
Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
Linear: manage issues, projects, teams via GraphQL + curl.
Implement multiple GitHub issues sequentially as stacked branches in separate worktrees, with an implementer sub-agent and an independent reviewer sub-agent per issue. Use when the user gives you two or more dependent issues and asks for them to be implemented in order, or says "stacked branches", "sequential issues", "issue chain", "do these in worktrees", or describes a parent epic with child issues that build on each other. Also reach for this whenever the user wants implementation and verification done by separate agents.
Use when building, backtesting, or deploying algorithmic trading systems with NautilusTrader (nautilus_trader) — writing Strategy/Actor classes and StrategyConfig, configuring BacktestNode/BacktestEngine, constructing instruments, order management, ParquetDataCatalog/wranglers, message bus and custom data streams, indicators, portfolio/analysis/report generation, or live TradingNode deployment. Curated for v1.230.0.
SAM: zero-shot image segmentation via points, boxes, masks.
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image-to-image translation, inpainting, or building custom diffusion pipelines.
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
Build, test, and debug Hermes Agent RL environments for Atropos training. Covers the HermesAgentBaseEnv interface, reward functions, agent loop integration, evaluation with tools, wandb logging, and the three CLI modes (serve/process/evaluate). Use when creating, reviewing, or fixing RL environments in the hermes-agent repo.
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model needed, more efficient than DPO. Use for preference alignment when want simpler, faster training than DPO/PPO.
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.
Linear: manage issues, projects, teams via GraphQL + curl.
Unsloth: 2-5x faster LoRA/QLoRA fine-tuning, less VRAM.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Notion API + ntn CLI: pages, databases, markdown, Workers.
Read, search, and create notes in the Obsidian vault.
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
Create, read, edit .pptx decks, slides, notes, templates.
Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
Airtable REST API via curl. Records CRUD, filters, upserts.
Airtable REST API via curl. Records CRUD, filters, upserts.
Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.
DSPy: declarative LM programs, auto-optimize prompts, RAG.
Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).
Read, search, create, and edit notes in the Obsidian vault.
DSPy: declarative LM programs, auto-optimize prompts, RAG.
Create, read, edit .pptx decks, slides, notes, templates.
Extract text from PDFs/scans (pymupdf, marker-pdf).
Notion API via curl: pages, databases, blocks, search.
Extract text from PDFs/scans (pymupdf, marker-pdf).
Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
Multi-phase brainstorming for complex feature implementations using parallel sub-tasks. Use when the user asks to brainstorm, design, or plan a complex feature, wants to explore implementation approaches, or needs architectural analysis before coding. Triggers on phrases like "brainstorm feature", "design implementation", "explore approaches", "plan architecture", or "how should we implement".
Find, extract, and port coding-agent output style prompts across Claude Code, Pi, and OMP.
Infographics: 21 layouts x 21 styles (信息图, 可视化).
Design one-off HTML artifacts (landing, deck, prototype).
Infographics: 21 layouts x 21 styles (信息图, 可视化).
Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
Generate project ideas via creative constraints.
Author/validate/export Google's DESIGN.md token spec files.
Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
Inspect installed Claude Code internals and official plugin recreations to locate prompts, output styles, built-in strings, and migration hooks.
Humanize text: strip AI-isms and add real voice.
Author/validate/export Google's DESIGN.md token spec files.
Manim CE animations: 3Blue1Brown math/algo videos.
Manim CE animations: 3Blue1Brown math/algo videos.
Humanize text: strip AI-isms and add real voice.
Design one-off HTML artifacts (landing, deck, prototype).
Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.
Generate images, video, and audio with ComfyUI — install, launch, manage nodes/models, run workflows with parameter injection. Uses the official comfy-cli for lifecycle and direct REST/WebSocket API for execution.
Generate project ideas via creative constraints.
Webhook subscriptions: event-driven agent runs.
Webhook subscriptions: event-driven agent runs.
Create, triage, label, assign GitHub issues via gh or REST.
GitHub PR lifecycle: branch, commit, open, CI, merge.
HeartMuLa: Suno-like song generation from lyrics + tags.
W&B: log ML experiments, sweeps, model registry, dashboards.
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
Use when copying TV media onto /mnt/Te_Amo and the imported show must be reshaped to match the nested TV Shows library layout on Te_Amo.
Host modded Minecraft servers (CurseForge, Modrinth).
GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
Clone/create/fork repos; manage remotes, releases.
Use the mcporter CLI to list, configure, auth, and call MCP servers/tools directly (HTTP or stdio), including ad-hoc servers, config edits, and CLI/type generation.
Drive a live marimo notebook as a workspace: run Python in the same kernel the user does, inspect live notebook state, and commit durable notebook changes. Use when the user wants to start a marimo notebook or pair on an active marimo session.
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
YouTube transcripts to summaries, threads, blogs.
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
Iterative Python via live Jupyter kernel (hamelnb).
Canonical folder and filename shape for TV shows stored under /mnt/Te_Amo/TV Shows.
