All Skills

Run a customer journey mapping workshop with adaptive questions to map stages, actions, emotions, pain points, and opportunities for a persona and scenario.

Design tool descriptions, schemas, and response formats that agents can reliably route on, including MCP server design, tool-set consolidation, and naming conventions.

Uses the filesystem as a durable overflow layer for agent context, enabling scratch pads, plan persistence, sub-agent communication, dynamic skill loading, and log management.

Designs autonomous agent harnesses with locked evaluators, editable surfaces, durable logging, novelty gates, pruning, rollback, and human approval boundaries.

Manages a personal operating system for content creation, relationship management, goal tracking, and weekly reviews using progressive disclosure and append-only data files.

Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

Build teams of autonomous AI agents that collaborate to solve complex tasks using role-based delegation, memory, and sequential or hierarchical execution.

Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.

Convert and quantize models to GGUF format for efficient CPU/GPU inference with llama.cpp, supporting 2-8 bit quantization and Apple Silicon acceleration.

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments.

Train large-scale MoE models with FP8/INT4 low-precision RL, speculative decoding, and train-inference alignment using the miles framework.

Generate music and sound effects from text descriptions using Meta's AudioCraft library, with support for melody conditioning, stereo output, and style transfer.

Run ML training and batch jobs across multiple clouds with automatic cost optimization, spot instance recovery, and unified orchestration.

Train reinforcement learning models using torchforge, Meta's PyTorch-native RL library for scalable, algorithm-focused experimentation with GRPO, DAPO, and custom loss functions.

Provides expert guidance for fine-tuning LLMs with LLaMA-Factory, covering WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, and multimodal support.

Train and use SentencePiece tokenizers for multilingual NLP, supporting BPE and Unigram algorithms with raw Unicode text.

Guarantee valid JSON, XML, or code structure during text generation using Pydantic models for type-safe outputs, supporting local models (Transformers, vLLM, llama.cpp) and maximizing inference speed with structured generation.

Pretrains large language models from scratch using PyTorch-native distributed training with 4D parallelism (FSDP2, TP, PP, CP) and Float8 support on H100 GPUs.

Scales machine learning training from single GPU to multi-node clusters with minimal code changes. Supports PyTorch, TensorFlow, and HuggingFace with built-in hyperparameter tuning, fault tolerance, and elastic scaling.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) using standardized prompts and metrics. Supports HuggingFace, vLLM, and API backends.

Draft publication-ready ML/AI papers for top conferences (NeurIPS, ICML, ICLR, ACL, AAAI, COLM) by exploring research repositories, structuring arguments, verifying citations via APIs, and formatting submissions.

Fast tokenization for NLP using Rust-based tokenizers supporting BPE, WordPiece, and Unigram algorithms, with training, alignment tracking, and padding/truncation.

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script.

Applies cognitive science frameworks for creative thinking to CS and AI research ideation, using combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded strategies.

Generate brand-quality product images by selecting a photography mode and answering a short interview; the backend enhances the prompt and submits it to GPT Image 2.

Write and debug code inside n8n's Custom Code Tool node, with guidance on input modes, return formats, error handling, and common pitfalls.

Manage multiple n8n instances over MCP by discovering, switching, and verifying the target instance before reads and writes.

Validate n8n expression syntax and fix common errors when writing {{}} expressions, accessing $json/$node variables, and mapping data between nodes.

Routes UI-related tasks to the smallest useful UI skill context via the ui-skills CLI.

Runs a structured code review as a pre-commit or pre-ship gate, supporting multiple review engines and scope governance.

Review, triage, validate, and discuss ClawHub GitHub issues and pull requests with structured evidence, best-fix analysis, and maintainer handoff.

Drive end-to-end Convex production migrations with explicit safety gates for destructive operations and cleanup.

Create developer handoff specifications with measurements, behaviors, assets, and edge cases.

Create an icon system specification covering grid, sizing, naming, categories, and implementation guidance.

Establish clear visual hierarchy through size, weight, color, spacing, and positioning to guide users through interfaces.