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 jamon8888/hacienda-private --skill chunking-embeddingsgit clone --depth 1 https://github.com/jamon8888/hacienda-privateWrote 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/jamon8888/hacienda-private/chunking-embeddings)<a href="https://agentmods.dev/skills/jamon8888/hacienda-private/chunking-embeddings"><img src="https://agentmods.dev/badge/skills/jamon8888/hacienda-private/chunking-embeddings/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/jamon8888/hacienda-private/chunking-embeddings"><img src="https://agentmods.dev/badge/skills/jamon8888/hacienda-private/chunking-embeddings.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 | $0.00016 | $0.01338 |
| Opus 5 | $0.00008 | $0.00669 |
| Sonnet 5 | $0.00003 | $0.00268 |
| Haiku 4.5 | $0.00002 | $0.00134 |
Grade A, and why
chunking-embeddings 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 10d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chunking & Embeddings
Text splitting strategies, embedding generation with FastEmbed, RAG pipeline integration
Chunking Architecture Overview
Location: crates/xberg/src/chunking/, crates/xberg/src/embeddings.rs
Extracted Text
|
[1. Normalization] -> Clean whitespace, remove control chars
|
[2. Chunk Strategy Selection] -> Fixed-size, semantic, syntax-aware, recursive
|
[3. Overlap Management] -> Control context window overlap
|
[4. Optional Embedding] -> Generate vectors with FastEmbed
|
Output: Vec<Chunk> with text, vectors, metadata
Chunking Strategies
Location: crates/xberg/src/chunking/mod.rs
| Strategy | Pattern | Best For |
|---|---|---|
| Fixed-Size | Sliding window with configurable overlap | Uniform chunks for embedding models with fixed token limits |
| Semantic | Split by sentences, merge/split by similarity threshold | Smart context preservation for LLM consumption and semantic search |
| Syntax-Aware | Split by paragraph/section/heading/code-block structure | Preserving document structure (sections, code blocks) in RAG |
| Recursive (LangChain pattern) | Try separators in order: \n\n, \n, , |
Best general-purpose chunking; auto-finds optimal split points |
Key config fields per strategy (see struct definitions in chunking/mod.rs):
- Fixed-Size:
chunk_size,overlap,trim_whitespace - Semantic:
target_chunk_size,min/max_chunk_size,semantic_threshold,use_sentence_boundaries - Syntax-Aware:
chunk_by(Paragraph/Section/Heading/Sentence/CodeBlock),max_chunk_size,respect_code_blocks - Recursive:
separators[],chunk_size,overlap
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.
- 10d ago First seen · 121 lines · 16 tokens per session scan A e8adfbc0870b
chunking-embeddings is a skill published in the GitHub repository jamon8888/hacienda-private (0 stars, last pushed 1mo ago), licensed MIT. It adds 16 tokens to every session and 1,338 once invoked, about $0.0001 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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