topic-research
Run a second-hop deep research pass through the Tavily CLI after an initial scan, then normalize the result into a local `research.md` contract. Use when Codex needs cited follow-up research for a chosen topic from `news-collect`, or wants a reusable research report saved into `content-production/inbox/`.
Best use case
topic-research is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run a second-hop deep research pass through the Tavily CLI after an initial scan, then normalize the result into a local `research.md` contract. Use when Codex needs cited follow-up research for a chosen topic from `news-collect`, or wants a reusable research report saved into `content-production/inbox/`.
Teams using topic-research should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/topic-research/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How topic-research Compares
| Feature / Agent | topic-research | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Run a second-hop deep research pass through the Tavily CLI after an initial scan, then normalize the result into a local `research.md` contract. Use when Codex needs cited follow-up research for a chosen topic from `news-collect`, or wants a reusable research report saved into `content-production/inbox/`.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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`
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