Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/waniwani-ai/sdk/knowledge-base)<a href="https://agentmods.dev/skills/waniwani-ai/sdk/knowledge-base"><img src="https://agentmods.dev/badge/skills/waniwani-ai/sdk/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 | $0.00034 | $0.01300 |
| Opus 5 | $0.00017 | $0.00650 |
| Sonnet 5 | $0.00007 | $0.00260 |
| Haiku 4.5 | $0.00003 | $0.00130 |
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.
How it starts
The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Set Up Knowledge Base
Add semantic search over markdown documents to an MCP project using the Waniwani KB API (client.kb).
Prerequisites
- Project must already be initialized (no
{{MCP_NAME}}placeholders) @waniwani/sdkmust be installedWANIWANI_API_KEYmust be set in the environment
Steps
1. Detect MCP name
Look in lib/ for the directory that isn't shared — that's the MCP name. Store as {MCP_NAME}.
2. Create knowledge directory
Create lib/{MCP_NAME}/knowledge-base/knowledge/ directory.
Ask the user: "Do you have .md files to add to the knowledge base, or should I create an example file?"
If no files provided, create lib/{MCP_NAME}/knowledge-base/knowledge/example.md:
# Example Knowledge Base
## What is this?
This is an example knowledge base entry. Replace this file with your own .md files containing information you want your AI assistant to be able to search through.
## How does it work?
Each .md file is split into chunks by H2 headings. The H1 title provides context for each chunk. Run `bun run kb:ingest` to upload your knowledge files to the Waniwani API.
3. Create the ingestion script
Create scripts/kb-ingest.ts (create scripts/ directory if it doesn't exist):
import { readdir, readFile } from "node:fs/promises";
import { join } from "node:path";
import { waniwani } from "@waniwani/sdk";
const knowledgeDir = join(import.meta.dirname, "../lib/{MCP_NAME}/knowledge-base/knowledge");
const mdFiles = (await readdir(knowledgeDir)).filter((f) => f.endsWith(".md"));
console.log(`Found ${mdFiles.length} knowledge file(s)`);
const files = await Promise.all(
mdFiles.map(async (filename) => ({
filename,
content: await readFile(join(knowledgeDir, filename), "utf-8"),
})),
);
const client = waniwani();
console.log("Ingesting files into knowledge base...");
console.log("⚠️ This will replace all existing KB chunks for this environment.");
const result = await client.kb.ingest(files);
console.log(`Done: ${result.chunksIngested} chunks from ${result.filesProcessed} files`);
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 · 181 lines · 34 tokens per session scan A 7cf1f5f72dfc
knowledge-base is a skill published in the GitHub repository WaniWani-AI/sdk (17 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,300 once invoked, about $0.0002 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.
Other skills, from other repositories
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
qdrant
Vector search engine for production RAG systems.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.
faiss
Fast vector similarity search at billion scale.
pinecone-research
Agent RAG and long-term memory with Pinecone.