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 lyonzin/knowledge-rag --skill rag-onboard-contextgit clone --depth 1 https://github.com/lyonzin/knowledge-ragWrote 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/lyonzin/knowledge-rag/rag-onboard-context)<a href="https://agentmods.dev/skills/lyonzin/knowledge-rag/rag-onboard-context"><img src="https://agentmods.dev/badge/skills/lyonzin/knowledge-rag/rag-onboard-context.svg" alt="Measured on agentmods" 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 | $0.00063 | $0.01429 |
| Opus 5 | $0.00032 | $0.00714 |
| Sonnet 5 | $0.00013 | $0.00286 |
| Haiku 4.5 | $0.00006 | $0.00143 |
Grade A, and why
rag-onboard-context 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 8d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rag-onboard-context — know your corpus before you use it
When to use this skill
Run this skill:
- At the start of any new conversation where knowledge-rag is available and the user is about to ask substantive questions
- When the topic shifts significantly (from security to infrastructure, from dev to research)
- After a major reindex (the corpus content changed under you)
- When the user says "I just indexed new docs" or similar
Do NOT run repeatedly — once per session is usually enough. The query_cache keeps it cheap even if you do.
What this skill commits to
Before diving into task-specific work, the agent gathers a mental map of the corpus:
- How big — chunk count, document count, cache health
- What is in it — which categories exist, roughly how many docs per category
- What flavor — probe 1–2 topics the user is likely to ask about, see if there are hits
That mental map informs every subsequent decision: whether to search first, whether to fall back to the web, how to phrase queries.
Steps
-
Get index health:
get_index_stats()Note:
documents_count,chunks_count,cache_hit_rate,embedding_model,embedding_dim. -
Enumerate categories:
list_categories()Note: which categories exist and how many docs each has. This tells you the corpus's shape.
-
Probe 1–2 topics the user is likely to touch. If the user's first message mentions a domain, probe it. Otherwise, probe the top 2 largest categories:
search_knowledge(query="<category-typical-term>", max_results=3, snippet_mode=true) -
Optionally, if you need concrete file names, call:
list_documents(category="<top-category>", limit=10) -
Store the summary internally — do not necessarily surface it to the user unless they ask. The value is that YOU now know:
- "The corpus has 15K chunks across 8 categories"
- "Biggest categories are security (5K), development (3K), research (2K)"
- "Search for
mitre attackreturned 3 solid hits — MITRE content is well indexed" - "Search for
kubernetesreturned nothing — no k8s docs indexed, defer to general knowledge"
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.
- 8d ago First seen · 137 lines · 63 tokens per session scan A 0f97de073319
rag-onboard-context is a skill published in the GitHub repository lyonzin/knowledge-rag (273 stars, last pushed 3d ago), licensed MIT. It adds 63 tokens to every session and 1,429 once invoked, about $0.0003 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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