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

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

Design tests that assess an AI agent's capabilities and reliability. Covers repeated runs to measure variation, checks for required behavior, and adversarial testing to expose failures that ordinary successful examples may miss.

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• Measuring variation across repeated runs of the same test
• Checking whether an agent follows required behavior
• Testing responses to unexpected or adversarial inputs

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

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

Adapt machine learning training to run across multiple GPUs or machines with Ray Train. Covers changes to existing code, comparing training settings with Ray Tune, and saving checkpoints so training can resume after failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapting existing PyTorch code for multiple GPUs
• Training HuggingFace models across multiple machines
• Comparing learning rates and batch sizes with Ray Tune

Automatically retrieves current library and framework documentation through the Context7 API during Claude Code conversations. Use it when asking about tools such as React, Next.js, or Prisma and needing up-to-date reference material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrieve current React documentation during a conversation
• Consult Next.js documentation in Claude Code
• Fetch Prisma reference material when asking questions

Manage multiple AI agents through the AI Maestro command-line tool. Create agents, inspect their status, pause and resume work, rename them, export or import configurations, and manage plugins from one interface.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an agent for a specific project
• Checking agent status and assigned tasks
• Hibernating agents and resuming them later

Organize complex tasks with three Markdown files for plans, findings, and progress. Keep goals, decisions, errors, and test results available across sessions so the agent can review its position and continue work with a clear next step.

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• Planning and tracking a task with multiple phases
• Saving findings during a research project
• Recording implementation progress and test results

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

Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.

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Push the current branch and all tags to origin, then create a GitHub release using the latest tag and its CHANGELOG.md entry. Use after tag-version to complete a release.

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Run the complete release workflow: changelog entry, version bump, git tag, and GitHub release. Invokes changelog-entry, tag-version, and publish in sequence. Use when releasing a new version of the project.

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Deploy a Python project to the target environment. Runs pytest, builds the wheel, and pushes a deployment tag. Only invoke manually when ready to deploy.

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Run all code quality checks (security, complexity, test coverage) on the current PR and post a combined report as a GitHub PR comment. Use before merging a PR, during code review, or when asked to check code quality.

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REST API design conventions for this codebase. Use when writing or reviewing API endpoints, route definitions, response formatting, or client code that calls the API.

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Error handling patterns for API endpoints. Use when implementing error handling, writing middleware, or reviewing how errors are caught and formatted in API code.

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Apply code formatting rules after file edits in the source directory. Use when asked to format code or after making changes to source files.

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Draft a CHANGELOG.md entry for the current changes in Keep a Changelog format. Use when releasing, tagging a version, or updating CHANGELOG.md.

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Perform a deep architectural analysis or security review requiring extended reasoning. Use for complex architecture decisions, security audits, or when standard analysis is not finding the root cause.

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Stage all changes and create a conventional commit with a generated message. Use when ready to commit and want automated staging and commit message generation.

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