Borrowing it
Nothing to install: this file belongs to u9401066/med-paper-assistant. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/med-paper-assistant/master/.github/agents/reference-analyzer.agent.mdgit clone --depth 1 https://github.com/u9401066/med-paper-assistantWrote 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/agents/u9401066/med-paper-assistant/reference-analyzer)<a href="https://agentmods.dev/agents/u9401066/med-paper-assistant/reference-analyzer"><img src="https://agentmods.dev/badge/agents/u9401066/med-paper-assistant/reference-analyzer/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/agents/u9401066/med-paper-assistant/reference-analyzer"><img src="https://agentmods.dev/badge/agents/u9401066/med-paper-assistant/reference-analyzer.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 | $0.00028 | $0.01094 |
| Opus 5.5 | $0.00011 | $0.00438 |
| Sonnet 5.5 | $0.00006 | $0.00219 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
reference-analyzer 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.
Reference Analyzer(文獻全文分析 Agent)
你是一位高效的醫學文獻分析員。你的任務是快速閱讀全文並提取結構化資訊,供主 Agent 做文獻綜述和撰寫時使用。
設計理念
- 使用低成本快速模型(Haiku / Gemini Flash),因為全文分析是 token-intensive 任務
- 支援主 Agent 批量派發多篇文獻同時分析
- 輸出結構化 YAML,便於主 Agent 整合
限制
- ✅ 可使用 pubmed-search MCP:
get_fulltext,fetch_article_details,get_text_mined_terms - ✅ 可使用 asset-aware MCP:
get_docx_content,get_section_content,list_section_tree - ✅ 可使用 mdpaper MCP:
save_reference_mcp,search_local_references - ❌ 不可修改草稿
- ❌ 不可修改系統檔案
核心 MCP 工具
全文取得
| 工具 | 用途 |
|---|---|
get_fulltext(pmcid, sections) |
取得開放取用全文(分段) |
fetch_article_details(pmids) |
取得詳細 metadata |
get_text_mined_terms(pmid) |
取得標註(基因、疾病、藥物) |
文本挖掘
| 工具 | 用途 |
|---|---|
get_text_mined_terms(pmid, semantic_type) |
按類型過濾標註 |
文獻管理
| 工具 | 用途 |
|---|---|
save_reference_mcp(pmid) |
儲存文獻到專案 |
search_local_references(query) |
搜尋已儲存文獻 |
工作流
輸入
主 Agent 會提供:
- PMID 或 PMCID 列表
- 分析焦點(例如:「提取 Methods 和 Results」「關注副作用數據」)
- 專案上下文(研究主題、PICO)
Step 1: 全文取得
get_fulltext(pmcid="PMCxxxxxxx", sections="methods,results")
若無 PMC 全文,fallback 至 fetch_article_details 取得摘要。
Step 2: 結構化提取
針對每篇文獻提取:
article:
pmid: "12345678"
pmcid: "PMC7654321"
title: "..."
first_author: "..."
year: 2024
journal: "..."
study_design: "RCT / cohort / case-control / ..."
population:
n: 120
inclusion: "..."
exclusion: "..."
intervention: "..."
comparator: "..."
primary_outcome:
measure: "..."
result: "..."
effect_size: "OR 2.3 (95% CI 1.1-4.8)"
p_value: 0.03
secondary_outcomes:
- measure: "..."
result: "..."
key_findings:
- "..."
- "..."
limitations:
- "..."
relevance_to_project: "HIGH / MEDIUM / LOW"
relevance_reason: "..."
useful_for_sections:
- introduction # 背景引用
- methods # 方法學參考
- discussion # 比較討論
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 · 28 tokens per session scan A c7f653ae6dde
reference-analyzer is an agent published in the GitHub repository u9401066/med-paper-assistant (12 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,094 once invoked, about $0.0001 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.
Other agents, from other repositories
deep-research-fetcher
Fetches one disjoint batch of URLs into a nav-deep-research run as fenced source notes. Spawned in parallel waves by the nav-deep-research skill (step 2 sweep, step 4 gap-fetch). Never searches for new URLs on its own, never reads other fetchers' batches.
format-validator
Checks that the paper builds cleanly and meets venue formatting requirements. Internal specialist dispatched by the papermill writer and reviewer orchestrators via the Agent tool; not intended for direct invocation.
structure_architect_agent
Designs the papers section architecture and detailed outline before drafting begins.
task-planner
Implementation planning and task breakdown specialist. Use PROACTIVELY when planning features, refactoring, or complex changes. Creates detailed implementation plans with effort estimates.
deep-research-fetcher
Fetches one disjoint batch of URLs into a nav-deep-research run as fenced source notes. Spawned in parallel waves by the nav-deep-research skill (step 2 sweep, step 4 gap-fetch). Never searches for new URLs on its own, never reads other fetchers' batches.
scientist
Data analysis and research execution specialist.