DeerFlow is an open-source super-agent harness that coordinates sub-agents, memory, tools, sandboxes, and extensible skills to handle research, coding, and content-creation tasks that may run for minutes or hours. It is intended for long-running, multi-step work performed by AI agents. The catalogue entries are skills, agents, and instructions that support its workflows.
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 bytedance/deer-flow --skill academic-paper-reviewgit clone --depth 1 https://github.com/bytedance/deer-flowWrote 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/bytedance/deer-flow/academic-paper-review)<a href="https://agentmods.dev/skills/bytedance/deer-flow/academic-paper-review"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/academic-paper-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/bytedance/deer-flow/academic-paper-review"><img src="https://agentmods.dev/badge/skills/bytedance/deer-flow/academic-paper-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00095 | $0.02548 |
| Opus 5 | $0.00048 | $0.01274 |
| Sonnet 5 | $0.00019 | $0.00510 |
| Haiku 4.5 | $0.00010 | $0.00255 |
Grade A, and why
academic-paper-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 11d 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.
Copies of this mod
5 near-identical copies found in the catalogue:
- academic-paper-review — 100% identical, 0 lines differ
- academic-paper-review — 100% identical, 0 lines differ
- academic-paper-review — 100% identical, 0 lines differ
- academic-paper-review — 100% identical, 0 lines differ
- academic-paper-review — 100% identical, 578 lines differ
How it starts
The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Paper Review Skill
Overview
This skill produces structured, peer-review-quality analyses of academic papers and research publications. It follows established academic review standards used by top-tier venues (NeurIPS, ICML, ACL, Nature, IEEE) to provide rigorous, constructive, and balanced assessments.
The review covers summary, strengths, weaknesses, methodology assessment, contribution evaluation, literature positioning, and actionable recommendations — all grounded in evidence from the paper itself.
Core Capabilities
- Parse and comprehend academic papers from uploaded PDFs or fetched URLs
- Generate structured reviews following top-venue review templates
- Assess methodology rigor (experimental design, statistical validity, reproducibility)
- Evaluate novelty and significance of contributions
- Position the work within the broader research landscape via targeted literature search
- Identify limitations, gaps, and potential improvements
- Produce both detailed review and concise executive summary formats
- Support papers in any scientific domain (CS, biology, physics, social sciences, etc.)
When to Use This Skill
Always load this skill when:
- User provides a paper URL (arXiv, DOI, conference proceedings, journal link)
- User uploads a PDF of a research paper or preprint
- User asks to "review", "analyze", "critique", "assess", or "summarize" a research paper
- User wants to understand the strengths and weaknesses of a study
- User requests a peer-review-style evaluation of academic work
- User asks for help preparing a review for a conference or journal submission
Review Methodology
Phase 1: Paper Comprehension
Thoroughly read and understand the paper before forming any judgments.
Step 1.1: Identify Paper Metadata
Extract and record:
| Field | Description |
|---|---|
| Title | Full paper title |
| Authors | Author list and affiliations |
| Venue / Status | Publication venue, preprint server, or submission status |
| Year | Publication or submission year |
| Domain | Research field and subfield |
| Paper Type | Empirical, theoretical, survey, position paper, systems paper, etc. |
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.
- 11d ago First seen · 290 lines · 95 tokens per session scan A a0285baaeacc
academic-paper-review is a skill published in the GitHub repository bytedance/deer-flow (82,196 stars, last pushed today), licensed MIT. It adds 95 tokens to every session and 2,548 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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