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 hamzabellouch/agent-skills --skill agent-platform-rag-engine-managementgit clone --depth 1 https://github.com/hamzabellouch/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/hamzabellouch/agent-skills/agent-platform-rag-engine-management)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/agent-platform-rag-engine-management"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-rag-engine-management/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/hamzabellouch/agent-skills/agent-platform-rag-engine-management"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/agent-platform-rag-engine-management.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 | $0.00085 | $0.02042 |
| Opus 5 | $0.00043 | $0.01021 |
| Sonnet 5 | $0.00017 | $0.00408 |
| Haiku 4.5 | $0.00009 | $0.00204 |
Grade A, and why
agent-platform-rag-engine-management 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 10d 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 agent-platform-rag-engine-management — 0 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 — 247 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Platform RAG Engine Management
This skill provides instructions on how to interact with Agent Platform RAG
Engine using the Agent Platform Python SDK. You
MUST use the vertexai Python SDK to perform RAG Engine operations, rather than
raw REST calls or MCP tools, because this code is intended to be run by external
clients.
Safety & Confirmation Tiers (CRITICAL)
Before executing any commands or scripts on behalf of the user, you must adhere to the following safety tiers based on the action requested:
- Tier R: Read-only (
list_corpora,list_files,get_corpus,retrieval_query)- No confirmation needed. Execute immediately to gather information or retrieve grounded contexts.
- Tier RC: Read-only but consumes Compute Resources (
client.models.generate_content)- Requires interactive confirmation with 'Yes'/'No' options before executing grounded content generation. The confirmation prompt MUST clearly explain the proposed generation execution and its key parameters (e.g., target corpus ID, query text, target model). Natural-language paraphrases without specifying exact parameters are insufficient, as explicit parameter listing is required to ensure unambiguous user approval of the specific resource and configuration.
- Same-turn restriction: Do not execute the generation code in the same turn as presenting the confirmation prompt. Stop and wait for the user's reply; only execute after explicit 'Yes' / approval.
- Gold Standard Example:
I will perform grounded content generation with the following parameters. Please confirm this information before I proceed:
- Target Corpus ID:
projects/123/locations/us/ragCorpora/abc - Target Model:
gemini-2.5-pro - Query Text: "What are the company policies on remote work?" Do you confirm? [Yes/No]
- Target Corpus ID:
Phase 0: Environment Setup
CRITICAL: Before running any of the Python snippets below, you must ensure the environment is correctly initialized by following these steps:
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.
- 10d ago First seen · 247 lines · 85 tokens per session scan A 9c5eef7e2c29
agent-platform-rag-engine-management is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 2,042 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 agent-platform-rag-engine-management, differing in 0 lines, and is treated as a copy.
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