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/ofcskn/mobile-automation-pluginWrote 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/ofcskn/mobile-automation-plugin/msd-setup-credentials)<a href="https://agentmods.dev/commands/ofcskn/mobile-automation-plugin/msd-setup-credentials"><img src="https://agentmods.dev/badge/commands/ofcskn/mobile-automation-plugin/msd-setup-credentials/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/ofcskn/mobile-automation-plugin/msd-setup-credentials"><img src="https://agentmods.dev/badge/commands/ofcskn/mobile-automation-plugin/msd-setup-credentials.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 | $0.00030 | $0.01383 |
| Opus 5.5 | $0.00012 | $0.00553 |
| Sonnet 5.5 | $0.00006 | $0.00277 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
msd-setup-credentials 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.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set up production credentials for store submission.
Load skills/submitting-app-release/references/credentials-guide.md before starting.
Step 1 — Choose storage path
Ask:
"Where should credentials be stored?
- EAS secrets (recommended) — encrypted in EAS cloud. Works in CI, GitHub Actions, and locally. Set once, works forever. Run
eas secret:listanytime to verify.- Local .env.production — stored in a local file. Faster to set up. Not available in CI unless you add it to your pipeline."
Step 2 — EXPO_TOKEN (required for both platforms)
Ask: "Do you have an Expo access token set? (check with eas whoami)"
If not:
- Direct user to: expo.dev → Account → Settings → Access Tokens → Create Token
- Ask user to paste the token (it starts with
expo_) - Validate format: must start with
expo_and be at least 20 chars - Store it:
# EAS secrets path
eas secret:create --scope project --name EXPO_TOKEN --value "<token>"
# Local path
echo 'EXPO_TOKEN=<token>' >> .env.production
Step 3 — iOS credentials (skip if Android-only)
App Store Connect API Key
-
Direct user to: App Store Connect → Users and Access → Integrations → App Store Connect API → Generate API Key
- Role: App Manager or higher
- Download the
.p8file — it can only be downloaded once
-
Collect three values:
APP_STORE_CONNECT_API_KEY_ID — shown on the key list (10 alphanumeric chars)
APP_STORE_CONNECT_ISSUER_ID — shown above the key list (UUID format)
APP_STORE_CONNECT_API_KEY_CONTENT — base64 of the .p8 file
- Generate the base64 content:
base64 -i AuthKey_XXXXXXXXXX.p8
# Copy the output — that is APP_STORE_CONNECT_API_KEY_CONTENT
- Validate each value before storing:
# Key ID: 10 alphanumeric chars
echo "KEYID" | grep -E '^[A-Z0-9]{10}$' && echo "✅ format valid" || echo "❌ invalid"
# Issuer ID: UUID
echo "ISSUERID" | grep -E '^[0-9a-f]{8}-([0-9a-f]{4}-){3}[0-9a-f]{12}$' && echo "✅ format valid" || echo "❌ invalid"
# Key content: must contain PEM header
echo "CONTENT" | grep "BEGIN PRIVATE KEY" && echo "✅ valid" || echo "❌ missing PEM header"
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 · 168 lines · 30 tokens per session scan A cd160bfe32a2
msd-setup-credentials is a command published in the GitHub repository ofcskn/mobile-automation-plugin (2 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 1,383 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
feat
Kick off tasks creation for a new feature (4-item interview before invoking pm-agent; an epic-sized requirement routes to phased decomposition, a feature already built on one platform routes to cross-platform parity, a change that touches no endpoint routes to client-only).
app-store-audit
Run an enterprise pre submission compliance audit on an iOS or Android app against Apple App Store and Google Play rejection rules. Pass a project path or run from the project root.
feature-build
Build a complete mobile feature from description to running code with E2E tests. Orchestrates 6 phases - planning, implementation, testing, build-fix, quality gate, and verification. Auto-detects platform.
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.