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-extracting-keywordsgit 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-extracting-keywords)<a href="https://agentmods.dev/skills/xberg-io/xberg/xberg-extracting-keywords"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/xberg-extracting-keywords/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-extracting-keywords"><img src="https://agentmods.dev/badge/skills/xberg-io/xberg/xberg-extracting-keywords.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 | $0.00058 | $0.01630 |
| Opus 5.5 | $0.00023 | $0.00652 |
| Sonnet 5.5 | $0.00012 | $0.00326 |
| Haiku 4.5 | $0.00006 | $0.00163 |
Grade A, and why
xberg-extracting-keywords 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extracting keywords, language, and embeddings
Use this for the enrichment surface around extraction: statistical keyword
extraction, language detection, and vector embeddings. Keywords and
language detection ride along with extraction and land on the result;
embeddings are produced by a dedicated embed command.
Keywords (YAKE / RAKE)
Keyword extraction is configured via the [keywords] config block (or
inline JSON) — there is no single --keywords CLI flag. When enabled,
extracted keywords appear on result.extracted_keywords (extractedKeywords
in Node.js; the CLI JSON field is extracted_keywords). Two algorithms are
available:
- YAKE (
"yake") — statistical, unsupervised single-document extraction. Good general default. - RAKE (
"rake") — co-occurrence / phrase-based. Favors multi-word key phrases.
Feature-gated: keyword extraction requires the CLI to be built with the
keywords-yakeand/orkeywords-rakeCargo features (both are in the default/fullbuild). If the CLI was built without them, the[keywords]config block is silently ignored —result.extracted_keywordssimply stays empty rather than erroring. The"yake"algorithm needskeywords-yake;"rake"needskeywords-rake.
Enable via inline JSON on the CLI:
xberg extract paper.pdf --format json \
--config-json '{"keywords":{"algorithm":"yake","max_keywords":15,"language":"en"}}' \
| jq '.result.extracted_keywords'
Or in a config file:
[keywords]
algorithm = "rake" # "yake" or "rake"
max_keywords = 10 # default 10
min_score = 0.0 # filter below this score (normalized 0.0-1.0 for both algorithms)
ngram_range = [1, 3] # unigrams..trigrams (default); config-file only
language = "en" # stopword language; omit to skip stopword filtering
xberg extract report.pdf --config xberg.toml --format json | jq '.result.extracted_keywords'
Field notes:
max_keywordscaps how many keywords are returned (default 10).min_scorefilters low-scoring keywords. Both YAKE and RAKE normalize their scores to the0.0-1.0range with higher-is-better, somin_scoreretains keywords withscore >= min_scoreidentically for either algorithm.ngram_rangeis[min, max]:[1,1]unigrams only,[1,2]adds bigrams,[1,3](default) adds trigrams. Config-file only — it is not a field on the language bindings'KeywordConfig.languageenables stopword filtering for that language; omit it to disable stopword filtering entirely.
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 · 170 lines · 58 tokens per session scan A cf28e8b4c842
xberg-extracting-keywords is a skill published in the GitHub repository xberg-io/xberg (9,418 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 1,630 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
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…
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…
advisor-orchestrator-worker
Use when a task is too large for one model pass, needs parallel research or generation across many subtasks (like researching a dozen competitors at once), or the user asks to orchestrate multiple models, split work across a model team, run an advisor-worker loop, have a stronger model review the plan while cheap…
rag-knowledge
Work with the RAG knowledge base — ingest documents, run semantic search, manage collections, or add a sync source/connector (Google Drive, S3). Use when populating or debugging the knowledge base, tuning retrieval, or adding a new document source. This project uses {{ cookiecutter.vectorstore }} + {{…
rag
Build a RAG (retrieval-augmented generation) pipeline using railtracks. Use when the user wants to ingest documents into a vector store and retrieve relevant passages to answer questions.
memori
You have access to Memori, agent-native memory infrastructure: an LLM-agnostic layer that structures memory from not just natural language, but also from agent trace that comes from execution.