skills/gangtise/SKILL.md

---
name: gangtise
description: |
  Use when the user asks about Chinese equity research, broker reports, analyst opinions,
  industry chain analysis, investment logic, company deep-dives, economic indicators,
  or needs to search financial knowledge bases. Triggers: "研报", "研究报告", "投资逻辑",
  "产业链", "调研提纲", "晨报", "指标查询", "GDP", "销量", "深度研究", "gangtise".
allowed-tools: Bash WebFetch Read Write Edit Glob Grep AskUserQuestion
user-invokable: true
---

# Gangtise Ultra — 投研知识库 & AI Agent API

港股/A股研报知识库智能检索 + AI 深度研究 Agent + 经济指标查询。

## Configuration

**Base URL:** `http://39.96.218.64:8009`

**Auth:** All requests require `Authorization: Bearer <token>` header.

### Token Resolution (MUST do before first API call)

Run this bash snippet to resolve the token. It checks multiple sources automatically:

```bash
# 1. Environment variable
TOKEN="${GANGTISE_TOKEN:-}"

# 2. .env in current directory
if [ -z "$TOKEN" ] && [ -f .env ]; then
  TOKEN=$(grep -E '^GANGTISE_TOKEN=' .env | cut -d= -f2- | tr -d '"'"'"' ')
fi

# 3. ~/.env global dotenv
if [ -z "$TOKEN" ] && [ -f ~/.env ]; then
  TOKEN=$(grep -E '^GANGTISE_TOKEN=' ~/.env | cut -d= -f2- | tr -d '"'"'"' ')
fi
```

**If `$TOKEN` is still empty after the above**, use `AskUserQuestion` to ask:
> "Gangtise API 需要 Bearer Token 才能调用。请提供 token，或者告诉我 .env 文件路径。"

Once obtained, export it for the rest of the session:
```bash
export GANGTISE_TOKEN="<the token>"
```

### Verify Connection

```bash
curl -s -o /dev/null -w "%{http_code}" http://39.96.218.64:8009/api/docs \
  -H "Authorization: Bearer $TOKEN"
# 200 = OK, 401 = token invalid
```

## API Quick Reference

| Endpoint | Method | Response | Use Case |
|----------|--------|----------|----------|
| `/api/search` | POST | JSON | 搜索研报/公告/纪要 |
| `/api/search/batch` | POST | JSON | 批量搜索多个主题 |
| `/api/ai/chat` | POST | JSON/SSE | AI 深度研究（支持3种返回模式） |
| `/api/ai/chat/create-group` | GET | JSON | 创建会话（用于多轮对话） |
| `/api/ai/indicator` | POST | JSON | 经济/行业指标数据查询 |
| `/api/docs` | GET | JSON | 完整接口文档 |

## 1. Knowledge Search — `/api/search`

Search broker reports, analyst opinions, company filings, meeting minutes.

```bash
curl -s http://39.96.218.64:8009/api/search \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "比亚迪",
    "top": 10,
    "time_range": "1m",
    "resource_types": ["BROKER_REPORT"]
  }'
```

**Parameters:**
- `query` (required): Search keywords
- `top`: 1-20, default 10
- `time_range`: `1d` `1w` `1m` `1q` `6m` `1y` `all` (default `1m`)
- `resource_types`: Filter array, options:
  - `BROKER_REPORT` — 券商研报
  - `INTERNAL_REPORT` — 内部研报
  - `ANALYST_OPINION` — 首席观点
  - `COMPANY_NOTICE` — 公司公告
  - `MEETING_MINUTES` — 会议纪要
  - `SURVEY_NOTES` — 调研纪要
  - `WEB_RESOURCE` — 网络资源
  - `INDUSTRY_OFFICIAL` — 产业公众号

**Response:** `{query, total, time_range, results: [{title, source, date, company, content, source_url, source_id}]}`

Each result includes `source_url` for citation. Use `[[n]](source_url)` format in replies.

## 2. Batch Search — `/api/search/batch`

Search multiple topics at once (max 5).

```bash
curl -s http://39.96.218.64:8009/api/search/batch \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "queries": ["比亚迪", "宁德时代", "特斯拉"],
    "top": 5,
    "time_range": "1m"
  }'
```

**Response:** `{total_queries, time_range, responses: [{query, total, results}]}`

## 3. Agent Deep Research — `/api/ai/chat`

AI-powered deep analysis with multi-round thinking. Supports 3 return modes.

**Response takes 30-120 seconds.** Use `stream="progress"` mode for best LLM experience.

### Recommended: Progress Mode (for LLM/Agent)

Returns lightweight SSE progress events during thinking, then final JSON result:

```bash
curl -s -N http://39.96.218.64:8009/api/ai/chat \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "比亚迪的投资逻辑",
    "forced_agent": "investment_logic",
    "stream": "progress",
    "thinking_detail": "summary"
  }' --max-time 200
```

Progress events arrive during processing, final result at end:
```
event: progress
data: {"phase":"think","round":1,"title":"识别问题","seq":1}

event: progress
data: {"phase":"answer","status":"generating"}

event: result
data: {"query":"...","answer":"...","thinking":[...],"usage":{"thinking_rounds":4,"answer_length":5041}}
```

To extract the final JSON result from progress mode:
```bash
curl -s -N ... --max-time 200 | grep '^data:' | tail -1 | sed 's/^data: //' | python3 -m json.tool
```

### JSON Mode (simple, blocking)

Wait for complete result as pure JSON (no streaming):

```bash
curl -s http://39.96.218.64:8009/api/ai/chat \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "比亚迪的投资逻辑",
    "forced_agent": "investment_logic",
    "stream": false,
    "include_thinking": true,
    "thinking_detail": "summary"
  }' --max-time 200
```

