StarNet is a local-first desktop harness for creating AI agents and arranging them in a pixel-art space station whose layout represents their capabilities, permissions, and workflow. It lets people run real model calls and tools through multiple bounded agents, using their own provider keys or accounts and optionally messaging the station through chat services. The catalogue entries are skills that extend or support work performed by StarNet agents.
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 androoAGI/starnet --skill document-to-action-itemsgit clone --depth 1 https://github.com/androoAGI/starnetWrote 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/androoagi/starnet/document-to-action-items)<a href="https://agentmods.dev/skills/androoagi/starnet/document-to-action-items"><img src="https://agentmods.dev/badge/skills/androoagi/starnet/document-to-action-items/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/androoagi/starnet/document-to-action-items"><img src="https://agentmods.dev/badge/skills/androoagi/starnet/document-to-action-items.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 | $0.00046 | $0.01013 |
| Opus 5.5 | $0.00018 | $0.00405 |
| Sonnet 5.5 | $0.00009 | $0.00203 |
| Haiku 4.5 | $0.00005 | $0.00101 |
Grade A, and why
document-to-action-items 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 12d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document to Action Items
Turn documents into cited facts and proposed actions. Extraction is not legal advice, and low-confidence OCR or ambiguous language must stay visible. Getting the text out is a separate job (see step 2); this skill owns what happens to the extracted content.
When to use
- "Extract the deadlines and obligations from this contract."
- "Turn this report into tasks."
- "Read these scanned forms and structure the data."
- "Find the risks, owners, and follow-ups in these attachments."
Not for plain text extraction with no structuring afterwards; use a PDF extraction recipe directly for that.
Procedure
1. Inventory the document set
Use fs.read for local files and web_fetch for URLs to identify the files, versions, dates, page counts, language, scan quality, and the output schema the Commander wants. Detect duplicate or revised copies before analysis. Done when the authoritative or latest version is known, or the ambiguity is stated.
2. Extract with provenance
Extract text and tables while keeping file and page or section coordinates:
.docxand.xlsxfiles:fs.readreturns readable text directly.- Images of pages (PNG, JPEG):
fs.readshows you the pixels, or useimage_analyzewith a focused question. - PDFs at a URL:
web_fetchoften returns clean text. - Local PDFs and scans that need OCR: follow the PDF extraction recipe if it is installed (
skill.viewwithlibrary:pdf-document-extraction), which uses local extractors on the WORKBENCH.
For scans, record OCR confidence or visible quality problems. Done when every extracted field can cite its source location.
3. Classify the evidence
Separate:
- parties, entities, and identifiers
- dates and deadlines
- money and quantities
- obligations and prohibitions
- approvals and signatures
- risks and exceptions
- factual background
- ambiguous or unreadable clauses
Do not collapse "may", "should", and "must". Done when modality and uncertainty are preserved.
What ships with it
2 files 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.
- 12d ago First seen · 92 lines · 46 tokens per session scan A ec204bac033d
document-to-action-items is a skill published in the GitHub repository androoAGI/starnet (1,468 stars, last pushed 3d ago), licensed MIT. It adds 46 tokens to every session and 1,013 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 2026-10-04.
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