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 johnsonhk88/AI-Bank-Statement-Document-Automation-By-LLM-And-Personal-Finanical-Analysis-Prediction --skill rag-query-handlinggit clone --depth 1 https://github.com/johnsonhk88/AI-Bank-Statement-Document-Automation-By-LLM-And-Personal-Finanical-Analysis-PredictionWrote 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/johnsonhk88/ai-bank-statement-document-automation-by-llm-and-personal-finanical-analysis-prediction/rag-query-handling)<a href="https://agentmods.dev/skills/johnsonhk88/ai-bank-statement-document-automation-by-llm-and-personal-finanical-analysis-prediction/rag-query-handling"><img src="https://agentmods.dev/badge/skills/johnsonhk88/ai-bank-statement-document-automation-by-llm-and-personal-finanical-analysis-prediction/rag-query-handling/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/johnsonhk88/ai-bank-statement-document-automation-by-llm-and-personal-finanical-analysis-prediction/rag-query-handling"><img src="https://agentmods.dev/badge/skills/johnsonhk88/ai-bank-statement-document-automation-by-llm-and-personal-finanical-analysis-prediction/rag-query-handling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00019 | $0.00088 |
| Opus 5 | $0.00010 | $0.00044 |
| Sonnet 5 | $0.00004 | $0.00018 |
| Haiku 4.5 | $0.00002 | $0.00009 |
Grade A, and why
rag-query-handling 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.
What it actually says
RAG Query Handling Skill
Rules
- After storing data in Vector DB, always prefer using the
rag_toolto answer questions instead of relying only on memory. - Use RAG especially when the document is long or detailed.
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 · 11 lines · 19 tokens per session scan A 404fad19129c
rag-query-handling is a skill published in the GitHub repository johnsonhk88/AI-Bank-Statement-Document-Automation-By-LLM-And-Personal-Finanical-Analysis-Prediction (627 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 88 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 2026-08-30.
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ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.