**Response:** `{query, group_id, answer, thinking, usage: {thinking_rounds, answer_length}}`

### Raw SSE Mode (for frontend streaming)

```bash
curl -s -N http://39.96.218.64:8009/api/ai/chat \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "比亚迪的投资逻辑", "forced_agent": "investment_logic", "stream": true}'
```

### Parameters

- `query` (required): Research question
- `stream`: Return mode — `true` (raw SSE, default), `false` (JSON), `"progress"` (SSE progress + JSON result)
- `forced_agent`: Force a specific agent (default: auto-select). See **Agent Selection Guide** above for routing logic.
- `include_thinking`: Include thinking rounds in response (default true, only for non-raw modes)
- `thinking_detail`: `"full"` (default) or `"summary"` (titles only, saves tokens)
- `group_id`: Pass to maintain conversation context
- `include_search_types`: Filter sources `[10,20,40,50,60,70,80,90]`
- `web_enable`: Enable web search (default false)

## 4. Create Chat Group — `/api/ai/chat/create-group`

Create a session for multi-turn agent conversations.

```bash
curl -s http://39.96.218.64:8009/api/ai/chat/create-group \
  -H "Authorization: Bearer $TOKEN"
```

**Response:** `{"group_id": 12345}`

Pass `group_id` to subsequent `/api/ai/chat` calls for context continuity.

## 5. Indicator Query — `/api/ai/indicator`

Query macroeconomic data, industry statistics, company operating metrics.

```bash
curl -s http://39.96.218.64:8009/api/ai/indicator \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"query": "比亚迪仰望销量"}'
```

**Response:** `{reasoning_content: "...", content: "..."}`

- `reasoning_content`: Data retrieval process
- `content`: Final result (often markdown tables)

**Good queries:** Company sales, GDP, CPI, PMI, battery installations, EV penetration, commodity prices.

## Agent Selection Guide

Match user intent to the right agent. **If uncertain, omit `forced_agent` — the upstream AI will auto-select.**

| User Intent (keywords) | `forced_agent` | What it does |
|---|---|---|
| 投资逻辑、买入/卖出理由、多空分析、估值 | `investment_logic` | 梳理公司核心投资逻辑，分析多空因素 |
| 产业链、上下游、竞争格局、行业分析 | `industry_expert` | 拆解产业链结构，分析竞争格局和趋势 |
| 调研提纲、调研问题、尽调清单 | `investigation_outline` | 生成结构化调研问题清单 |
| 晨报、今日主题、板块速览、每日简报 | `theme_daily_report` | 生成主题/板块晨间简报 |
| 查找、搜索、最新研报、有什么消息 | `searcher` | 通用信息检索，适合开放性问题 |
| 销量、GDP、CPI、产量等具体数字 | *(use `/api/ai/indicator` instead)* | — |

**Routing decision tree:**
1. User asks for **specific numbers/data** → `/api/ai/indicator` (not agent chat)
2. User asks for **latest reports/news** on a topic → `/api/search` or `searcher`
3. User asks **"why invest in X"** / **"X的投资逻辑"** → `investment_logic`
4. User asks about **supply chain / industry structure** → `industry_expert`
5. User asks to **prepare for a company visit** → `investigation_outline`
6. User asks for a **morning briefing / theme summary** → `theme_daily_report`
7. **Unclear** → omit `forced_agent`, let upstream auto-select

## Workflow Patterns

### Pattern A: Quick Research
User asks about a company or topic → `/api/search` with relevant keywords → Synthesize results with citations.

### Pattern B: Deep Analysis (with agent routing)
User wants deep analysis → Select `forced_agent` per Agent Selection Guide above → `/api/ai/chat` with `stream="progress"` → Present the final answer from `event: result`.

Example: "分析宁德时代的产业链" → `forced_agent: "industry_expert"`
Example: "比亚迪值得买吗" → `forced_agent: "investment_logic"`
Example: "帮我准备一份调研小鹏汽车的提纲" → `forced_agent: "investigation_outline"`

### Pattern C: Data Query
User asks for specific metrics (sales, GDP, production) → `/api/ai/indicator` → Present the data table. Do NOT use agent chat for pure data queries.

### Pattern D: Comparative Research
User wants to compare multiple companies → `/api/search/batch` with company names → Cross-reference results.

### Pattern E: Multi-Turn Research
Create group → ask initial question → follow up with `group_id` for contextual conversation.

### Pattern F: Combined Research
Complex questions often need multiple API calls:
1. `/api/ai/indicator` for hard data (sales, financials)
2. `/api/ai/chat` with `investment_logic` for qualitative analysis
3. `/api/search` for latest broker reports as supporting evidence
Synthesize all three into a comprehensive answer with citations.

## Citation Format

When presenting search results, use this citation format:

**In text:** `比亚迪1月出口增45% [[1]](source_url)`

**At end:**
```
## 参考来源
[1] 券商研报《标题》(日期) [查看原文](source_url)
```

## Common Mistakes

| Mistake | Fix |
|---------|-----|
| Forgetting Bearer token | Always include `Authorization: Bearer $TOKEN` |
| Using `stream=true` (raw SSE) for LLM | Use `stream="progress"` or `stream=false` instead |
| Setting short timeout for agent_chat | Allow 200s+ (`--max-time 200`) for deep research |
| Not passing `group_id` for follow-ups | Create group first, reuse ID |
| Searching with overly broad queries | Be specific: company name + topic |
| Including full thinking in responses | Use `thinking_detail="summary"` to save tokens |