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Hlzd market report

Skill Alexxiang2008/hlzd-b2b-export-skills/skills/hlzd-market-report

B2B 工业品海外市场情报报告生成 —— 9 节结构 HTML 自包含报告(cover / TOC / signals / platforms / comparison / actions / methodology / sources)+ Markdown 简化版。从 hlzd-b2b-research 输出或原始 web 信号自动组装。Use when 用户说'出报告'、'海外调研报告'、'市场情报'、'跨境选品报告'、'做份 deliverable'、'跨境市场扫描 HTML'、'generate B2B market report'、'market intelligence brief'。From its SKILL.md

Install
npx -y skills add Alexxiang2008/hlzd-b2b-export-skills --skill hlzd-market-report

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

6.9 KB, ~2.0k tokens by cl100k_base, as published. Nobody here has run it

HLZD 市场情报报告

把"调研出来的信号"组装成 9 节自包含 HTML 报告,便于发给客户、董事会议或朋友圈。


When to use

调用本 Skill 当用户:

  • 跑完 hlzd-b2b-research 拿到 JSON,想打包成可视报告
  • 跑完 hlzd-buyer-finder 拿到买家名单,想附市场分析
  • 要给客户/老板看"我做了什么"的市场扫描
  • 想在 5 分钟内交付一份视觉 + 数据 都达标的报告

不要调用本 Skill 当

  • 用户只想要一份纯文本摘要 —— 用 hlzd-b2b-research 的 Markdown 输出即可
  • 用户要做实时仪表盘 —— 这是静态报告(v0.1)

How this skill is invoked

# 全自动:topic + 关键词 → 一键 HTML + Markdown
py scripts/pipeline.py --topic "OCTG casing export to UAE" \
                        --keywords "OCTG casing" "API 5CT" "Aramco tender" \
                        --output-html report.html --output-md report.md

# 复用 hlzd-b2b-research 的 JSON
py scripts/pipeline.py --topic "auto" \
                        --from-research b2b-research-output.json \
                        --output-html report.html

9-节报告结构

┌── Cover ────────────────────────┐
│  ★ Headline (L1: 56-72px)         │
│  Tagline (L2: 16-18px)            │
│  4 KPI cards                      │
│  3-step action plan (L1 action)    │
│  Meta line (L3: 13px)              │
└────────────────────────────────┘
       ↓
TOC  (9 anchored links)
       ↓
1. Solution Overview
   - Recap (16-18px)
   - 5-row conclusion table
       ↓
2. 3 Core Signals
   - User pain quote (blockquote)
   - Root cause
   - Market implication
   - Source chips
       ↓
3. Platform Deep Dive (×4-5 platforms)
   - Stat chip
   - 5 findings each (with inline source link)
       ↓
4. Strategic Comparison Table
   - 10 dimensions × 3 vendors
       ↓
5. 3-Step Action Plan
   - Green card with options / risk / execution
       ↓
6. Methodology & Limitations
   - 5-step process / 5-dim signal judgement / 7-limitations
   - Yellow callout box
       ↓
7. Sources (grouped by category)
   - Sources grid
       ↓
Footer (HLZD brand + license)

Output schema (input 数据契约)

{
  "$schema": "hlzd/market-report/v1",
  "meta": { "topic": str, "audience": str, "window_days": int, "language": str },
  "cover": {
    "headline": str, "tagline": str,
    "kpis": [ {"label": str, "value": str, "unit": str?}, ... ],
    "three_step_plan": [ {"step": str, "action": str}, ... ]
  },
  "toc": [ {"id": str, "title": str}, ... ],
  "solution_overview": {
    "recap": str,
    "conclusion_table": [ {"metric": str, "finding": str, "implication": str}, ... ]
  },
  "three_signals": {
    "signals": [
      { "title": str, "user_pain_quote": str, "root_cause": str,
        "market_implication": str, "sources": [ {"name": str, "url": str}, ... ] }
    ]
  },
  "platform_deep_dive": {
    "platforms": [
      { "name": str, "stat": str,
        "findings": [ {"headline": str, "body": str, "source_name": str, "source_url": str}, ... ] }
    ]
  },
  "strategic_comparison": {
    "vendors": [ { "name": str, "attr_1": str, ... }, ... ],
    "attributes": [str, ...]
  },
  "action_plan": {
    "actions": [ {"title": str, "options": [str, ...], "risk": str, "execution": str}, ... ]
  },
  "methodology": {
    "five_step": [str, ...], "five_dim": [str, ...], "seven_limitations": [str, ...]
  },
  "footer_sources": {
    "sources": [ {"name": str, "url": str, "category": str}, ... ]
  }
}

Voice Contract (LAW 7 条)

继承自 b2b-overseas-market-report spec,落地为可执行检查:

LAW含义实施位置
1Body 开头 = "What I learned:"lib.check_law_1
2永远- 不用 em-dash / en-dashlib.normalize_dashes 自动替换
3引用必须 [name](url) inline linklib.md_link
4无 trailing Sources: block(footer 替代)lib.check_law_4
5每个 claim 必须有 URL 或 engagement signallib.validate_voice_contract
6"best X 2026" 按信号质量排heuristic,由 agent 做
7不假设用户是某个品牌lib.validate_voice_contract (brand_assumption)

输出前 validate_voice_contract() 跑一遍,violations 列表可由 pipeline 选 warn 或 block。


Integration with hlzd-b2b-research

# 跑完 b2b-research → 出 Markdown
py ../hlzd-b2b-research/scripts/run_research.py \
    --product "OCTG casing" --markets "UAE Saudi" \
    --output-json b2b-output.json

py scripts/pipeline.py --topic "auto" --from-research b2b-output.json \
    --output-html report.html --output-md report.md

from_b2b_research() adapter 自动把 b2b-research 的 trade_data / trends / buyers 转成 report schema:

  • 每个 market = 一个 signal
  • trade_data → platforms
  • HS code + 中国份额 → comparison data

Sample outputs

规模HTMLMarkdown
Standard (auto)~14 KB~4 KB
With data (full)~16 KB~5 KB

CSS 内嵌(自包含),无外部字体 / CDN 依赖。Print-friendly:自动翻转为高对比黑白版。


Anti-pattern / Limitations

限制处置
Brave API 需要 HLZD_BRAVE_API_KEYfallback 到 ddgs;都没有则手动输入信号
pip install ddgs 是 v0.1 唯一可选外部依赖需要网络爬取能力
报告不含 LLM 调用 — signals 是 heuristic 提炼上层 Agent 可接 LLM 增强 from_b2b_research 提炼 3 core signals
Voice Contract 检查是 advisory — 不阻断上层 Agent 可硬 fail 在 violation > 0
多人多语言一次性渲染:v0.1 仅 en 模板二期加 zh / es / ar 多语言模板

Versioning

版本说明
0.1.0首版:9 节 HTML + 8 节 Markdown + 从 hlzd-b2b-research 接入

升级轨迹见仓库根 VERSIONS.md


Crafted for B2B industrial exporters — HLZD Cross-Border AI Platform · 2026

What ships with it: 6 files

69.8 KB alongside SKILL.md, 6 of them executable

scripts/

tests/

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