Xberg is a document-intelligence engine that reads files, URLs, archives, and source trees and extracts text, metadata, images, tables, and structured data, with additional code-language understanding. Developers use it through language bindings, a command-line tool, REST API, or MCP server, and the catalogue entries support those integrations.
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 xberg-io/xberg --skill chunking-embeddingsgit clone --depth 1 https://github.com/xberg-io/xbergWrote 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/xberg-io/xberg/chunking-embeddings)<a href="https://agentmods.dev/skills/xberg-io/xberg/chunking-embeddings"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/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/xberg-io/xberg/chunking-embeddings"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/chunking-embeddings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.01339 |
| 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 9d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chunking & Embeddings
Text splitting, ONNX/static embedding generation, RAG pipeline integration
Locations: crates/xberg/src/chunking/ and crates/xberg/src/embeddings/ (both directories,
not single files).
Chunking
ExtractionConfig.chunking: Option<ChunkingConfig> drives it. The standalone entry points are
chunking::chunk_text(text, &ChunkingConfig, page_boundaries) -> Result<ChunkingResult>
(chunking/core.rs) and chunking::rag::chunk_for_rag(text, &ChunkingConfig)
(chunking/rag.rs), which upgrades ChunkerType::Text to Markdown and fills each chunk's
heading_path.
ChunkingResult { chunks: Vec<Chunk>, chunk_count: usize }. Chunk carries content,
chunk_type, metadata, and the optional vectors embedding, sparse_embedding,
late_interaction (types/extraction.rs).
ChunkerType — there is no strategy enum beyond this
Text (default), Markdown, Yaml, Semantic (core/config/processing.rs).
Semantic splits at embedding-based topic shifts when an EmbeddingConfig is present, and
falls back to a structural-boundary heuristic otherwise — topic_threshold has no effect on
the fallback path.
ChunkingConfig fields and their serde wire names
| Field | Wire name (config file) | Default |
|---|---|---|
max_characters |
max_chars (alias max_characters) |
1000 |
overlap |
max_overlap (alias overlap) |
200 |
trim |
trim |
true |
chunker_type |
chunker_type |
Text |
preset |
preset |
none |
The renames are load-bearing: a config file that writes max_characters works only via the
alias, and a typo'd key is silently ignored (see config-loading-precedence).
Presets set chunk size AND the embedding model
ChunkingConfig.preset resolves through resolve_preset(), which is
#[cfg(feature = "embeddings")]-gated — without that feature it is a no-op and the preset
name does nothing. A preset overrides max_characters and overlap and, if no embedding
config was given, selects the model.
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.
- 9d ago First seen · 110 lines · 16 tokens per session scan A 89cbfd1aecb0
chunking-embeddings is a skill published in the GitHub repository xberg-io/xberg (9,275 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 1,339 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 2026-08-30.
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