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 xberg-picking-a-formatgit 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/xberg-picking-a-format)<a href="https://agentmods.dev/skills/xberg-io/xberg/xberg-picking-a-format"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/xberg-picking-a-format/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/xberg-picking-a-format"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/xberg-picking-a-format.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 | $0.00051 | $0.00968 |
| Opus 5.5 | $0.00020 | $0.00387 |
| Sonnet 5.5 | $0.00010 | $0.00194 |
| Haiku 4.5 | $0.00005 | $0.00097 |
Grade A, and why
xberg-picking-a-format 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 yesterday.
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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Picking a format
Xberg has two orthogonal format knobs. Get them right up front and the downstream code stays simple.
| Knob | What it controls | Values | Default |
|---|---|---|---|
--format |
How the CLI prints the result | text, json, toon |
text (extract), json (batch) |
--content-format |
How extracted content is rendered inside result |
plain, markdown, djot, html, json, doctags |
plain |
--token-reduction |
Strip whitespace / boilerplate for LLM contexts | off, light, moderate, aggressive, maximum |
off |
--format json returns an envelope wrapping the ExtractedDocument — the
document lives under .result for extract and under .results[] for
batch, with content, metadata, tables, and images as fields of
that nested document. --format text prints just content.
--content-format is what shows up inside that content field.
Decision tree
Who consumes the output?
├── LLM (Claude, GPT, Gemini, local) — embed/prompt context
│ --format text --content-format markdown
├── Vector store / RAG indexer
│ --format json --content-format markdown
│ (markdown preserves structure for chunking)
├── Downstream parser that expects machine-readable JSON
│ --format json --content-format plain
│ (cleanest text + structured metadata)
├── Human review / archival
│ --format text --content-format markdown
├── HTML re-rendering / web display
│ --format json --content-format html
├── Lossless intermediate for pandoc / academic tooling
│ --format json --content-format djot
└── Token-budget-constrained pipeline
--format text --content-format plain
(drops markup; add --token-reduction moderate for further savings)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 104 lines · 51 tokens per session scan A dca750b31d57
xberg-picking-a-format is a skill published in the GitHub repository xberg-io/xberg (9,418 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 968 once invoked, about $0.0002 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 2026-10-15.
Other skills, from other repositories
leiloeiro-edital-ranbot-ai
Analise e auditoria de editais de leilao judicial e extrajudicial. Riscos ocultos, clausulas perigosas, debitos, ocupante e classificacao da oportunidade.
advogado-criminal
Advogado criminalista especializado em Maria da Penha, violencia domestica, feminicidio, direito penal brasileiro, medidas protetivas, inquerito policial e acao penal.
mcp-builder
DEPRECATED: This skill has been replaced by mcp-app-builder. Check if mcp-app-builder is available in the skills folder. If not, install it: npx skills install mcp-use/mcp-use --skill mcp-app-builder Use mcp-app-builder instead of this skill. Build Model Context Protocol (MCP) servers with mcp-use framework. Use when…
markitdown
Converts heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
thinking-out-loud
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's…
llamaindex
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM…