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/chohra-med/expo_boilerplateWrote 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/chohra-med/expo_boilerplate/plan)<a href="https://agentmods.dev/commands/chohra-med/expo_boilerplate/plan"><img src="https://agentmods.dev/badge/commands/chohra-med/expo_boilerplate/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/chohra-med/expo_boilerplate/plan"><img src="https://agentmods.dev/badge/commands/chohra-med/expo_boilerplate/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 | $0.00000 | $0.00257 |
| Opus 5 | $0.00000 | $0.00129 |
| Sonnet 5 | $0.00000 | $0.00051 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
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 10d 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
Command: plan — spec → design (the HOW)
The flexible "how" on top of the stable "what". Architecture decisions live here, not in the spec. Run the researcher first so the design references real, existing code.
Invocation
spec-harness plan --feature <slug>
Reads spec.md (+ optional research.md), writes specs/<slug>/design.md.
The design must contain
- Architecture map — the layers/modules and how data flows between them (a diagram).
- Key decisions — each as: context → options → choice → reason. These double as the
interview talking track and seed
.memory/60-decisions.md. - The one hard part — most case studies hinge on a single tricky thing (an algorithm, a streaming contract). Name it, and the approach, explicitly.
- Reuse — which existing symbols (from research.md) get used instead of rebuilt.
- File structure — the target tree.
Rule
Decisions must be justifiable out loud. If AI usage gets grilled (it will), every choice in this file is something the human reads back and defends without notes.
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.
- 10d ago First seen · 24 lines · 0 tokens per session scan A 0f59d91d483f
plan is a command published in the GitHub repository chohra-med/expo_boilerplate (32 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 257 tokens. 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.
Other commands, from other repositories
arc-skill
You are scaffolding a new React Native (Expo) project using the arc-skill architecture.
arc-connect
Connect API layer, storage, state management, authentication, and i18n to an existing React Native Expo project. Use after arc-scaffold to add backend integration. Trigger when the user mentions adding API calls, auth flow, login, storage, state management, internationalization, backend connection, or data fetching to…
arc-scaffold
Scaffold a new React Native Expo project with folder structure, dependencies, TypeScript config, linting, and navigation shell. Use when starting a new mobile app from scratch, when the user says 'create a new app', 'start a project', 'set up React Native', 'init Expo app', or needs project boilerplate. Trigger this…
arc-ui
Add theme system, reusable components, screen layouts, and mobile UX design to a React Native Expo project. Use after arc-scaffold to build the visual layer. Trigger when the user wants to add a theme, dark mode, UI components, buttons, inputs, screen layouts, design system, or visual polish to their React Native app.
arc-audit
Audit a React Native Expo project for mobile UX issues, performance problems, and architecture violations. Use to check code quality against arc-skill best practices. Trigger when the user asks to review code, check for issues, wants a health check, mentions touch targets, accessibility, performance audit, or says 'is…
arc-feature
Add a complete new feature domain to an existing React Native Expo project — types, API module, React Query hooks, screen, and components in one go. Use when adding a new entity like products, orders, chat, users, recipes, or any CRUD resource. Trigger when the user says 'add a feature', 'create a new screen for X'…