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 GDvega/super-android-kotlin-firebase-skill --skill firebase-ai-logicgit clone --depth 1 https://github.com/GDvega/super-android-kotlin-firebase-skillWrote 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/gdvega/super-android-kotlin-firebase-skill/firebase-ai-logic)<a href="https://agentmods.dev/skills/gdvega/super-android-kotlin-firebase-skill/firebase-ai-logic"><img src="https://agentmods.dev/badge/skills/gdvega/super-android-kotlin-firebase-skill/firebase-ai-logic/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/gdvega/super-android-kotlin-firebase-skill/firebase-ai-logic"><img src="https://agentmods.dev/badge/skills/gdvega/super-android-kotlin-firebase-skill/firebase-ai-logic.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 | $0.00033 | $0.00595 |
| Opus 5 | $0.00016 | $0.00298 |
| Sonnet 5 | $0.00007 | $0.00119 |
| Haiku 4.5 | $0.00003 | $0.00060 |
Grade A, and why
firebase-ai-logic 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 12d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Add Gemini/AI features to Android apps safely, with clear privacy and fallback behavior.
When to use
- Adding Firebase AI Logic or Gemini features.
- Designing prompts or multimodal input.
- Choosing cloud vs on-device AI.
- Handling model errors, limits and fallback UX.
Inputs to inspect
- AI use case and user value.
- Data sent to model and privacy classification.
- Firebase/Google AI provider choice.
- Fallback and human review requirements.
Required workflow
- Classify data and decide if AI is appropriate.
- Choose Firebase AI Logic, backend proxy or on-device path.
- Design prompts and output validation.
- Handle rate limits, safety and offline fallback.
- Add fakes/tests for AI boundaries.
Rules
- Do not expose private API keys in the client.
- Minimize sensitive data sent to models.
- Do not rely on AI for critical irreversible decisions without review.
- Validate structured outputs.
- Provide deterministic fallback.
Related existing skills
Local skills to invoke
- firebase-core
- security-privacy
- networking-retrofit-ktor
- ui-state-design
- testing
External companion skills to use when installed
Do not assume these companion skills are installed. Prefer the local skills above first, then consult Companion Skills for install and verification commands.
- firebase/agent-skills — use for deeper Firebase product, Firestore, Security Rules or emulator workflow guidance when installed.
Files commonly touched
AI repository/serviceprompt templatesViewModel stateFirebase setuptests/fakes
Commands to validate
./gradlew test
./gradlew assembleDebug
firebase emulators:start
Common mistakes to avoid
- Hardcoding secret keys.
- Sending unnecessary PII.
- Trusting AI output without validation.
- No fallback or error UX.
Checklist
- Privacy reviewed.
- Provider chosen.
- Prompt/output validated.
- Fallback exists.
- Tests/fakes included.
Example prompts
What ships with it
2 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.
- 12d ago First seen · 101 lines · 33 tokens per session scan A 732655adbbed
firebase-ai-logic is a skill published in the GitHub repository GDvega/super-android-kotlin-firebase-skill (2 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 595 once invoked, about $0.0002 per session on Opus 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.
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