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/hyh0620/mcp-knowledge-servicenpx agentmods add skills/hyh0620/mcp-knowledge-service/ingest-knowledgeWrote 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/hyh0620/mcp-knowledge-service/ingest-knowledge)<a href="https://agentmods.dev/skills/hyh0620/mcp-knowledge-service/ingest-knowledge"><img src="https://agentmods.dev/badge/skills/hyh0620/mcp-knowledge-service/ingest-knowledge/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/hyh0620/mcp-knowledge-service/ingest-knowledge"><img src="https://agentmods.dev/badge/skills/hyh0620/mcp-knowledge-service/ingest-knowledge.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 | $0.00027 | $0.00227 |
| Opus 5.5 | $0.00011 | $0.00091 |
| Sonnet 5.5 | $0.00005 | $0.00045 |
| Haiku 4.5 | $0.00003 | $0.00023 |
Grade A, and why
ingest-knowledge 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 1mo 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.
What it actually says
Ingest Knowledge
Inputs
- Source path.
- Collection name.
- Optional
--dry-run.
Pipeline
- Dry run:
python scripts/ingest.py --path <SOURCE_PATH> --collection <COLLECTION> --dry-run - Ingest:
python scripts/ingest.py --path <SOURCE_PATH> --collection <COLLECTION> --force - Verify query:
python scripts/query.py --query "<QUERY>" --collection <COLLECTION> --top-k 4 - Inspect runtime counts from command output and local ChromaDB/BM25 metadata.
Output
- Files processed.
- Failed files.
- Chunk count.
- Vector count.
- BM25 document count.
- Collection name.
Rules
- Do not commit
data/,logs/, traces, or generated runtime reports.
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.
- 1mo ago First seen · 42 lines · 27 tokens per session scan A 5b89385352f4
ingest-knowledge is a skill published in the GitHub repository hyh0620/mcp-knowledge-service (0 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 227 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-01.
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A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.