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 agentmods add instructions/kimsb2429/internal-knowledge-base/claude-mdgit clone --depth 1 https://github.com/kimsb2429/internal-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/instructions/kimsb2429/internal-knowledge-base/claude-md)<a href="https://agentmods.dev/instructions/kimsb2429/internal-knowledge-base/claude-md"><img src="https://agentmods.dev/badge/instructions/kimsb2429/internal-knowledge-base/claude-md.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 | $0.00821 | $0.00821 |
| Opus 5 | $0.00411 | $0.00411 |
| Sonnet 5 | $0.00164 | $0.00164 |
| Haiku 4.5 | $0.00082 | $0.00082 |
Grade A, and why
internal-knowledge-base CLAUDE.md 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 3d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
internal-knowledge-base
VA Education RAG pipeline — from raw corpus to MCP server.
Zero-to-MCP plan
The canonical build plan is the "From Zero to Knowledge MCP" section in docs/2026-04-11-engineering-rag-evidence-and-howtos.md. The plan doc is authoritative for any "zero to mcp" references.
Standing rules
Post-eval analysis protocol (apply after every eval run — baseline, Loop A iteration, Loop B iteration, anywhere): never report aggregate metrics alone. Always follow with (a) per-query_type segmentation, (b) 2-3 representative failures pulled per failure cluster with full chunk/judge reasoning, (c) named failure patterns with concrete mechanisms, (d) mapping from each pattern to a specific plan step / lever. The plan doc (§ "From Zero to Knowledge MCP", step 9) has the full template. Anti-pattern: proposing the next intervention directly from aggregate deltas — that tunes blindly.
Key decisions
- HTML articles stay as HTML — no markdown conversion. Markdown is lossy for tables (colspan/rowspan). LLMs read HTML fine. One code path for all 237 HTML articles.
- Markdown only for VADIR ICD PDF — no HTML source, so parsed markdown +
MarkdownHeaderTextSplitteris the right path. - Generic chunker + source-specific preprocessors —
chunk_documents.pyis source-agnostic; each source gets its own<source>_preprocess.pythat normalizes HTML quirks (headings, layout tables, div wrappers) before chunking. Adding a new source means writing a preprocessor, not forking the chunker. - Custom HTML splitter — LangChain's
HTMLHeaderTextSplitterstrips HTML tags, destroying table structure. We use our own (split_html_by_headings+split_chunk_by_elementsinchunk_documents.py) that preserves raw HTML. - Row-group splitting for tables >50K tokens — above 50K, summary indexing breaks down (full content can't be served in a 200K context window). Split into row-groups with repeated headers; each chunk is self-contained.
- Heading-only
embed_textfor oversized chunks — instead of LLM-generated summaries ($13 in Sonnet calls), usetitle + heading_pathfor the embed vector. Full content still served at generation. If eval shows content-level queries failing, add aretrieve_full_docMCP method or upgrade to LLM summaries in Loop A. - Schema:
embed_textholds what was embedded (content for regular chunks, heading-only for oversized);contentalways holds the full original.
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.
- 3d ago First seen · 36 lines · 821 tokens per session scan A 715d7302ea82
internal-knowledge-base CLAUDE.md is an instructions file published in the GitHub repository kimsb2429/internal-knowledge-base (0 stars, last pushed 3mo ago), licensed MIT. It adds 821 tokens to every session, about $0.0041 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 2026-08-31.
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