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 xyva-yuangui/XyvaClaw --skill auto-researchergit clone --depth 1 https://github.com/xyva-yuangui/XyvaClawWrote 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/xyva-yuangui/xyvaclaw/auto-researcher)<a href="https://agentmods.dev/skills/xyva-yuangui/xyvaclaw/auto-researcher"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/auto-researcher/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/xyva-yuangui/xyvaclaw/auto-researcher"><img src="https://agentmods.dev/badge/skills/xyva-yuangui/xyvaclaw/auto-researcher.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 | $0.00004 | $0.02131 |
| Opus 5 | $0.00002 | $0.01066 |
| Sonnet 5 | $0.00001 | $0.00426 |
| Haiku 4.5 | $0.00000 | $0.00213 |
Grade A, and why
auto-researcher 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 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.
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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔬 🔬 Auto Researcher 🔬
重要: 触发后必须先询问用户确认，再执行操作。
重要: 触发后必须先询问用户确认，再执行操作。
全自动深度研究引擎：给定主题，自动完成从信息搜集到报告输出的全流程。
核心流程
主题输入 → 问题分解 → 多轮搜索 → 信息提取 → 交叉验证 → 知识入库 → 报告生成
↑ |
└──────────────── 发现子问题，自动追问 ←────────────────────────┘
🔬 🔬 # 快速开始
# 基础研究
python3 scripts/researcher.py --topic "2026年中国新能源汽车市场竞争格局"
# 深度研究（更多轮次、更多来源）
python3 scripts/researcher.py --topic "AI Agent 架构演进" --depth deep
# 指定输出格式
python3 scripts/researcher.py --topic "量化选股策略对比" --format feishu-doc
# 中英文混合研究
python3 scripts/researcher.py --topic "全球半导体供应链风险" --langs "zh,en"
# 恢复中断的研究
python3 scripts/researcher.py --resume session-20260305-abc123
参数
| 参数 | 说明 | 默认值 |
|---|---|---|
--topic |
研究主题 | 必填 |
--depth |
研究深度: quick/standard/deep | standard |
--format |
输出格式: markdown/html/feishu-doc/pdf | markdown |
--langs |
搜索语言 | zh,en |
--max-rounds |
最大搜索轮次 | 5 (deep=10) |
--max-sources |
最大信息源数量 | 20 (deep=50) |
--output |
输出目录 | ./output/research/ |
--resume |
恢复之前的研究会话 | - |
--check |
健康检查 | - |
🔬 🔬 # 研究深度对比
| 维度 | quick (5min) | standard (15min) | deep (30min+) |
|---|---|---|---|
| 搜索轮次 | 1-2 | 3-5 | 5-10 |
| 信息源 | 5-10 | 10-20 | 20-50 |
| 交叉验证 | 基础 | 标准 | 严格+多语言 |
| 子问题追问 | 无 | 1 层 | 2-3 层递归 |
| 报告字数 | 500-1000 | 2000-5000 | 5000-15000 |
| 图表 | 无 | 1-2 张 | 3-5 张 |
🔬 🔬 # 研究流程详解
Step 1: 问题分解
原始主题: "2026年中国新能源汽车市场竞争格局"
↓
子问题:
├── Q1: 2026年中国新能源汽车销量和市场份额数据？
├── Q2: 主要竞争者（比亚迪/特斯拉/蔚来...）最新动态？
├── Q3: 政策环境变化（补贴/碳积分/出口）？
├── Q4: 技术趋势（固态电池/智驾/充电）？
└── Q5: 海外市场拓展情况？
使用 deep-reasoning-chain 进行问题拆解，确保覆盖全面。
Step 2: 多轮搜索
每个子问题独立搜索，使用 multi-search-engine 多引擎并行：
Q1 → Google + Bing + 百度 → 结果集 R1
Q2 → Google + Reddit + 微信搜索 → 结果集 R2
Q3 → Google + 政府网站 → 结果集 R3
...
搜索策略:
- 每个子问题生成 3-5 个不同角度的搜索词
- 中英文同时搜索（
--langs控制） - 自动过滤低质量来源（广告、SEO 垃圾）
- 优先权威来源（政府、学术、行业报告）
What ships with it
4 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 · 249 lines · 4 tokens per session scan A 1021baac1222
auto-researcher is a skill published in the GitHub repository xyva-yuangui/XyvaClaw (21 stars, last pushed 1mo ago), licensed MIT. It adds 4 tokens to every session and 2,131 once invoked, about $0.0000 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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