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 skills add gaelic-ghost/socket --skill app-intents-workflowgit clone --depth 1 https://github.com/gaelic-ghost/socketWrote 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/skills/gaelic-ghost/socket/app-intents-workflow)<a href="https://agentmods.dev/skills/gaelic-ghost/socket/app-intents-workflow"><img src="https://agentmods.dev/badge/skills/gaelic-ghost/socket/app-intents-workflow/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/skills/gaelic-ghost/socket/app-intents-workflow"><img src="https://agentmods.dev/badge/skills/gaelic-ghost/socket/app-intents-workflow.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 | $0.00059 | $0.01050 |
| Opus 5.5 | $0.00024 | $0.00420 |
| Sonnet 5.5 | $0.00012 | $0.00210 |
| Haiku 4.5 | $0.00006 | $0.00105 |
Grade A, and why
app-intents-workflow 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App Intents Workflow
Purpose
Expose the smallest useful action and content surface outside an app without turning system integration into a second app architecture. Apple documents App Intents as the bridge that makes app actions and data discoverable in system experiences; select real user-recognizable verbs and entities before adding an intent type.
When To Use
- Use for
AppIntent,AppEntity,AppEnum,AppShortcutsProvider, entity queries, result snippets, Spotlight indexing, widgets, controls, Live Activities, Siri, and Action button or Apple Pencil actions. - Use when an app action needs an explicit deep-link or scene-opening handoff after system invocation.
- Hand off Xcode target, extension, build, simulator, and runtime inspection work to
xcode-build-run-workflow; hand test plans and UI automation toxcode-testing-workflow.
Single-Path Workflow
- Apply the Apple docs gate through
explore-apple-swift-docs. Confirm the framework, platform availability, and target system surface before changing code. - Define one user-recognizable verb and the minimum data it needs. Prefer an existing app domain type; do not invent a parallel intent-only repository, store, or DTO layer.
- Classify the surface: direct action, parameterized action, entity lookup, App Shortcut, Spotlight result, widget/control action, Live Activity action, or hardware interaction.
- Read
references/intent-entity-and-shortcut-shapes.mdfor intent, entity, query, shortcut, and result patterns. Readreferences/system-surfaces-and-validation.mdfor indexing, extension boundaries, privacy, and validation. - Implement the narrow intent and make
perform()call the existing domain action or explicit app handoff. Keep parameter confirmation, failures, and result output descriptive and user-facing. - Add
AppEntityonly when people need to select, search, or inspect app-owned content. Give entity display representations stable identifiers and use the appropriate query shape; do not expose private, transient, or unverifiable data. - Add App Shortcuts only for actions people plausibly invoke repeatedly. Use short, natural phrases and real parameter defaults rather than advertising every internal operation.
- For widgets, controls, Live Activities, and Spotlight, validate the owning extension and system-surface contract rather than assuming the main app target alone is enough.
- Validate discovery, parameter resolution, cancellation/failure behavior, app handoff, accessibility, and privacy on the intended platform. Record system-surface limitations separately from app behavior.
What ships with it
4 files 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.
- 1mo ago First seen · 67 lines · 59 tokens per session scan A e932b37a63bc
app-intents-workflow is a skill published in the GitHub repository gaelic-ghost/socket (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,050 once invoked, about $0.0002 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 skills, from other repositories
app-store-optimization
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
flutter-screenshots
Automate App Store & Google Play screenshot generation for Flutter apps. Scans routing and screens, scores high-value marketing screens, generates self-contained mock harness, runs native simulator captures (iPhone 6.9", iPad 13", Android Phone/Tablet), strips alpha channels, and validates exact store resolutions.
flutter-ui
Build Flutter UI from Figma MCP or image input. Scans src for design tokens (colors, sizes, text styles), existing components, and naming conventions before writing a single line of code. Never hard-codes values.
flutter-dart-code-review
Library-agnostic Flutter/Dart code review checklist covering widget best practices, state management patterns (BLoC, Riverpod, Provider, GetX, MobX, Signals), Dart idioms, performance, accessibility, security, and clean architecture.
craft
Use when a product idea is still vague and needs to become a clear definition of what to build — "let's craft an app like X", "help me define what I actually want", "clarify this idea before we plan it". Also use before planning or implementation when requirements, UX, domain behaviour, or technical preferences have…
pipeline
Use when the user asks to take a substantial feature from an initial idea through implementation in one mostly autonomous run.