Use when setting up, repairing, or using Arch User Repository (AUR) workflows on Arch Linux: prerequisites, yay/paru helper installation, PKGBUILD downloads, libalpm rebuilds after pacman upgrades, and verification.
Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.
Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
Spotify: play, search, queue, manage playlists and devices.
YouTube transcripts to summaries, threads, blogs.
Himalaya CLI: IMAP/SMTP email from terminal.
GitHub PR lifecycle: branch, commit, open, CI, merge.
Himalaya CLI: IMAP/SMTP email from terminal.
Host modded Minecraft servers (CurseForge, Modrinth).
Inspect codebases w/ pygount: LOC, languages, ratios.
Play Pokemon via headless emulator + RAM reads.
Create, triage, label, assign GitHub issues via gh or REST.
Iterative Python via live Jupyter kernel (hamelnb).
GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
Review PRs: diffs, inline comments via gh or REST.
Review PRs: diffs, inline comments via gh or REST.
Exploratory QA of web apps: find bugs, evidence, reports.
Clone/create/fork repos; manage remotes, releases.
Spotify: play, search, queue, manage playlists and devices.
MCP client: connect servers, register tools (stdio/HTTP).
Exploratory QA of web apps: find bugs, evidence, reports.
MCP client: connect servers, register tools (stdio/HTTP).
Outlines: structured JSON/regex/Pydantic LLM generation.
W&B: log ML experiments, sweeps, model registry, dashboards.
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
HuggingFace hf CLI: search/download/upload models, datasets.
lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
OBLITERATUS: abliterate LLM refusals (diff-in-means).
llama.cpp local GGUF inference + HF Hub model discovery.
OBLITERATUS: abliterate LLM refusals (diff-in-means).
vLLM: high-throughput LLM serving, OpenAI API, quantization.
SAM: zero-shot image segmentation via points, boxes, masks.
vLLM: high-throughput LLM serving, OpenAI API, quantization.
Search/download GIFs from Tenor via curl + jq.
AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
HuggingFace hf CLI: search/download/upload models, datasets.
Inspect local columnar/partitioned datasets (Parquet, SQLite staging/workspace DBs, JSONL metadata exports) to answer data-availability, earliest/latest timestamp, schema, and provenance questions without assuming labels are obvious.
HeartMuLa: Suno-like song generation from lyrics + tags.
Inspect codebases w/ pygount: LOC, languages, ratios.
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
Search/download GIFs from Tenor via curl + jq.
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
Find nearby places (restaurants, cafes, bars, pharmacies, etc.) using OpenStreetMap. Works with coordinates, addresses, cities, zip codes, or Telegram location pins. No API keys needed.
Manage Apple Reminders via remindctl CLI (list, add, complete, delete).
Drive a live marimo notebook as a workspace: run Python in the same kernel the user does, inspect live notebook state, and commit durable notebook changes. Use when the user wants to start a marimo notebook or pair on an active marimo session.
Manage Apple Notes via memo CLI: create, search, edit.
ASCII video: convert video/audio to colored ASCII MP4/GIF.
Apple Reminders via remindctl: add, list, complete.
Track Apple devices/AirTags via FindMy.app on macOS.
Send and receive iMessages/SMS via the imsg CLI on macOS.
Drive the macOS desktop in the background — screenshots, mouse, keyboard, scroll, drag — without stealing the user's cursor, keyboard focus, or Space. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.
Delegate coding to Claude Code CLI (features, PRs).
Delegate coding to OpenAI Codex CLI (features, PRs).
Delegate coding to OpenAI Codex CLI (features, PRs).
Configure, extend, or contribute to Hermes Agent.
Delegate coding to OpenCode CLI (features, PR review).
Delegate coding to OpenCode CLI (features, PR review).
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
ASCII video: convert video/audio to colored ASCII MP4/GIF.
Knowledge comics (知识漫画): educational, biography, tutorial.
Knowledge comics (知识漫画): educational, biography, tutorial.
Use when a Hermes Kanban worker wants to run Codex CLI as an isolated implementation lane while Hermes keeps ownership of task lifecycle, reconciliation, testing, and handoff.
ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
Manage Apple Notes via the memo CLI on macOS (create, view, search, edit).
Article illustrations: type × style × palette consistency.
Configure, extend, or contribute to Hermes Agent.
Dark-themed SVG architecture/cloud/infra diagrams as HTML.
Delegate coding to Claude Code CLI (features, PRs).
Send and receive iMessages/SMS via the imsg CLI on macOS.
Track Apple devices and AirTags via FindMy.app on macOS using AppleScript and screen capture.
Conducts a structured Socratic interview to produce or update a single experiment log entry — the durable record of what was run, what it showed, and what it means. Use this skill whenever the user wants to log an experiment, write up results, record a backtest, capture a finding, pre-register a run, document a study, or update an existing entry with new results or a revised interpretation. Trigger on phrases like "log this experiment," "write up the results of...", "I ran X, help me document it," "pre-register this," "update the entry for...", or when the user shares results and asks for help interpreting and recording them. The skill enforces the four-way separation between what happened, what it means, what it implies, and what comes next; challenges the user's interpretations with evidence requests and alternative explanations; and writes incrementally to keep context clean and the entry always grounded.
