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 prasad-vennam/Awesome-Android-AI-Agent-Skills --skill android-rag-localgit clone --depth 1 https://github.com/prasad-vennam/Awesome-Android-AI-Agent-SkillsWrote 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/prasad-vennam/awesome-android-ai-agent-skills/android-rag-local)<a href="https://agentmods.dev/skills/prasad-vennam/awesome-android-ai-agent-skills/android-rag-local"><img src="https://agentmods.dev/badge/skills/prasad-vennam/awesome-android-ai-agent-skills/android-rag-local/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/prasad-vennam/awesome-android-ai-agent-skills/android-rag-local"><img src="https://agentmods.dev/badge/skills/prasad-vennam/awesome-android-ai-agent-skills/android-rag-local.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 | $0.00017 | $0.00287 |
| Opus 5 | $0.00009 | $0.00143 |
| Sonnet 5 | $0.00003 | $0.00057 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
android-rag-local 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 11d 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
Android Local RAG & Vector Databases 🔎
Retrieval-Augmented Generation (RAG) on Edge devices provides privacy-first, low-latency contextual intelligence. Instead of using cloud Pinecone/Weaviate, this skill enforces local vector embeddings via Room or SQLite.
⚡ When to Use
- When tasked with "RAG", "semantic search", or "vector embeddings".
- Building highly private AI features.
🏗️ Core Rules / Pillars
1. Vector Search in Room
- Pattern: Standard Room does not have native vector similarity out of the box in stable. Use L2 distance extensions or
sqlite-vss. - Implementation: Avoid pulling unmaintained third-party vector DBs. Use SQLite virtual tables with FTS or simple cosine similarity functions if embeddings are small.
🚧 Critical Anti-Hallucination Guards
- Trap: Do NOT hallucinate that Room natively supports
SELECT * FROM tbl ORDER BY VECTOR_DISTANCE()securely out of the box without specific extensions.
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.
- 11d ago First seen · 31 lines · 17 tokens per session scan A 5d2c7f2d3419
android-rag-local is a skill published in the GitHub repository prasad-vennam/Awesome-Android-AI-Agent-Skills (9 stars, last pushed 5mo ago), licensed MIT. It adds 17 tokens to every session and 287 once invoked, about $0.0001 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.
Other skills, from other repositories
ai-native-knowledge-rag
A workflow for designing a knowledge system that lets an AI find answers in company documents. RAG means retrieving relevant source material before generating an answer.
ai-native-knowledge-rag
A practical guide to RAG and knowledge-system design. RAG, or retrieval-augmented generation, lets an AI find relevant information from a knowledge source before producing an answer.
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.