Topic Research#

This skill deepens a selected topic after news-collect or any manually chosen theme. It does not replace first-pass collection. The report now also produces a local writing decision layer.

Quick Start#

Run the default command:

bash

.venv/bin/python -m skill_runtime.cli run-skill topic-research --input content-production/inbox/20260405-agent-topic-research.md

Prepare Input#

Pass a markdown request file with YAML frontmatter.

Supported fields:

• topic
• question
• model: mini / pro / auto
• source_file: optional path to a prior news-report.md
• seed_urls: optional list or comma-separated URLs

Example:

markdown

---
topic: AI coding agents
question: 这些产品近一周的产品化方向和商业化信号是什么？
model: pro
source_file: content-production/inbox/20260405-ai-news-report.md
seed_urls:
  - https://example.com/a
  - https://example.com/b
---

补充说明：优先输出能转成中文公众号选题判断的结论。

Follow Research Workflow#

1. Validate that tvly is installed and available on PATH.
2. Combine the request fields into a single research query.
3. Call tvly research ... --json.
4. Save the raw JSON and rewrite the result into a normalized markdown research report.
5. Add a writing-decision section covering whether the topic is worth writing, recommended structure, opening hooks, title directions, and evidence risks.

Write Output#

Write the report to:

text

content-production/inbox/YYYYMMDD-<slug>-research.md

Write the raw JSON to:

text

content-production/inbox/raw/research/YYYY-MM-DD/<slug>.json

Respect Constraints#

• Only use the repo-local dependency marker skills/tavily-research/ for this integration
• Do not silently fall back if tvly is missing or not logged in
• Keep the output contract stable even if Tavily CLI changes its JSON schema

Read Related Files#

• Runtime entry: skill_runtime/engine.py
• Wrapper runtime: skills/topic-research/runtime.py
• Vendor dependency marker: skills/tavily-research/
• Data contract: docs/data-contracts.md