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/tanstack/ai/pr-sweepnpx skills add TanStack/ai --skill pr-sweepgit clone --depth 1 https://github.com/TanStack/aiWhat 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 | $0.00159 | $0.04640 |
| Opus 5 | $0.00079 | $0.02320 |
| Sonnet 5 | $0.00032 | $0.00928 |
| Haiku 4.5 | $0.00016 | $0.00464 |
Grade A, and why
pr-sweep 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 2d 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 — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Sweep
Prep PRs for review. Fan out one subagent per PR (cap 100). Default is dry-run (report only). Mutating steps require --apply (or the user saying "apply" / "go ahead").
Args
| Invocation | Behavior |
|---|---|
/pr-sweep |
All open non-draft PRs (full audit) |
/pr-sweep 12 34 56 |
Only those PR numbers |
/pr-sweep --apply |
Full set, then rebase and push (--force-with-lease) |
/pr-sweep --apply 12 34 |
Rebase and push listed PRs only |
/pr-sweep --outside-only |
Action target = outside authors only (default for mutations) |
/pr-sweep --include-in-house |
Also rebase/update in-house branches (still never merge) |
/pr-sweep --behind |
Only PRs behind base / BEHIND / not up to date |
/pr-sweep --conflicts |
Only CONFLICTING / DIRTY / dirty merge state |
/pr-sweep --changed |
Only PRs changed since last snapshot (or updatedAt within 24h if no snapshot) |
/pr-sweep --daily |
Recommended daily recipe: --changed ∪ --behind ∪ --conflicts ∪ new outside PRs; security-scan new outside; lighter pass on the rest |
/pr-sweep --apply --daily --include-in-house |
Daily apply: prep outside + rebase ours when behind/conflicting |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 390 lines · 159 tokens per session scan A 27ae3af5384a
pr-sweep is a skill published in the GitHub repository TanStack/ai (3,056 stars, last pushed today), licensed MIT. It adds 159 tokens to every session and 4,640 once invoked, about $0.0008 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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