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cue-fund-advisory

用 Cue 跑「基金投顾」场景的深度研究:多源公开数据交叉、结论带来源。 Run Cue deep research for the "Fund Advisory" scenario.

Skill by sensedeal

IndexedMIT★ 6
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/sensedeal-cue-fund-advisory

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用 Cue 跑「基金投顾」场景的深度研究:多源公开数据交叉、结论带来源。 Run Cue deep research for the "Fund Advisory" scenario. 触发 Triggers: 基金投顾、基金与机构、财务与业绩 / fund advisory, fund attribution, fund comparison, portfolio allocation

Use cases: research

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What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/cue-fund-advisory

What's inside

Skill name
cue-fund-advisory
Description
用 Cue 跑「基金投顾」场景的深度研究:多源公开数据交叉、结论带来源。 Run Cue deep research for the "Fund Advisory" scenario. 触发 Triggers: 基金投顾、基金与机构、财务与业绩 / fund advisory, fund attribution, fund comparison, portfolio allocation
License
MIT
Metadata
scene: 基金投顾 · source: cuecue.cn/playbook · generated_from: /api/playbook
Instructions
42 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

# Cue「基金投顾」研究 skill

加载本 skill 后,你可以用 Cue 跑这个场景的深度研究(多源公开数据交叉、结论带来源链接)。

## 何时用
基金投顾:基金与机构、财务与业绩、行情与交易。

## 当前可用搭子(仅供理解;运行时以 live 为准)
  - 大类资产配置月报:宏观配置外脑。整合全球CPI、利率、国内宏观及资金流向,从宏观逻辑推导至股/债/商配置比例,一键生成月度资产配置建议书。
  - 公募基金全维尽调:公募基金深度诊断与避坑雷达。一键穿透基金底仓,深度透视基金经理真实管理能力与风格漂移情况。直击A/C份额成本测算、历史回
  - 固收+与纯债基金排雷与优选:底仓资产的安全卫士。穿透债基底层资产,自动计算久期与杠杆率,扫描隐性信用下沉风险(如地产债/弱资质城投债暴露)。按最大回
  - 基金组合穿透与重复配置排雷:基于同一报告期的基金官方定期报告,穿透核验多只基金的已披露持仓重合与组合集中度。所有计算明确权重假设和前十大披露边界;用
  - 基金业绩归因与能力评估:拆解公募基金业绩来源、量化评估经理选股与择时能力、核查净值异常偏离,产出基金遴选与投后跟踪可用的归因评估底稿。
  - 主题/行业基金反查:按主题概念或行业反向定位持有相关标的的公募基金,量化基金对热门赛道的配置热度、重仓占比与持仓集中度,产出赛道资金流向与候

## 准备 Cue runner(首次用时,幂等)
本 skill 不自带脚本,靠 Cue 开源 runner 跑研究。先确认 runner 是否就绪:
- 若你已安装 `cue-skills`(或本 skill 来自整包发布)→ 直接用其中的 `cue-research/scripts/research_run.py`,**跳过本节**。
- 否则克隆开源仓(拿到自包含的 cue-research runner;整仓克隆最省事),**有则更新、无则克隆**(GitHub 不通走镜像):
  ```bash
  if [ -d ~/.cue/cue-skills/.git ]; then
    git -C ~/.cue/cue-skills pull --ff-only
  else
    git clone https://github.com/sensedeal/cue-skills ~/.cue/cue-skills \
      || git clone https://gitee.com/sensedeal/cue-skills ~/.cue/cue-skills
  fi
  ```
  之后 runner = `~/.cue/cue-skills/cue-research/scripts/research_run.py`。需 `git` + `python3`(runner 仅用标准库)。

## 怎么跑(搭子是动态的,运行时查 live)
1. **拉本场景当前搭子**:`GET https://cuecue.cn/api/playbook`,找 `secondary_category == "基金投顾"` 的 scene,读 `buddies[]`(每个有 `template_id`/`title`/`goal`)。若该场景当前不在返回里(临时未达展示门槛)→ 告知用户暂不可用。
2. **选一个搭子**:**委托 cue-research 的匹配逻辑**(其 `+match`/Stage-2:对 `goal` 做语义匹配、把用户的具体主体从匹配中剥离、弱命中先列 ≤2 候选确认)——不要只按字面 title 关键词裸选。取选中搭子的 `template_id`。
3. **确认 credits(强制)**:跑深度研究消耗 credits。运行前显式问用户「将用搭子 X 跑【主体】,耗 credits,是否继续?」并等确认。
4. **跑**:`python3 ~/.cue/cue-skills/cue-research/scripts/research_run.py --query "<用户主体/问题>" --template-id <template_id>`(用上一节就绪的 runner 路径;已装 cue-skills 则用你本地的 `cue-research/scripts/research_run.py`)。深度研究 3–15 分钟;长跑 live 流常不带报告段,用 replay 取最终报告。 读 runner 末行 `RESULT ok|empty`:`empty` → 告知用户本次未取到内容、可换主体/搭子重试,**不要编造**。
5. **回报**:把带来源链接的报告交给用户,不去掉来源、不杜撰。

## 前置
- Cue 账号 API key(cue CLI 登录后在 `~/.cue/config.json`,runner 自动读);新账号赠送积分(注册 500 + 每天 10),可先用赠送额度试。
- `git` + `python3`(自举 runner 用;runner 仅标准库)。
- 跑深度研究**消耗 credits**;只覆盖公开数据,不替代尽调/法律/核保。

From sensedeal/cue-skills, MIT.

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Last commit (this folder)
Sep 11, 2026 (1 month ago)
Last release
Oct 9, 2026 (19 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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cue-fund-advisory skill. Agent Mart. https://agentmart.dev/skills/sensedeal-cue-fund-advisory (last updated 2026-10-24)