skills

In the author's own words, from each skill's SKILL.md.

• ai-co-scientist

  Transform Claude Code into an AI Scientist that orchestrates research workflows using tree-based hypothesis exploration. Triggers on "research project", "scientific experiment", "run experiments", "AI scientist", "tree search experimentation", "systematic study".

  skills/ai-co-scientist/SKILL.md on GitHub (opens in a new tab)

• codex

  Run OpenAI's Codex CLI agent in non-interactive mode using `codex exec`. Use when delegating coding tasks to Codex, running Codex in scripts/automation, or when needing a second agent to work on a task in parallel.

  skills/codex/SKILL.md on GitHub (opens in a new tab)

• commit-splitter

  Split large sets of uncommitted changes into logical, well-organized commits. Use when the user has many uncommitted changes and wants structured commits, or proactively suggest when detecting a large diff that would benefit from splitting.

  skills/commit-splitter/SKILL.md on GitHub (opens in a new tab)

• cs-research-methodology

  Conduct a literature review and develop a CS research proposal. Use when asked to review a research area, find gaps in existing work, and propose a novel research contribution. The output is a research proposal identifying an assumption to challenge (the "bit flip") and how to validate it.

  skills/cs-research-methodology/SKILL.md on GitHub (opens in a new tab)

• icml-reviewer

  Paper reviewer that evaluates machine learning research projects following official ICML reviewer guidelines. Provides comprehensive reviews with actionable feedback across all key dimensions: claims/evidence, relation to prior work, originality, significance, clarity, and reproducibility. Also provides formative feedback on incomplete drafts, proposals, and research code repositories. MANDATORY…

  skills/icml-reviewer/SKILL.md on GitHub (opens in a new tab)

• neuro-symbolic-reasoning

  Neuro-symbolic AI combining LLMs with symbolic solvers. Use when exploring neuro-symbolic approaches (ideation, no code) or implementing solver integrations (code).

  skills/neuro-symbolic-reasoning/SKILL.md on GitHub (opens in a new tab)

• project-referee

  Critiques ML conference papers with reviewer-style feedback. Use when users want to anticipate reviewer concerns, identify weaknesses, check claim-evidence gaps, or find missing citations.

  skills/project-referee/SKILL.md on GitHub (opens in a new tab)

• skill-to-card

  End-to-end workflow that creates a skill from a description and attached files, publishes it to Sundial as a private skill, generates a trading card (front + back with QR code), and sends it to a printer. Use when the user wants to create a skill and get a printed trading card, or says "skill to card", "create and print a skill card", "make me a skill with a card".

  skills/skill-to-card/SKILL.md on GitHub (opens in a new tab)

• skill

  Find, install, create, improve, and publish AI agent skills through the Sundial ecosystem. Use when the user wants to find or search for skills, install a skill, create a new skill, improve or evaluate an existing skill, or publish a skill to Sundial Hub. Trigger phrases include "find a skill", "install skill", "create a skill", "make a skill", "improve this skill", "evaluate skill", "publish ski…

  skills/skill/SKILL.md on GitHub (opens in a new tab)

• tinker-training-cost

  Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.

  skills/tinker-training-cost/SKILL.md on GitHub (opens in a new tab)

• tinker

  Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, multimodal models including Thinking Machines' Inkling (vision, audio, thinking effort), and both high-level Cookbook patterns and low-level API usage.

  skills/tinker/SKILL.md on GitHub (opens in a new tab)

• training-data-curation

  Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.

  skills/training-data-curation/SKILL.md on GitHub (opens in a new tab)

• cs448b-visualization

  Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.

  skills/visualization-skill/SKILL.md on GitHub (opens in a new tab)

No skill matches that filter.

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