Borrowing it
Nothing to install: this file belongs to llama-farm/llamafarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/llama-farm/llamafarm/main/.claude/skills/wt/SKILL.mdgit clone --depth 1 https://github.com/llama-farm/llamafarmWrote 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/llama-farm/llamafarm/wt)<a href="https://agentmods.dev/skills/llama-farm/llamafarm/wt"><img src="https://agentmods.dev/badge/skills/llama-farm/llamafarm/wt/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/llama-farm/llamafarm/wt"><img src="https://agentmods.dev/badge/skills/llama-farm/llamafarm/wt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 52 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00025 | $0.00907 |
| Opus 5 | $0.00013 | $0.00453 |
| Sonnet 5 | $0.00005 | $0.00181 |
| Haiku 4.5 | $0.00003 | $0.00091 |
Grade A, and why
wt 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 9d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wt - Worktree Manager Skill
Manages isolated LlamaFarm development environments using git worktrees. Each worktree has its own services, ports, and data directories - enabling parallel agent sessions without conflicts.
Full Documentation
For complete documentation, architecture details, and advanced usage: @scripts/wt/README.md
Quick Reference
| Task | Command |
|---|---|
| Create worktree and start services | wt create feat/my-feature |
| Create and cd into worktree | wt create --go feat/my-feature |
| List all worktrees with status | wt list |
| Check service health | wt status or wt health |
| Start/stop services | wt start / wt stop |
| View service logs | wt logs [server|rag|runtime|designer|all] |
| Delete worktree | wt delete <name> |
| Switch to worktree | wt switch <name> |
| Open Designer in browser | wt open |
| Diagnose issues | wt doctor |
| Clean orphaned data | wt gc |
When to Use wt
Use wt when:
- Starting isolated work on a feature/task that needs running services
- Running parallel coding sessions (multiple agents or terminals)
- Testing changes without affecting the main development environment
- Avoiding port conflicts between concurrent LlamaFarm instances
Common Workflows
Starting a New Task
# Create isolated environment with services running
wt create --go feat/my-task
# Work in the worktree...
# Services are already running on auto-assigned ports
# Check status anytime
wt status
# View logs if needed
wt logs server
Checking What's Running
# List all worktrees with their port assignments
wt list
# Example output:
# NAME STATUS SERVER DESIGNER RUNTIME
# feat-my-task running 8150 5150 11150
# fix-bug stopped 8234 5234 11234
Cleaning Up
# Stop services and remove a worktree
wt delete feat-my-task
# Remove data for worktrees that no longer exist
wt gc
# Remove worktrees for branches merged to main
wt 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.
- 9d ago First seen · 141 lines · 25 tokens per session scan A f649ddae755c
wt is a skill published in the GitHub repository llama-farm/llamafarm (838 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 907 once invoked, about $0.0001 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 2026-08-30.
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