Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/strikersam/autonomous-ai-agency/parallel-worktrees)<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/parallel-worktrees"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/parallel-worktrees/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/strikersam/autonomous-ai-agency/parallel-worktrees"><img src="https://agentmods.dev/badge/skills/strikersam/autonomous-ai-agency/parallel-worktrees.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01009 |
| Opus 5 | $0.00019 | $0.00504 |
| Sonnet 5 | $0.00008 | $0.00202 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
parallel-worktrees scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: parallel-worktrees
When to Use
Use this skill when you need to:
- Run the test suite on
mainwhile implementing on a feature branch - Keep a clean reference copy of the code for comparison during refactors
- Work on a hotfix without disturbing in-progress feature work
- Avoid
git stash/git checkoutcycles that interrupt flow
Concept
Git worktrees let you check out multiple branches of the same repo into different
directories simultaneously. Each worktree shares the .git database but has its
own working tree and index.
/home/user/local-llm-server/ ← main worktree (your current branch)
/home/user/local-llm-server-main/ ← linked worktree (main branch, read reference)
/home/user/local-llm-server-hotfix/ ← linked worktree (hotfix branch)
Instructions
Step 1 — Create a worktree for reference/parallel work
# Create a linked worktree for the main branch (read reference)
git worktree add ../local-llm-server-main main
# Create a linked worktree for a new feature branch
git worktree add ../local-llm-server-feature -b feature/my-feature
# Create a linked worktree for an existing remote branch
git worktree add ../local-llm-server-hotfix origin/hotfix/issue-42
Step 2 — List active worktrees
git worktree list
Output shows each worktree's path, HEAD commit, and branch.
Step 3 — Work across worktrees
Each worktree is an independent directory. You can:
# Run tests on main without switching branches
cd ../local-llm-server-main && pytest -x
# Edit files in the feature worktree
cd ../local-llm-server-feature && $EDITOR proxy.py
# Compare files across worktrees
diff ../local-llm-server-main/proxy.py ./proxy.py
Step 4 — Remove a worktree when done
# Remove the linked worktree (does not delete the branch)
git worktree remove ../local-llm-server-main
# Force remove if worktree has uncommitted changes
git worktree remove --force ../local-llm-server-main
# Prune stale worktree references
git worktree prune
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 151 lines · 38 tokens per session scan A f8067f19d86f
parallel-worktrees is a skill published in the GitHub repository strikersam/autonomous-ai-agency (8 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,009 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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