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 benchmark-workflowgit 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/benchmark-workflow)<a href="https://agentmods.dev/skills/xberg-io/xberg/benchmark-workflow"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/benchmark-workflow/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/benchmark-workflow"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/benchmark-workflow.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 | $0.00043 | $0.00547 |
| Opus 5.5 | $0.00017 | $0.00219 |
| Sonnet 5.5 | $0.00009 | $0.00109 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
benchmark-workflow 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 8d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark workflow
The benchmark system lives in tools/benchmark-harness/; the GitHub workflow is
.github/workflows/benchmarks.yaml. The workflow is dispatch-only, so it does not run on push or gate merges. Treat
a result as evidence for its exact commit SHA and inputs, not for newer local work.
Ground-truth integrity
- Never use Xberg's own extractor output as benchmark ground truth. Use an independent source and record it in the
fixture's
ground_truth.sourcefield (manual,vision,pdf_text_layer,pandoc,python-docx, and similar). - Before blaming ground truth for a score, render or otherwise inspect the source document. If the derived
.mdor.txtdisagrees with the source, fix the ground truth; if it agrees, investigate the extractor or metric. - Use the fixture schema in
tools/benchmark-harness/README.md, the generator attools/benchmark-harness/scripts/generate_markdown_gt.py, and the harnessvalidate-gtcommand implemented intools/benchmark-harness/src/validate_gt.rs. Do not replace these with an ad-hoc conversion pipeline. - A quality claim requires the same corpus, config, renderer, cache state, and metric on control and experiment. Disable or invalidate extraction and OCR caches before A/B runs whose output behavior changed.
Diagnosing runs
- Separate infrastructure failures from extraction or quality failures. A missing backend library, absent fixture, malformed artifact, or runner setup error does not describe extractor quality.
- Inspect the per-adapter artifacts before the aggregate job. Aggregate contract failures may be caused by missing or unexpectedly named artifacts even when individual adapters ran.
- Compare accepted OCR pages before raw word counts. Rejected OCR pages contribute neither text nor structured paragraphs.
- Measure headings and lists using Markdown output. Plain output normalizes away list markers and cannot distinguish detection from rendering.
- Do not quote a coverage, latency, or quality threshold unless the workflow or harness currently enforces it.
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.
- 8d ago Changed · +3 lines · -4 tokens per session ce8e27ce2d8e
- 1mo ago First seen · 41 lines · 47 tokens per session scan A 6802ba9ffa18
benchmark-workflow is a skill published in the GitHub repository xberg-io/xberg (9,428 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 547 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 2026-08-30.
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