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 samqin123/Claude_skill_pool --skill research-analyst-systemgit clone --depth 1 https://github.com/samqin123/Claude_skill_poolWrote 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/samqin123/claude_skill_pool/research-analyst-system)<a href="https://agentmods.dev/skills/samqin123/claude_skill_pool/research-analyst-system"><img src="https://agentmods.dev/badge/skills/samqin123/claude_skill_pool/research-analyst-system/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/samqin123/claude_skill_pool/research-analyst-system"><img src="https://agentmods.dev/badge/skills/samqin123/claude_skill_pool/research-analyst-system.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 | $0.00081 | $0.00950 |
| Opus 5 | $0.00041 | $0.00475 |
| Sonnet 5 | $0.00016 | $0.00190 |
| Haiku 4.5 | $0.00008 | $0.00095 |
Grade A, and why
research-analyst-system 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 9d 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.
What it actually says
Research Analyst System - 多层级投资研究团队
Overview
三层级 Agent 协作系统：首席分析师 → 6 大小组长 → N 名研究员。并行研究、交叉验证、逐层汇总，输出包含具体标的和驱动逻辑的综合研究报告，自动保存到 report/ 目录。
Architecture
首席分析师 (用户接口 + 任务拆解 + 最终整合)
├── 01 内资股票小组 (沪深A股、港股通)
├── 02 外资股票小组 (美股、欧股)
├── 03 大宗商品小组 (黄金、铜、锂、石油)
├── 04 加密货币小组 (BTC、ETH、USDT)
├── 05 中国宏观小组 (M2、政策、经济数据)
└── 06 美国宏观小组 (美联储、美元、利率)
每个小组长可分配 N 名研究员并行工作。
Workflow
1. 需求理解与拆解（首席分析师）
- 分析用户研究问题涉及的领域和维度。
- 识别需要调用哪些研究小组。
- 制定研究任务清单。
2. 任务分配（首席分析师 → 小组长）
- 将拆解后的任务分派给对应小组长。
- 明确研究目标和交付标准。
3. 子任务分解（小组长 → 研究员）
- 小组长将任务拆解为具体研究点。
- 为每个研究点分配专门研究员。
- 明确搜索范围和数据来源。
4. 并行研究（研究员执行）
- 独立阅读项目内资料。
- 必要时调用搜索工具（WebSearch / MCP PubMed / metaso 等）。
- 撰写独立研究报告。
5. 质量审查（小组长）
- 汇总研究员报告。
- 交叉验证数据和结论，剔除矛盾和低质量信息。
- 整合形成小组级完整报告。
6. 最终整合（首席分析师）
- 汇总所有小组报告。
- 识别跨小组关联和逻辑。
- 构建统一叙事框架，生成综合报告。
Report Output
自动保存到 report/ 目录：
report/
└── YYYYMMDD_HHMMSS_研究主题/
├── 00_首席分析师报告.md
├── 01_内资股票小组/
│ ├── 小组长报告.md
│ └── 研究员01_任务名称.md
├── 02_外资股票小组/ ...
├── 03_大宗商品小组/ ...
├── 04_加密货币小组/ ...
├── 05_中国宏观小组/ ...
└── 06_美国宏观小组/ ...
Quality Control
- 研究员：必须引用具体数据来源，区分事实与观点，标注不确定性。
- 小组长：交叉验证多名研究员结论，剔除矛盾信息，确保逻辑一致。
- 首席分析师：检查各小组一致性，识别跨领域机会，平衡不同观点。
Guardrails
- 任何投资建议都必须包含风险提示。
- 关键结论应由多名研究员独立验证。
- 对快速变化的市场数据标注时效性。
- 严格遵循报告目录结构，确保可追溯性。
- 每个报告包含生成时间戳，同一主题更新时创建新目录。
What ships with it
1 file 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.
- 9d ago First seen · 86 lines · 81 tokens per session scan A 3cbdc1190519
research-analyst-system is a skill published in the GitHub repository samqin123/Claude_skill_pool (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 81 tokens to every session and 950 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.