一键导入
reddit-research-post
Research subreddits and create posts — find the right subreddit, analyze trends, check rules and flairs, craft content, draft for review
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Research subreddits and create posts — find the right subreddit, analyze trends, check rules and flairs, craft content, draft for review
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
Analyze spreadsheet data intelligently - understand structure, choose right analysis tool (SQL/pivot/chart), apply formatting, present insights.
Create charts and visualizations in Google Sheets — detect data structure, select chart type, format for clarity, present insights
Generate an editable Microsoft Word (.docx) document — reports, letters, memos, structured docs. Use when the user wants a Word file. Built with docx-js (Node) for high fidelity.
Generate a polished, printable PDF — reports, letters, invoices, resumes, one-pagers. Use when the user wants a PDF document. Typst is the primary engine; LaTeX (tectonic) is the fallback for niche packages or specific academic/journal templates.
Generate a PowerPoint (.pptx) slide deck — pitch decks, reviews, summaries. Use when the user wants slides. Built with pptxgenjs (Node) for native, editable slides and charts.
Generate an Excel (.xlsx) workbook or a CSV file — tables, financial models, data exports, formatted reports with charts. Use when the user wants a spreadsheet or CSV. Built with openpyxl + pandas (Python).
基于 SOC 职业分类
| name | reddit-research-post |
| description | Research subreddits and create posts — find the right subreddit, analyze trends, check rules and flairs, craft content, draft for review |
| target | reddit_agent |
User wants to post on Reddit for marketing, community engagement, research, or content sharing.
Find relevant subreddits:
REDDIT_GET_SUBREDDITS_SEARCH(q="artificial intelligence", limit=10)
Check subreddit rules (critical before posting):
REDDIT_GET_SUBREDDIT_RULES(subreddit="MachineLearning")
Analyze what works — read top posts:
REDDIT_GET_R_TOP(subreddit="MachineLearning", t="month", limit=10)
REDDIT_RETRIEVE_REDDIT_POST(subreddit="startup", sort="hot", max_results=10)
When researching multiple subreddits in parallel:
spawn_subagent(task="Research r/MachineLearning - top posts and rules", context="Focus on what content succeeds and community rules")
spawn_subagent(task="Research r/startup - top posts and rules", context="Focus on what content succeeds and community rules")
spawn_subagent(task="Research r/AI - top posts and rules", context="Focus on what content succeeds and community rules")
This enables parallel research across multiple subreddits.
Think:
Before posting, check if the topic was recently covered:
REDDIT_SEARCH_ACROSS_SUBREDDITS(
search_query="title:AI productivity tools subreddit:startup",
sort="new",
limit=20
)
If similar recent post exists → warn user about potential duplicate.
Many subreddits require flair:
REDDIT_GET_R_SUBREDDIT_LINK_FLAIR_V2(subreddit="startup")
Pick the most appropriate flair based on content.
Text post (self):
Content structure:
- Hook: Opening that grabs attention
- Value: What the reader gains
- Body: Details, evidence, experience
- CTA: Question or call to discussion
Reddit markdown tips:
- Use ## headers for sections
- **Bold** key points
- Use bullet lists
- Keep paragraphs short (2-3 sentences)
- TL;DR at the end for long posts
Present draft to user before posting:
Reddit Post Draft:
Subreddit: r/startup
Flair: "Discussion"
Title: "We cut our customer onboarding time by 60% — here's exactly how"
---
[Body preview]
---
Rules check: Self-promotion allowed (max 1 in 10 posts)
Similar posts: None in past 30 days
Should I post this?
REDDIT_CREATE_REDDIT_POST(
subreddit="startup",
title="We cut our customer onboarding time by 60% — here's exactly how",
text="Full markdown body...",
kind="self",
flair_id="uuid-from-flair-list" # Must be valid UUID from REDDIT_GET_R_SUBREDDIT_LINK_FLAIR_V2
)
For link posts:
REDDIT_CREATE_REDDIT_POST(
subreddit="startup",
title="Great article on scaling",
url="https://example.com/article",
kind="link"
)
If the user wants to respond to comments:
REDDIT_RETRIEVE_POST_COMMENTS(article="t3_...")
REDDIT_POST_REDDIT_COMMENT(thing_id="t1_...", text="Thanks for the feedback! ...")