You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Ensure work happens in an isolated workspace. Prefer your platform's native worktree tools. Fall back to manual git worktrees only when no native tool is available.
Core principle: Detect existing isolation first. Then use native tools. Then fall back to git. Never fight the harness.
Announce at start: "I'm using the using-git-worktrees skill to set up an isolated workspace."
Before creating anything, check if you are already in an isolated workspace.
GIT_DIR=$(cd "$(git rev-parse --git-dir)" 2>/dev/null && pwd -P)
GIT_COMMON=$(cd "$(git rev-parse --git-common-dir)" 2>/dev/null && pwd -P)
BRANCH=$(git branch --show-current)
Submodule guard: GIT_DIR != GIT_COMMON is also true inside git submodules. Before concluding "already in a worktree," verify you are not in a submodule:
# If this returns a path, you're in a submodule, not a worktree — treat as normal repo
git rev-parse --show-superproject-working-tree 2>/dev/null
If GIT_DIR != GIT_COMMON (and not a submodule): You are already in a linked worktree. Skip to Step 2 (Project Setup). Do NOT create another worktree.
Report with branch state:
<path> on branch <name>."<path> (detached HEAD, externally managed). Branch creation needed at finish time."If GIT_DIR == GIT_COMMON (or in a submodule): You are in a normal repo checkout.
Has the user already indicated their worktree preference in your instructions? If not, ask for consent before creating a worktree:
"Would you like me to set up an isolated worktree? It protects your current branch from changes."
Honor any existing declared preference without asking. If the user declines consent, work in place and skip to Step 2.
You have two mechanisms. Try them in this order.
The user has asked for an isolated workspace (Step 0 consent). Do you already have a way to create a worktree? It might be a tool with a name like EnterWorktree, WorktreeCreate, a /worktree command, or a --worktree flag. If you do, use it and skip to Step 2.
Native tools handle directory placement, branch creation, and cleanup automatically. Using git worktree add when you have a native tool creates phantom state your harness can't see or manage.
Only proceed to Step 1b if you have no native worktree tool available.
Only use this if Step 1a does not apply — you have no native worktree tool available. Create a worktree manually using git.
Follow this priority order. Explicit user preference always beats observed filesystem state.
Check your instructions for a declared worktree directory preference. If the user has already specified one, use it without asking.
Check for an existing project-local worktree directory:
ls -d .worktrees 2>/dev/null # Preferred (hidden)
ls -d worktrees 2>/dev/null # Alternative
If found, use it. If both exist, .worktrees wins.
If there is no other guidance available, default to .worktrees/ at the project root.
MUST verify directory is ignored before creating worktree:
git check-ignore -q .worktrees 2>/dev/null || git check-ignore -q worktrees 2>/dev/null
If NOT ignored: Add to .gitignore, commit the change, then proceed.
Why critical: Prevents accidentally committing worktree contents to repository.
# Determine path based on chosen location
path="$LOCATION/$BRANCH_NAME"
git worktree add "$path" -b "$BRANCH_NAME"
cd "$path"
Sandbox fallback: If git worktree add fails with a permission error (sandbox denial), tell the user the sandbox blocked worktree creation and you're working in the current directory instead. Then run setup and baseline tests in place.
Auto-detect and run appropriate setup:
# Node.js
if [ -f package.json ]; then npm install; fi
# Rust
if [ -f Cargo.toml ]; then cargo build; fi
# Python
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
if [ -f pyproject.toml ]; then poetry install; fi
# Go
if [ -f go.mod ]; then go mod download; fi
Run tests to ensure workspace starts clean:
# Use project-appropriate command
npm test / cargo test / pytest / go test ./...
If tests fail: Report failures, ask whether to proceed or investigate.
If tests pass: Report ready.
Worktree ready at <full-path>
Tests passing (<N> tests, 0 failures)
Ready to implement <feature-name>
| Situation | Action |
|---|---|
| Already in linked worktree | Skip creation (Step 0) |
| In a submodule | Treat as normal repo (Step 0 guard) |
| Native worktree tool available | Use it (Step 1a) |
| No native tool | Git worktree fallback (Step 1b) |
.worktrees/ exists | Use it (verify ignored) |
worktrees/ exists | Use it (verify ignored) |
| Both exist | Use .worktrees/ |
| Neither exists | Check instruction file, then default .worktrees/ |
| Directory not ignored | Add to .gitignore + commit |
| Permission error on create | Sandbox fallback, work in place |
| Tests fail during baseline | Report failures + ask |
| No package.json/Cargo.toml | Skip dependency install |
| Excuse | Reality |
|---|---|
| "I'm obviously not in a worktree — no need to check" | Run Step 0. Harness-created isolation and submodules both fool eyeballing; the detection commands settle it. |
"git worktree add is quicker than hunting for a native tool" | A native tool (e.g. EnterWorktree) owns placement, branching, and cleanup. Bypassing it is the #1 mistake — it creates phantom state your harness can't see or manage. |
| "The worktree directory is surely ignored already" | Run git check-ignore. An unignored worktree directory commits the whole tree into the repo. |
| "Any directory name works" | Explicit instructions beat an existing project-local directory, which beats the .worktrees/ default. |
| "The workspace is fresh — baseline tests can wait" | A dirty baseline makes every later failure ambiguous. Run the tests now; proceeding past failures is your human partner's call. |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
日本語の概要は準備中です。原文の説明を表示しています。
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
日本語の概要は準備中です。原文の説明を表示しています。
Use when you have a written implementation plan to execute in a separate session with review checkpoints
日本語の概要は準備中です。原文の説明を表示しています。
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
日本語の概要は準備中です。原文の説明を表示しています。