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 ai4s-research/ai4s-skills --skill literature-surveygit clone --depth 1 https://github.com/ai4s-research/ai4s-skillsWrote 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/ai4s-research/ai4s-skills/literature-survey)<a href="https://agentmods.dev/skills/ai4s-research/ai4s-skills/literature-survey"><img src="https://agentmods.dev/badge/skills/ai4s-research/ai4s-skills/literature-survey/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/ai4s-research/ai4s-skills/literature-survey"><img src="https://agentmods.dev/badge/skills/ai4s-research/ai4s-skills/literature-survey.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 | $0.00066 | $0.01979 |
| Opus 5.5 | $0.00026 | $0.00792 |
| Sonnet 5.5 | $0.00013 | $0.00396 |
| Haiku 4.5 | $0.00007 | $0.00198 |
Grade A, and why
literature-survey 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 1mo 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Survey
Overview
End-to-end literature survey builder. Single stage, full quality from the start. The agent (Claude Code / Cursor / Aider / Codex / …) does the entire build using its own tools (WebFetch, WebSearch, Write, Bash). This SKILL is procedure + reference playbooks + LaTeX template — no Python runtime, no LLM SDK.
The substantive work is decomposed into reference playbooks under references/:
| Reference | Topic |
|---|---|
references/00-incremental-execution.md |
how to actually do this without losing work: batch sizes, persistence, resume — read first |
references/01-bibliography-expansion.md |
grow bibliography.bib to 60+ real entries (100+ recommended) via WebFetch (no memory) |
references/02-survey-figures.md |
taxonomy / timeline / coverage-matrix / area-map figures |
references/03-survey-section-playbook.md |
per-section structure for survey-shaped papers |
references/04-layout-discipline.md |
tables, figures, floats, cross-refs, author + disclosure footnote |
references/05-quality-gate.md |
self-check before delivery |
Read the relevant reference before writing, not after. The full pass does not fit in a single turn — references/00-incremental-execution.md is the only execution mode that completes.
When to Use
- User asks for a "survey" / "review" on a specific topic.
- User has a research topic and wants a structured map of the field with citations.
- User needs background reading curated for a thesis chapter or grant section.
When NOT to Use
- User wants original research with experiments →
paper-writer. - User wants only an outline / topic exploration →
research-explorer. - User wants experiment code →
experiment-suite. - Topic is too broad (e.g., "all of AI") — narrow it before starting.
Workflow
Step 1 — Understand the topic and scope
Confirm with the user:
- Topic — specific research area (e.g., "federated learning in healthcare"). If too broad, narrow it first.
- Scope — broad survey of a field vs. focused review of a sub-area.
- Citation budget — minimum 60 unique entries; aim for 100+ (push higher for a broad survey).
- Language — default Chinese in conversation; the LaTeX paper is English unless requested otherwise.
What ships with it
11 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.
- references/00-incremental-execution.md 7.7 KB
- references/01-bibliography-expansion.md 13 KB
- references/02-survey-figures.md 27 KB
- references/03-survey-section-playbook.md 9.1 KB
- references/04-layout-discipline.md 10 KB
- references/05-quality-gate.md 15 KB
- templates/survey/check_bibliography_freshness.py 3.8 KB runs code
- templates/survey/compile.sh 586 B runs code
- templates/survey/figures/.gitkeep 0 B
- templates/survey/main.tex 2.1 KB
- templates/survey/sections/.gitkeep 0 B
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.
- 1mo ago First seen · 160 lines · 66 tokens per session scan A fc0699464f14
literature-survey is a skill published in the GitHub repository ai4s-research/ai4s-skills (256 stars, last pushed 3mo ago), licensed MIT. It adds 66 tokens to every session and 1,979 once invoked, about $0.0003 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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pydicom
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liteparse
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities…
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Organizes research with the self-hosted Open Notebook alternative to NotebookLM. Supports source ingestion (PDFs, web pages, audio, video, and Office documents), cited document chat, text and vector search, notes, custom transformations, and multi-speaker podcasts. Use when automating Open Notebook through its REST…
pptx-posters
Creates and audits editable scientific posters in macro-free PowerPoint (.pptx) from author-approved local content and assets. Used when the requested deliverable is a PowerPoint research/conference poster and exact physical, printer, accessibility, provenance, and package-security checks are required.