Worktree Data Sync Skill

Non-git data sync between existing worktrees — seed, diff, apply, data teardown. Worktree lifecycle (create / enter / remove): skills/agent-orchestration/references/worktree-harness-fallback.md.

When to Use

Activate the data-sync CLI below for:

• seeding non-git-controlled data from one existing worktree into another
• comparing non-git files across existing worktrees
• copying managed data between worktrees
• reconciling non-git differences after parallel work

Command Surface

Single CLI entrypoint (<skill-dir> = directory containing this SKILL.md; --from defaults to the worktree containing the caller's current directory):

python3 <skill-dir>/scripts/sync_worktree_data.py --to <worktree-path> --mode <seed|diff|apply> [OPTIONS]

Modes

--mode seed

Materialize missing managed files in destination from source. Never overwrites existing destination files.

Per managed directory root, a stat-only preflight walk picks the cheapest path:

• fresh, clean destination: one cp -c -R -p clones the whole root (COW where the filesystem supports it, falling back to shutil.copytree)
• fresh destination with cloud-placeholder (dataless) files: only the directories holding a placeholder are recreated — placeholders become symlinks to their resolved source, siblings clone whole, loose files batch-copy
• more than half the root's files are dataless: seeds per-file (symlink placeholders, batch-copy the rest) and prints a suggestion to annotate the root # data-sync:symlink, since it never switches modes automatically
• destination root already exists: falls back to the per-file merge walk, copying only what's missing

Every failed path is recorded with its reason; seed prints the listing to stderr (capped, plus a total count) and exits nonzero when any path failed.

Optional: --seed-sync-mode <auto|force-symlink|force-cow> (default: auto)

• auto: preserve current per-path behavior (symlink-only roots get symlinks, others get copies)
• force-symlink: create top-level symlinks for all managed roots when the destination path does not already exist; conflicting paths are skipped
• force-cow: copy/COW all managed roots, including symlink-only annotated paths

--seed-sync-mode is valid only with --mode seed; the CLI rejects it elsewhere.

--mode diff

Report source-to-destination differences for managed files.

Statuses:

• new: exists in source but missing in destination
• modified: exists in both but differs
• unchanged: identical (only shown with --include-unmodified)

Output options:

• human-readable report (default)
• JSON report (--json), compatible with apply mode

--mode apply

Execute sync actions for selected changes.

Actions:

• --action overwrite: copy source file to destination path
• --action rename: copy source file to destination with suffix

Selection options:

• --from-json <file> from prior diff output
• --files <path...> for explicit relative paths
• omit both to auto-process current diff (new + modified)

No delete/discard action is provided.

Managed Path Discovery

Discovery is stateless and source-driven. Managed roots come from:

• gitignored paths via git ls-files --others --ignored --exclude-standard --directory, minus a built-in denylist (below)
• tracked symlinks that resolve outside the repo
• top-level symlink safety net, skipping symlinks git already tracks (git checks those out in the destination)
• .gitignore symlink-only annotations

A gitignored entry whose basename matches a well-known non-data name — .venv, venv, .direnv, node_modules, __pycache__, .pytest_cache, .mypy_cache, .ruff_cache, .tox, .nox, .cache, .ipynb_checkpoints, .quarto, dist, build, *.egg-info, .DS_Store, .env, .envrc, .worktrees, .claude, .codex — is excluded from managed entries. The denylist filters discovered entries only — it excludes nothing from inside an otherwise-managed root. A # data-sync:symlink annotation always wins over the denylist, so an annotated root stays managed even under a denylisted name.

Annotate a path as symlink-only by adding a duplicate line with the tag comment:

Data/
Data/  # data-sync:symlink

The first line is the actual gitignore rule; the second is the annotation the discovery script parses. Legacy tag # worktree:symlink is also supported.

Symlink-only roots are symlinked in seed auto mode and excluded from diff/apply actions.

Examples

# Seed from the current worktree into destination
python3 <skill-dir>/scripts/sync_worktree_data.py \
  --to ../MyRepo-feature \
  --mode seed

# Seed using top-level symlinks for all managed roots
python3 <skill-dir>/scripts/sync_worktree_data.py \
  --to ../MyRepo-feature \
  --mode seed \
  --seed-sync-mode force-symlink

# Seed using copy/COW for all managed roots (including symlink-only)
python3 <skill-dir>/scripts/sync_worktree_data.py \
  --to ../MyRepo-feature \
  --mode seed \
  --seed-sync-mode force-cow

# Diff explicit source -> destination
python3 <skill-dir>/scripts/sync_worktree_data.py \
  --from ../MyRepo-expA \
  --to ../MyRepo-expB \
  --mode diff --json

# Apply overwrite using diff json
python3 <skill-dir>/scripts/sync_worktree_data.py \
  --to ../MyRepo-expB \
  --mode apply \
  --from-json /tmp/changes.json \
  --action overwrite

# Apply rename for explicit files
python3 <skill-dir>/scripts/sync_worktree_data.py \
  --from ../MyRepo-expA \
  --to ../MyRepo-expB \
  --mode apply \
  --files output/result.csv notes/draft.md \
  --action rename \
  --suffix _from_expA

Data Teardown

Materialized data inside a worktree (copies, COW clones, symlinks created by --mode seed) disappears when the worktree directory is deleted — no separate "unseed" step. The source worktree's data is untouched.

Worktree removal itself (git worktree remove, branch deletion, safety checks): skills/agent-orchestration/references/worktree-harness-fallback.md §Remove.

See Also

• skills/agent-orchestration/references/worktree-harness-fallback.md — worktree lifecycle (create / enter / remove), harness tools preferred, raw-git fallback, placement conventions.
• skills/agent-orchestration/references/parallel-dispatch.md — when parallel subagents each need their own worktree, and how data seeding fits into that flow.