Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/temporary111111/agent-mcp-gateway-v2npx agentmods add skills/temporary111111/agent-mcp-gateway-v2/knowledge-baseWrote 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/temporary111111/agent-mcp-gateway-v2/knowledge-base)<a href="https://agentmods.dev/skills/temporary111111/agent-mcp-gateway-v2/knowledge-base"><img src="https://agentmods.dev/badge/skills/temporary111111/agent-mcp-gateway-v2/knowledge-base.svg" alt="Measured on agentmods" 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 | $0.00112 | $0.02165 |
| Opus 5 | $0.00056 | $0.01082 |
| Sonnet 5 | $0.00022 | $0.00433 |
| Haiku 4.5 | $0.00011 | $0.00216 |
Grade A, and why
knowledge-base 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 6d 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 knowledge-base — 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 — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Base (Markdown, agent-readable)
A knowledge base (KB) here is a folder of Markdown files designed so any AI agent can navigate it without a vector database: the agent reads one index file, picks the relevant notes by their descriptions, and opens only those. Keep the whole KB readable and the index lean — the index is what gets loaded into context, so it must be high-signal.
Apply the rules below when creating a new KB, adding/editing notes, or doing a maintenance pass. When the user's request is ambiguous (new KB vs. add note vs. cleanup), ask which one before acting.
Core principles (the "why")
- Index-first navigation. The index is a map, not a container. An agent
reads
INDEX.md, selects notes by their one-line descriptions, then opens only those files. No note is "in" the KB unless it's registered in the index. - Atomicity. One topic per note. If a title needs an "and", it's probably two notes. Atomic notes are easier to find, link, and reuse.
- Stable IDs. Every note has a permanent ID that never changes and is never reused, so links survive renames and moves.
- Linked with reason. When two notes connect, state why (prerequisite, supports, contrasts, see-also). Connections carry as much value as the notes.
- Lean instructions. Keep
INDEX.mdinstructions short and high-signal — it competes for the agent's context budget. Bodies load on demand. - Structured for retrieval. Clear H1 title and H2/H3 sections so an agent can grab the relevant slice of a note, not the whole thing.
Folder structure (nested by topic)
knowledge-base/
INDEX.md # entry point: instructions + full note registry
topics/
<topic>/
_topic.md # topic map: what this topic covers + its notes
<slug>.md # one atomic note
assets/ # images / attachments referenced by notes
- Topic folders are short, lowercase nouns (
auth,billing,deploys). _topic.mdis the topic-level map of content (MOC): a short intro plus links to every note in that topic. It is a convenience view;INDEX.mdremains the source of truth.
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.
- 6d ago First seen · 199 lines · 112 tokens per session scan A 34ca1c1fd9e6
knowledge-base is a skill published in the GitHub repository temporary111111/agent-mcp-gateway-v2 (0 stars, last pushed 16d ago), licensed MIT. It adds 112 tokens to every session and 2,165 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to knowledge-base, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
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Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
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Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
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Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.