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.
git clone --depth 1 https://github.com/Titanium-Birch/SFTPWranglerWrote 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/rules/titanium-birch/sftpwrangler/agent-bootstrap)<a href="https://agentmods.dev/rules/titanium-birch/sftpwrangler/agent-bootstrap"><img src="https://agentmods.dev/badge/rules/titanium-birch/sftpwrangler/agent-bootstrap/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/rules/titanium-birch/sftpwrangler/agent-bootstrap"><img src="https://agentmods.dev/badge/rules/titanium-birch/sftpwrangler/agent-bootstrap.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 | $0.00067 | $0.00067 |
| Opus 5.5 | $0.00027 | $0.00027 |
| Sonnet 5.5 | $0.00013 | $0.00013 |
| Haiku 4.5 | $0.00007 | $0.00007 |
Grade A, and why
agent-bootstrap 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 1mo 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.
What it actually says
Hello, welcome to the project! A few points to help you get started:
- You're in a devcontainer. Therefore, to change dependencies, modify the source, run
poetry lock, then ask the user to rebuild the container. - Use poetry to run python
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.
- 1mo ago First seen · 12 lines · 67 tokens per session scan A 94498ae37fec
agent-bootstrap is a cursor rule published in the GitHub repository Titanium-Birch/SFTPWrangler (5 stars, last pushed 6d ago), licensed MIT. It adds 67 tokens to every session, about $0.0003 per session on Opus 5.5. 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 cursor rules, from other repositories
k8s
Kubernetes and Helm review and scaffolding for EKS workloads. Use when user says 'review my helm values', 'before I deploy', 'scaffold a new service', 'check values.yaml', or when working in values.yaml, Chart.yaml, or Helm template files.
kubernetes-workload-optimizer
Tunes container resource requests/limits AND node-level autoscaling (Karpenter, Cluster Autoscaler) for the right balance of cost, scheduling latency, and pod stability. Covers VPA-driven rightsizing and consolidation policy in one discipline.
container-rightsizer
Rightsizes container CPU and memory requests and limits using real usage data. Reduces requested resources without hitting OOMKills or CPU throttling.
cluster-autoscaler-tuner
Tunes Cluster Autoscaler and Karpenter for the right balance of responsiveness, bin-packing, and spot-aware consolidation. Reduces idle capacity without degrading scheduling latency.
docker
Docker operations, Dockerfile best practices, Compose, image optimization, and registry workflows. Use when user says 'review my Dockerfile', 'optimize my image', 'reduce image size', 'container won't start', 'set up compose', 'multi-stage build', or when working in Dockerfile, docker-compose.yml, or .dockerignore…
python-containerization-cursorrules-prompt-file
Cursor rules for Python development with containerization integration.