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 BlackBeltTechnology/pi-agent-dashboard --skill doc-summarizergit clone --depth 1 https://github.com/BlackBeltTechnology/pi-agent-dashboardWrote 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/blackbelttechnology/pi-agent-dashboard/doc-summarizer)<a href="https://agentmods.dev/skills/blackbelttechnology/pi-agent-dashboard/doc-summarizer"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/doc-summarizer/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/blackbelttechnology/pi-agent-dashboard/doc-summarizer"><img src="https://agentmods.dev/badge/skills/blackbelttechnology/pi-agent-dashboard/doc-summarizer.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 | $0.00105 | $0.01141 |
| Opus 5 | $0.00053 | $0.00571 |
| Sonnet 5 | $0.00021 | $0.00228 |
| Haiku 4.5 | $0.00011 | $0.00114 |
Grade A, and why
doc-summarizer 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Summarizer
Summarize documents of any size. Extraction goes through the document-converter
engine facade (dc.convertToMarkdown) — the same Docker-quarantined engine the
document-converter skill uses. There are NO host-side extractor scripts here;
the facade is the only extraction surface. Chunking and synthesis are agent work.
Prerequisites
- The
document-converterpackage built and runnable: Docker available, image built (cd packages/document-converter && npm run build:image). See thedocument-converterSKILL for the full facade contract. - Nothing else. No
pdftotext/pandoc/Python on the host — the engine owns all format handling inside Docker.
Step 1 — Extract to Markdown via the engine
Call the facade; never invoke Python, docling, or pdftotext directly.
import { createDocumentConverter } from "@blackbelt-technology/pi-dashboard-document-converter";
const dc = createDocumentConverter({ image: "pi-doc-engine:0.1.0", stagingDir: "/abs/staging" });
const { output } = await dc.convertToMarkdown("<file_path>"); // digital PDF/DOCX/…
// scanned PDF: pass OCR explicitly
await dc.convertToMarkdown("<file_path>", { ocr: { mode: "force", lang: ["english"] } });
The result is a provenance-stamped .md in stagingDir. Read that file to get
the document text. On failure the call rejects with DocConverterError
(.code, .stderr) — surface UNSUPPORTED_FORMAT, OCR_LANG_UNSUPPORTED,
INGEST_FAILED, DOCKER_UNAVAILABLE rather than retrying blindly.
Step 2 — Decide direct vs. chunked
Measure the extracted Markdown:
- < ~8,000 words (~10k tokens): summarize directly in the current context (Step 3a).
- >= ~8,000 words: chunk and fan out (Step 3b).
Step 3a — Direct summarization (small documents)
Read the extracted .md and produce a summary using the output format
below: title/subject, key points, entities, document type, language.
Step 3b — Chunked summarization (large documents)
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 · 116 lines · 105 tokens per session scan A 798c7b3563f7
doc-summarizer is a skill published in the GitHub repository BlackBeltTechnology/pi-agent-dashboard (278 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 1,141 once invoked, about $0.0005 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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