Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/sniko/agent-skills/swe-worktreenpx skills add SNIKO/agent-skills --skill swe-worktreegit clone --depth 1 https://github.com/SNIKO/agent-skillsWrote 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/sniko/agent-skills/swe-worktree)<a href="https://agentmods.dev/skills/sniko/agent-skills/swe-worktree"><img src="https://agentmods.dev/badge/skills/sniko/agent-skills/swe-worktree.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.00969 |
| Opus 5 | $0.00016 | $0.00485 |
| Sonnet 5 | $0.00007 | $0.00194 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
swe-worktree 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 5d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pipeline
swe-shape → swe-spec → [swe-plan] → [swe-worktree] → swe-execute → swe-review, with swe-research available at any point.
Each stage runs in its own session: the user invokes one skill, reviews the result, clears the context, then invokes the next. Assume no memory of other stages beyond the files named below.
- This stage: optional infrastructure. Isolate the change before implementation.
- Reads: the change directory produced by earlier stages —
brief.md,spec.html, andplan.mdwhen present. Their contents are not needed; only their location. - Writes: a git branch and worktree, with the change directory copied into it.
- Next: the user runs
swe-executeinside the new worktree. Do not invoke it.
Purpose
Create a safe isolated branch and git worktree, copy the complete SWE change workspace into it, and report where implementation should continue. Worktrees are optional execution infrastructure, not a required specification stage.
Inputs
Infer from user input or an artifact path:
CHANGE_DIR:.swe/<change>/SLUG: kebab-case change labelREPO_NAME: repository root basenameBRANCH_NAME: repository convention, otherwise<slug>WORKTREE_PARENT: repository convention, otherwise../worktrees/<repo-name>/WORKTREE_PATH:<worktree-parent>/<slug>
Use the change directory named by the user. If it is ambiguous, ask which one to prepare.
The shared research corpus at .swe/research/ is not change scope. It is repository documentation that outlives any branch, so it is not copied and not branched: research produced during implementation is written to the corpus in the main working tree and committed independently. Copying it would fork the corpus per branch and guarantee divergence.
Workflow
- Inspect repository state. Read repository git guidance and inspect
git status --short, current branch, existing branches, and worktrees. Preserve unrelated changes. - Resolve names. Follow repository branch and worktree conventions. Fall back to the defaults above only when guidance is absent.
- Handle conflicts. If the branch or path already exists, report the exact conflict and ask whether to reuse it or choose another name.
- Create the worktree. Run:
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.
- 5d ago First seen · 84 lines · 33 tokens per session scan A ef2973aa1576
swe-worktree is a skill published in the GitHub repository SNIKO/agent-skills (2 stars, last pushed 13d ago), licensed MIT. It adds 33 tokens to every session and 969 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.
Other skills, from other repositories
release-candidate-prep
Preflight and prepare an OpenAI Agents Python release candidate in a dedicated worktree from exact origin/main, gate readiness before branch creation, freeze the released API contract, create or replace the local release branch with one release commit, enforce final release review as a checker, and produce…
final-release-review
Perform pre-release planning or a final release-candidate review for openai-agents-python by comparing the target with the previous remote tag, determining the minimum compatible release type, auditing regressions and contract changes, reviewing open documentation PR coverage, drafting minor-release Key Changes, and…
implementation-strategy
Choose compatibility-aware scope for runtime and API changes in openai-agents-python. Use before initial implementation and each review-feedback batch to decide whether to patch, reset the design, preserve compatibility, or reject unsupported cases.
pr-draft-summary
Create the required PR-ready summary block, branch suggestion, title, and draft description for openai-agents-python. Use before the final response whenever the current task changed runtime code, tests, examples, build/test configuration, or docs with behavior impact, regardless of perceived change size and including…
examples-run-analysis
Analyze artifacts from the latest completed manual examples Make run. Read the main log, every relevant per-example log, and example source; validate every exit-0 example and classify failures, skips, and environment restrictions. Never execute or control examples.
playwright
Use when the task requires capturing or automating a real browser from the terminal.