topic-brief
Generate a topic-focused briefing in HTML from public news sources. Use when user asks for a briefing / observation / digest / 简报 / 观察 on any subject — region (Middle East, ASEAN, India), industry (semiconductors, EV supply chain, AI), policy issue (AI regulation, critical minerals, cross-border payments), institution (Fed, ECB, IMF), or theme. Output is a single self-contained HTML file with blue-color "TOPIC BRIEF" branding, optimized for both browser viewing and direct copy-paste into 微信公众号 / WeChat Official Account editor. Walks through 5 steps with one user confirmation midway. Do NOT use
npx skills add genli-ai/market-research-skills --skill topic-brief --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Topic Brief — Thematic Observation Briefing Generator Generate a public-news-based information briefing for any subject (region / industry / policy issue / institution) and produce a single self-contained HTML file ready to paste into the 微信公众号 / WeChat Official Account editor. > Bilingual skill. Chinese version: `SKILL.zh.md`. English is the single source of truth; the `.zh.md` is a synchronized translation — always edit the English first, then mirror the change into `.zh.md` in the same change-set, never edit only the Chinese. ## Trigger phrases User says something like: - "做一份 5 月下半月的中东观察" - "Generate a semiconductor industry briefing for the past month" - "做一期 AI 立法主题观察，时间是 2026 年 Q2" - "/topic-brief" - "用 topic-brief 跑一份 ..." Any descriptive request such as "make a briefing / observation / digest on XX" should activate this skill. ## Scope exclusions Before producing any briefing, check whether the subject falls into a refused category. The following are out of scope regardless of phrasing: - **Political issues** — elections, parties, political figures' positions, government legitimacy debates - **Military issues** — operations, force composition, equipment counts, conflict
- Trigger phrases
- Scope exclusions
- Response language
- 5-step workflow
- Step 1 — Collect parameters (one user-facing question prompt, 4 questions)
- Step 2 — Parallel material gathering
- Step 3 — Direction confirmation (mandatory user check-in) ⚠️
- Step 4 — Compose the JSON
- Step 5 — Render and report
- Tool-call mapping (cross-LLM adaptation)
- Failure handling
- Quick command reference
- Self-check checklist (run after Step 4)
- Red lines
python3 scripts/render.py output/seed/<subject>_<period_end>.json --out output --open
Enter the skill directory (path inside the market-research-skills monorepo)
cd /path/to/market-research-skills/skills/topic-brief
Render (auto-fixes quotes + opens browser)
python3 scripts/render.py reference/region_middle_east.example.json --out output --open
Quote-fix only
python3 -c "from lib.fix_quotes import fix_file; from pathlib import Path; fix_file(Path('output/seed/xxx.json'))"
Install dependency (first time)
python3 -m pip install --user jinja2What does the topic-brief skill do?
Generate a topic-focused briefing in HTML from public news sources. Use when user asks for a briefing / observation / digest / 简报 / 观察 on any subject — region (Middle East, ASEAN, India), industry (semiconductors, EV supply chain, AI), policy issue (AI regulation, critical minerals, cross-border payments), institution (Fed, ECB, IMF), or theme. Output is a single self-contained HTML file with blue-color "TOPIC BRIEF" branding, optimized for both browser viewing and direct copy-paste into 微信公众号 / WeChat Official Account editor. Walks through 5 steps with one user confirmation midway. Do NOT use
How do I install it?
Run `npx skills add genli-ai/market-research-skills --skill topic-brief --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From genli-ai/market-research-skills, a repository with 57 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.