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 LazyAGI/LazyMind --skill single-document-reviewgit clone --depth 1 https://github.com/LazyAGI/LazyMindWrote 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/lazyagi/lazymind/single-document-review)<a href="https://agentmods.dev/skills/lazyagi/lazymind/single-document-review"><img src="https://agentmods.dev/badge/skills/lazyagi/lazymind/single-document-review/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/lazyagi/lazymind/single-document-review"><img src="https://agentmods.dev/badge/skills/lazyagi/lazymind/single-document-review.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 | $0.00082 | $0.01369 |
| Opus 5 | $0.00041 | $0.00685 |
| Sonnet 5 | $0.00016 | $0.00274 |
| Haiku 4.5 | $0.00008 | $0.00137 |
Grade A, and why
single-document-review 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Single Document & Paper Review Skill
Overview
This skill produces structured, professional-grade analyses of single academic papers or general business/technical documents. It adapts established academic peer-review standards to evaluate both scientific publications and corporate reports (e.g., whitepapers, strategic memos, design docs).
The review covers executive summary, strengths, weaknesses, methodology/logic assessment, contextual positioning, and actionable recommendations — all grounded in evidence from the text itself.
When to Use This Skill
Always load this skill when:
- User provides a single URL (arXiv, blog, documentation) or file path and asks to "review", "analyze", or "summarize" it.
- User queries a specific document from the Knowledge Base (
kb_search) for detailed critique. - User wants to understand the strengths, weaknesses, and validity of a specific study, proposal, or report.
- User requests a peer-review-style evaluation of their own drafted document.
Note: If the user asks to synthesize or compare MULTIPLE documents, use the multi-document systematic review skill instead.
Available Tools & Acquisition
Depending on how the user provides the document, use the appropriate native tool to ingest the text into your context:
- Academic Papers: Use
url_fetchon the HTML version (e.g.,https://ar5iv.labs.arxiv.org/html/<id>) or usearxiv_searchfor metadata. - Web Articles: Use
url_fetch. - Internal Knowledge: Use
kb_searchorkb_keyword_searchto pull the specific document from the Knowledge Base.
Review Methodology (Internal Processing)
Once the document is loaded into your context, perform a deep reading pass using your internal attention.
Phase 1: Comprehension & Metadata Extraction
Identify:
- Title & Creators: Authors, Departments, or Organizations.
- Document Type: Is this an Empirical Paper, Theoretical Proof, Business Proposal, Technical Design Doc, or Quarterly Report?
- Core Claims: What are the 2-3 main arguments or contributions?
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 · 117 lines · 82 tokens per session scan A d059d8656526
single-document-review is a skill published in the GitHub repository LazyAGI/LazyMind (77 stars, last pushed today), licensed Apache-2.0. It adds 82 tokens to every session and 1,369 once invoked, about $0.0004 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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