// 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.
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 skill", "push skill", "search for skills".
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".
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.
Neuro-symbolic AI combining LLMs with symbolic solvers. Use when exploring neuro-symbolic approaches (ideation, no code) or implementing solver integrations (code).
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.
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".
| name | commit-splitter |
| description | 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. |
git stash push -m "save before structured commit: <brief description>" --include-untracked
git stash apply
Keep changes applied throughout. Do NOT re-stash between commits. Only drop the safety stash at the very end.
Generate patches for all modified files and list their hunks. Quote paths with special characters (brackets, spaces, etc.):
git diff "<file>" > /tmp/<file>.patch
.claude/skills/commit-splitter/scripts/extract-hunks.py /tmp/<file>.patch --list
This outputs numbered hunks:
Hunks (3 total):
1: @@ -10,6 +10,7 @@ def foo(): (+1/-0) print("perf log")...
2: @@ -25,4 +26,6 @@ def bar(): (+2/-0) import threading...
3: @@ -50,3 +53,4 @@ def baz(): (+1/-0) min_containers=1...
Reference hunks by number in the plan:
modal_app.py:
Hunk 1: perf logging
Hunk 2: async threading
Hunk 3: min_containers=1
Commit 1: "perf: add logging" - modal_app.py hunks 1
Commit 2: "perf: async + min_containers" - modal_app.py hunks 2,3
CRITICAL: Follow the approved plan exactly. Never combine commits because splitting "seems hard."
NEVER use these shortcuts:
git checkout to reset files then re-edit themWrite tool or heredocs to create patchesWhole files: just git add <file>
New files: just git add <file>
Partial files (specific hunks):
.claude/skills/commit-splitter/scripts/extract-hunks.py /tmp/<file>.patch <hunk_numbers> > /tmp/commit.patch
git apply --cached /tmp/commit.patch
Example:
# Commit 1: just perf logging (hunk 1)
.claude/skills/commit-splitter/scripts/extract-hunks.py /tmp/modal_app.patch 1 > /tmp/commit1.patch
git apply --cached /tmp/commit1.patch
git commit -m "perf: add logging"
# Commit 2: async + min_containers (hunks 2,3)
.claude/skills/commit-splitter/scripts/extract-hunks.py /tmp/modal_app.patch 2,3 > /tmp/commit2.patch
git apply --cached /tmp/commit2.patch
git commit -m "perf: async and min_containers"
After each commit, verify with git diff --cached --stat.
Location: .claude/skills/commit-splitter/scripts/extract-hunks.py
# List hunks with summaries
.claude/skills/commit-splitter/scripts/extract-hunks.py file.patch --list
# Extract specific hunks
.claude/skills/commit-splitter/scripts/extract-hunks.py file.patch 1,3,5 # hunks 1, 3, 5
.claude/skills/commit-splitter/scripts/extract-hunks.py file.patch 2-4 # hunks 2, 3, 4
.claude/skills/commit-splitter/scripts/extract-hunks.py file.patch 1,3-5,7 # mixed
The script outputs a valid patch to stdout. Redirect to a file, then git apply --cached.
If something goes wrong:
git reset HEAD to unstagegit stash list to find safety stashgit checkout -- <file> then git stash apply to restore a fileAfter all commits succeed:
git stash drop to remove safety stash