nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Find relevant Reddit, HN, and ProductHunt threads and draft value-first community responses with launch checklist. Use when user says "find communities", "draft outreach", "Reddit promotion", "ProductHunt launch", or "community marketing". Do NOT use for social media posts (use /content-gen) or video scripts (use /video-promo).
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install fortunto2/solo-community-outreach打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
Find relevant community threads (Reddit, Hacker News, ProductHunt) and draft contextual, value-first responses. NOT spam — genuine helpful answers that naturally mention the product.
web_search(query, engines, include_raw_content) — search Reddit, HN, webkb_search(query) — find related methodologyproject_info(name) — get project detailsIf MCP tools are not available, use WebSearch/WebFetch as fallback.
Parse project from $ARGUMENTS.
Extract search keywords:
Search communities (run searches in parallel):
For each keyword group, search via MCP web_search(query) or WebSearch:
"{problem} reddit" — pain point threads"{solution category} recommendations reddit" — recommendation requests"{competitor} alternative reddit" — competitor frustration"{competitor} vs reddit" — comparison threadsFor each result, extract: subreddit, title, URL, post date, comment count. Filter: prefer threads < 6 months old, > 5 comments (active).
Search via site:news.ycombinator.com:
"Show HN: {similar product category}" — similar launches"Ask HN: {problem domain}" — questions in the space"{competitor name} site:news.ycombinator.com" — competitor mentionsExtract: title, URL, points, comment count.
Search via site:producthunt.com:
"{product category} site:producthunt.com" — similar launches"{competitor} site:producthunt.com" — competitor pagesExtract: product names, launch dates, upvote counts, taglines.
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# /community-outreach
Find relevant community threads (Reddit, Hacker News, ProductHunt) and draft contextual, value-first responses. NOT spam — genuine helpful answers that naturally mention the product.
## MCP Tools (use if available)
- `web_search(query, engines, include_raw_content)` — search Reddit, HN, web
- `kb_search(query)` — find related methodology
- `project_info(name)` — get project details
If MCP tools are not available, use WebSearch/WebFetch as fallback.
## Steps
1. **Parse project** from `$ARGUMENTS`.
- Read PRD/README to understand: problem, solution, ICP, key features.
- If empty: ask via AskUserQuestion.
2. **Extract search keywords:**
- Problem keywords (what users complain about)
- Solution keywords (what users search for)
- Category keywords (the market/niche)
- Competitor names (for "vs" and "alternative" threads)
3. **Search communities** (run searches in parallel):
### 3a. Reddit
For each keyword group, search via MCP `web_search(query)` or WebSearch:
- `"{problem} reddit"` — pain point threads
- `"{solution category} recommendations reddit"` — recommendation requests
- `"{competitor} alternative reddit"` — competitor frustration
- `"{competitor} vs reddit"` — comparison threads
For each result, extract: subreddit, title, URL, post date, comment count.
Filter: prefer threads < 6 months old, > 5 comments (active).
### 3b. Hacker News
Search via `site:news.ycombinator.com`:
- `"Show HN: {similar product category}"` — similar launches
- `"Ask HN: {problem domain}"` — questions in the space
- `"{competitor name} site:news.ycombinator.com"` — competitor mentions
Extract: title, URL, points, comment count.
### 3c. ProductHunt
Search via `site:producthunt.com`:
- `"{product category} site:producthunt.com"` — similar launches
- `"{competitor} site:producthunt.com"` — competitor pages
Extract: product names, launch dates, upvote counts, taglines.
### 3d. Oth...AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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