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 dair-ai/dair-academy-plugins --skill survey-generatorgit clone --depth 1 https://github.com/dair-ai/dair-academy-pluginsWrote 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/dair-ai/dair-academy-plugins/survey-generator)<a href="https://agentmods.dev/skills/dair-ai/dair-academy-plugins/survey-generator"><img src="https://agentmods.dev/badge/skills/dair-ai/dair-academy-plugins/survey-generator/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/dair-ai/dair-academy-plugins/survey-generator"><img src="https://agentmods.dev/badge/skills/dair-ai/dair-academy-plugins/survey-generator.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 | $0.00096 | $0.01975 |
| Opus 5 | $0.00048 | $0.00988 |
| Sonnet 5 | $0.00019 | $0.00395 |
| Haiku 4.5 | $0.00010 | $0.00198 |
Grade A, and why
survey-generator scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Python 3 with stdlib only (urllib). No external dependencies. How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Survey Generator Skill
Generate an academic-style survey paper as a single self-contained HTML file.
What this skill does
Given a topic and a public anchor resource, this skill:
- Reads the anchor resource and extracts the landscape of relevant work.
- Builds a structured
research_bundle.json(title, taxonomy, sections, bibliography of real papers). - Calls Kimi K2.6 via the Fireworks chat completions API with the research bundle and a fixed
style_spec.json. - Writes a single-file HTML artifact with inline SVG figures, an academic layout, numbered sections, and a References list.
The agent using this skill is responsible only for research curation. All prose, figures, and HTML are generated by Kimi K2.6 in one API call.
Inputs from the user
The user invokes this skill with at minimum:
topic: a concise survey topic, for example "Agentic Engineering" or "Reasoning Models".source_url: a public anchor resource. Any curated list, canonical blog post, arXiv survey, GitHub awesome-list, or index page works. Suggested starting points: DAIR.AI AI Papers of the Week (a continuously updated open-source index of notable AI/ML papers, well suited for broad topics), a GitHub awesome-* repo, an arXiv survey PDF, or a well-maintained papers page.
Optional:
bibliography_size: target bibliography size. Default 20 for a quick survey. Use 40 to 50 for a comprehensive survey, 80 to 100 for an exhaustive one. Section length and token budget scale with this.section_count: number of sections, default 6 to 10.
If the user has not provided these, use AskUserQuestion to collect them before proceeding.
Requirements
FIREWORKS_API_KEYexported in the environment. The build script reads it fromos.environ.- Python 3 with stdlib only (urllib). No external dependencies.
Workflow for the agent
Follow these steps in order. Do not skip steps.
Step 1. Read the anchor resource
Fetch and read source_url. If it is a GitHub repo, fetch the README and any relevant README-*.md or papers.md indices. If it is an arXiv survey, use the abstract, figures, and section headings. If it is a blog post, read it in full. Extract the key subtopics and the papers or systems it references by name.
What ships with it
6 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 · 113 lines · 96 tokens per session scan A 47e899e0dda2
survey-generator is a skill published in the GitHub repository dair-ai/dair-academy-plugins (614 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 1,975 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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