Agent skill · AI & Agents

report-export

Export a final reviewed research report into one or more requested output formats. This skill is the unified output-layer renderer for the pipeline.

Yiming Zhao438★ · 1 repos on radarProfile →
cursorships scriptsMIT
Install
npx skills add gaotiexinqu/OneResearchClaw --skill report-export --agent cursor

Same command for any agent — swap --agent for claude-code, codex, copilot.

Facts
Files in the skill folder: 10
SKILL.md size: 10 KB
Bundled scripts: yes
Path: .cursor/skills/report-export/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 438
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Report Export This skill is the **unified output-layer export skill**. It reads a final reviewed research report and renders it into one or more requested output formats. This skill is the output-layer renderer for the pipeline: grounding -> grounded-research-lit -> grounded-summary -> grounded-review -> report-export --- ## Purpose Use this skill to export a final reviewed report from: - `data/

More from OneResearchClaw
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About this skill
What does the report-export skill do?

Export a final reviewed research report into one or more requested output formats. This skill is the unified output-layer renderer for the pipeline.

How do I install it?

Run `npx skills add gaotiexinqu/OneResearchClaw --skill report-export --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 gaotiexinqu/OneResearchClaw, a repository with 438 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.

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