Use when an agent needs to produce, update, validate, or normalize a standardized research proposal artifact without running an interview. Defines the canonical structure, confidence-tag semantics, decision logic, and completion checks for proposal.md-style research plans.
Use when an agent needs to produce, update, validate, or normalize a standardized experiment-log entry without running an interview. Defines the canonical structure, pre-registration rules, evidence/interpretation split, calibration tags, and append-only revision model for durable experiment records.
Conducts a structured Socratic interview to produce a comprehensive markdown research proposal that handles cascading uncertainty (fixed end-question, branching experiments). Use this skill whenever the user wants to write a research proposal, research plan, study design, experiment plan, thesis proposal, RFC, or "spec out" a research direction — even if they don't explicitly say "interview me." Trigger when the user says things like "help me plan this research", "I want to design experiments for X", "draft a proposal for...", "think through a research direction", or shares a half-formed research idea and asks for help structuring it. The skill interviews the user, challenges their priors with evidence requests and falsifiers, optionally uses sub-agents to explore prior art, and builds the proposal markdown incrementally so context stays clean and the document is always grounded.
Interact with Proton Pass via the `pass-cli` command-line tool. Use this skill when the user wants to manage passwords, vaults, items, SSH keys, secret injection, or any credential management through the Proton Pass CLI. Triggers on mentions of pass-cli, proton pass, secret references, pass:// URIs, vault management, or SSH agent integration with Proton Pass.
Analyzes algorithmic trading backtest results from Jupyter notebooks and generates summary reports. Use when the user wants to analyze or summarize backtest notebooks.
Adversarial brainstorming partner that pressure-tests ideas, challenges assumptions, and explores the codebase to arrive at well-architected solutions. Use when the user asks to brainstorm, challenge an idea, explore approaches, pressure-test a design, or think through architecture. Triggers on phrases like "brainstorm", "challenge this", "pressure test", "what if", "should we", "is this the right approach", "explore alternatives".
Commit changes in a git workspace using the Conventional Commits specification. Use when the user asks to commit, stage, or save changes to git, or mentions "conventional commits". Analyzes staged/unstaged changes, groups related modifications, generates properly formatted commit messages, and commits files separately or together as appropriate. Confirms with user when commit ordering is ambiguous.
Primer for using the data-juicer Python library (also written `datajuicer` or `DJ`) — a YAML-driven, OP-based system for cleaning, filtering, deduplicating, transforming, and synthesizing text and multimodal data for foundation models. Use this skill whenever the user mentions data-juicer, DJ, dj-process, dj-analyze, "DJ format", building data recipes / YAML pipelines for LLM training data, or writing custom Filter / Mapper / Deduplicator / Selector / Aggregator / Grouper operators ("OPs"). Also reach for it when the user is putting together a data preprocessing pipeline for LLM pre-training, post-tuning, or multimodal datasets and DJ would be a natural fit, even if they haven't named the library yet — flagging DJ as an option is often the most helpful move.
Manage dotfiles and keep synced with external directories. Use on Linux / macOS systemswhen thetre are external connfigs which are being imported into the dotfiles, or when stored dotfiles need to be brought outside to the XDG environment.
Package a Claude Code skill directory into a distributable .skill file. Use when the user wants to export, package, bundle, zip, or distribute a skill. Triggers on mentions of .skill files, packaging skills, exporting skills, or sharing skills.
Push local branches and create stacked PRs. Analyzes branch topology, pushes, creates PRs with correct base branches, and resolves merge conflicts. Use when you have a chain of local branches to turn into stacked PRs.
p5.js sketches: gen art, shaders, interactive, 3D.
Use when building creative browser demos with @chenglou/pretext — DOM-free text layout for ASCII art, typographic flow around obstacles, text-as-geometry games, kinetic typography, and text-powered generative art. Produces single-file HTML demos by default.
54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.
p5.js sketches: gen art, shaders, interactive, 3D.
Pixel art w/ era palettes (NES, Game Boy, PICO-8).
Throwaway HTML mockups: 2-3 design variants to compare.
Pixel art w/ era palettes (NES, Game Boy, PICO-8).
54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
Control a running TouchDesigner instance via twozero MCP — create operators, set parameters, wire connections, execute Python, build real-time visuals. 36 native tools.
Throwaway HTML mockups: 2-3 design variants to compare.
Use when building creative browser demos with @chenglou/pretext — DOM-free text layout for ASCII art, typographic flow around obstacles, text-as-geometry games, kinetic typography, and text-powered generative art. Produces single-file HTML demos by default.
Songwriting craft and Suno AI music prompts.
Songwriting craft and Suno AI music prompts.
llama.cpp local GGUF inference + HF Hub model discovery.
Play Pokemon via headless emulator + RAM reads.
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.