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/kensaurus/cursor-kenjiWrote 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/commands/kensaurus/cursor-kenji/mobile-plan)<a href="https://agentmods.dev/commands/kensaurus/cursor-kenji/mobile-plan"><img src="https://agentmods.dev/badge/commands/kensaurus/cursor-kenji/mobile-plan/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/commands/kensaurus/cursor-kenji/mobile-plan"><img src="https://agentmods.dev/badge/commands/kensaurus/cursor-kenji/mobile-plan.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 | $0.00017 | $0.00169 |
| Opus 5.5 | $0.00007 | $0.00068 |
| Sonnet 5.5 | $0.00003 | $0.00034 |
| Haiku 4.5 | $0.00002 | $0.00017 |
Grade A, and why
mobile-plan 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
Mobile Readiness Plan
Run the plan-mobile-readiness skill: audit a Capacitor/React Native app
for App Store and Play submission readiness (privacy manifest, data-safety,
permissions, metadata), then produce a readiness plan. Audit and plan only —
edit no manifests until the plan is approved.
Emit findings ranked by severity (submission blockers first) with a phased
checklist. After approval, execute with plan-capacitor-hardening and the
relevant mobile skills.
The full playbook lives in the plan-mobile-readiness skill.
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 Changed · +2 lines cefeac3ae1e6
- 1mo ago First seen · 18 lines · 17 tokens per session scan A 7d024437e0f1
mobile-plan is a command published in the GitHub repository kensaurus/cursor-kenji (9 stars, last pushed 26d ago), licensed MIT. It adds 17 tokens to every session and 169 once invoked, about $0.0001 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 commands, from other repositories
preflight
Audit a build against App Review rejection triggers by app type before submitting.
code-review
Confidence-based code review checklist.
mcp
Switch MCP mode to optimize context window.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.