strategies for different kinds of knowledge and experience.

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• Design memory for information used across conversations
• Choose storage for experiences, facts, and procedures
• Review retrieval when an agent misses saved information

Search automatically generated code documentation to check function parameters, class definitions, API details, and comments before making changes. Find relevant documents by meaning, exact keywords, or document type, then retrieve their full contents.

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• Check function parameters before implementation
• Find documentation about authentication
• List class and component documentation

Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.

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• Training a smaller model from a larger teacher
• Fine-tuning with teacher-generated responses
• Distilling domain knowledge into a specialized model

Search past conversations to recover decisions, discussions, and context across sessions. Find related ideas by meaning, match exact terms, or locate code identifiers, with options to search only user or assistant messages through local AI Maestro tools.

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• Recalling decisions before resuming work
• Finding discussions with different wording
• Looking up conversations about a function or class

Adapt an AI agent’s approach to brainstorming, implementation, debugging, review, teaching, or release preparation. Defines how each mode should ask questions, prioritize work, and present results, with automatic mode selection and explicit switching examples.

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• Explore several ideas before implementation
• Investigate a bug through hypotheses and checks
• Organize review findings by severity

Helps configure and troubleshoot DeepSpeed for training large models across multiple devices. Covers memory optimization, parallel training, and DeepNVMe storage transfers, with examples for tuning data movement and configuring models that divide work among specialized components.

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• Configuring distributed training for large models
• Tuning tensor transfers to NVMe storage
• Setting up models with specialized expert components

Combine capabilities from multiple fine-tuned LLMs without retraining using mergekit. Choose a merging method, configure how models are blended, and evaluate the resulting model on tasks such as math, code generation, and conversation.

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• Combining math, coding, and conversation capabilities
• Blending two models at different ratios
• Comparing variants made with different merging methods

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

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• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

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• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

Configure Claude Code and improve how you assign coding tasks. This guide provides a CLAUDE.md template, practical prompting examples, debugging approaches using logs, and a workflow for making and checking small changes.

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• Set up project guidelines in CLAUDE.md
• Write clearer requests for coding tasks
• Investigate unexpected agent behavior using logs

Collect daily AI release news from major labs, product changelogs, blogs, and Hacker News. Remove previously reported items and email a digest with source links and brief explanations tailored to the intended audience.

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• Receiving a daily AI release digest
• Tracking changes to AI development tools
• Sharing relevant AI updates with a team

Develop Garry's Mod addons with guidance on Lua scripts, file organization, assets, and packaging. Covers server and client execution contexts, API lookup, and debugging, with in-game testing included in the workflow.

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• Planning a new Garry's Mod addon
• Writing Lua scripts for server and client contexts
• Organizing addon assets and directories

Design tools that AI agents can understand and call correctly. Covers JSON Schema, a format for defining inputs, along with clear descriptions, input examples, validation, and error responses that help agents recover from failures.

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• Designing inputs for an AI agent tool
• Improving tool descriptions and input examples
• Reviewing validation and error responses

Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.

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• Build a research-to-writing agent workflow
• Coordinate specialists through a manager agent
• Create workflows with conditional branches

Design autonomous coding agents using implementation patterns for tool calls, permission checks, browser interaction, and saved task state. Code examples and checklists help structure agent behavior and workflows that include human approval.

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• Designing a coding agent's execution loop
• Defining tool interfaces for files and commands
• Adding permissions and human approval steps

Split independent debugging or repair tasks among multiple AI agents. Define a focused scope, constraints, and expected report for each agent, then review their changes, check for conflicts, and test the combined result.

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• Investigating unrelated failures across test files
• Assigning separate subsystems to different agents
• Writing focused tasks with clear constraints

Research markets, competitors, companies, and technical topics using multiple sources in Google NotebookLM. Produce a structured brief with key findings and recommended actions, with optional slides, podcasts, videos, infographics, reports, or mind maps.

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• Researching market trends and business opportunities
• Comparing competitors and their positioning
• Researching prospects before a proposal

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

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• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent