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 agentmods add skills/arindamxd/camerax-android/appfunctionsnpx skills add arindamxd/camerax-android --skill appfunctionsgit clone --depth 1 https://github.com/arindamxd/camerax-androidWrote 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/arindamxd/camerax-android/appfunctions)<a href="https://agentmods.dev/skills/arindamxd/camerax-android/appfunctions"><img src="https://agentmods.dev/badge/skills/arindamxd/camerax-android/appfunctions.svg" alt="Measured on agentmods" 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 | $0.00089 | $0.00947 |
| Opus 5 | $0.00044 | $0.00474 |
| Sonnet 5 | $0.00018 | $0.00189 |
| Haiku 4.5 | $0.00009 | $0.00095 |
Grade A, and why
appfunctions 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 5d 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.
This is a copy
100% identical to appfunctions — 22 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzes Android apps to identify key user workflows for AppFunctions such as creating a note, playing media, or sending an automated or AI agent triggered message, voice commands, or system shortcuts, without needing to open the app UI.
Generates Kotlin code to expose these workflows to the Android system, allowing agents to discover and execute them on-device.
Also refines KDoc documentation to ensure AI agents correctly understand and use the provided functionality.
Prerequisites
The app must targetSdk 36 or newer and use compileSdk 37 or newer as AppFunctions, part of the Android platform API, are available from Android 16 onwards. Always use the Jetpack library because it handles backward compatibility.
Workflows
This skill enables the caller to discover features that will be provided to system agents, implement these with AppFunctions, improve function description for agents, and use ADB commands for local evaluation and testing.
The full AppFunction development flow consists of these four steps:
- Step 1: Discovery: Analyze Android codebases to identify and recommend potential AppFunctions. Use this step when a user asks to "discover AppFunctions," "find features for AI," or "analyze my app for agentic tools."
- Step 2: Implementation and configuration: Generate Kotlin implementations of AppFunctions, manage system-wide configuration, and configure build dependencies. Use this step when a user asks to "implement AppFunctions," "set up the AppFunctions framework," or "configure Hilt for AppFunctions."
- Step 3: KDoc refinement: Optimize AppFunction KDoc for AI agents and Model Context Protocol. If a user asks to "write KDoc", "optimize for MCP", or "refactor tool descriptions for LLMs", use this step.
- Step 4: Testing and debugging: Provide commands to interact with AppFunctions using ADB for testing and debugging. If a user wants to "list app functions", "invoke an app function", or "verify app function registration" on a device, use this step.
What ships with it
6 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.
- 5d ago First seen · 62 lines · 89 tokens per session scan A ada02a3a8f74
appfunctions is a skill published in the GitHub repository arindamxd/camerax-android (132 stars, last pushed 6d ago), licensed Apache-2.0. It adds 89 tokens to every session and 947 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to appfunctions, differing in 22 lines, and is treated as a copy.
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