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 automateyournetwork/netclaw --skill raggit clone --depth 1 https://github.com/automateyournetwork/netclawWrote 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/automateyournetwork/netclaw/rag)<a href="https://agentmods.dev/skills/automateyournetwork/netclaw/rag"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/rag/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/automateyournetwork/netclaw/rag"><img src="https://agentmods.dev/badge/skills/automateyournetwork/netclaw/rag.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 | $0.00000 | $0.02672 |
| Opus 5 | $0.00000 | $0.01336 |
| Sonnet 5 | $0.00000 | $0.00534 |
| Haiku 4.5 | $0.00000 | $0.00267 |
Grade A, and why
rag 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 9d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: RAG Knowledge Base
Purpose: Give NetClaw a fully offline, user-curated document knowledge base — vendor guides, standards (RFC/IEEE/vendor), customer design documents, install guides — with agentic retrieval, mandatory citations, and opt-in point-in-time snapshots.
Overview
Users teach NetClaw by uploading documents (Slack attachment, HUD Knowledge panel, or URL). Documents are parsed, chunked structure-aware, embedded locally, and stored at ~/.openclaw/rag/. Retrieval is a tool NetClaw invokes on its own judgment — iteratively, with self-critique — never a fixed pipeline.
This is NOT memory. The knowledge base holds only what users deliberately put into it. NetClaw's own experience (facts, session summaries, decisions, entity graphs) lives in the Memory MCP (memory_* tools, ~/.openclaw/memory/). Neither store writes into the other.
MCP Tools
| Tool | WHEN to use |
|---|---|
rag_ingest |
A document file on disk should be learned |
rag_ingest_base64 |
A Slack attachment should be learned (decode → ingest) |
rag_ingest_url |
The user asks to ingest a web page (ALWAYS preview scope first) |
rag_search |
Question concerns vendor procedures, customer standards, install steps, or ingested content |
rag_list |
User asks what the knowledge base contains |
rag_stats |
User asks about corpus size/health or retrieval telemetry |
rag_update_metadata |
Fix a document's doc_type/title/version |
rag_delete |
User asks to remove a document (CONFIRM with the user first) |
rag_reindex |
Chunking/embedding config changed (CONFIRM with the user first) |
rag_snapshot |
User EXPLICITLY asks to store live output for later comparison (confirm scope first — never automatic) |
The Four Knowledge Sources (routing rules)
Route every question to the right source. Most questions need NO retrieval.
- Parametric knowledge — timeless networking fundamentals (OSPF LSA types, BGP path selection). Answer directly. Do not search.
- Memory MCP (
memory_recall,memory_get_facts,memory_get_decisions) — NetClaw's own past sessions, learned facts, and decisions about THIS network. "What was that BGP issue last month?" goes here, never torag_search. - RAG knowledge base (
rag_search) — user-uploaded documents. Vendor procedures, customer standards, install steps. Check it BEFORE declaring ignorance on these topics. - Live MCP servers (pyATS, NetBox, etc.) — current network state. NEVER answer a live-state question from the RAG store. The only exception is an explicitly requested snapshot, whose age must always be shown.
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.
- 9d ago First seen · 164 lines · 0 tokens per session scan A f3356ea3a8ef
rag is a skill published in the GitHub repository automateyournetwork/netclaw (657 stars, last pushed 6d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 2,672 tokens. 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 2026-09-03.
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