| name | knowledge-cat-xiaohongshu-positioning |
| description | Use this skill when helping users position, launch, diagnose, or plan a Xiaohongshu/RedNote graphic-text account based on the 知识猫 AI 实战课程. It covers account positioning, direction scoring, benchmark teardown, first 20 post planning, 14-day validation, and AI prompt-assisted content planning. |
知识猫小红书图文起号定位
Use this skill to help a user move from a vague Xiaohongshu account idea to a testable positioning loop.
Core Workflow
- Write the positioning formula:
目标人群 × 具体问题 × 内容形式 × 成交产品.
- Score candidate directions before choosing one:
search demand, sustainable output, differentiation, monetization fit, and risk boundary.
- Pick one launch model:
knowledge infographic, vertical problem-solving, business lead generation, or tutorial/cohort.
- Build a 14-day test:
positioning card, benchmark library, first 20 topics, 7-10 published notes, then review click/save/comment/follow/DM/homepage visits.
- Use AI as co-pilot:
generate options, prompts, topic lists, and templates, but require human judgment on resources, facts, delivery capability, and compliance.
When Working
- Do not treat broad track names as positioning. Convert "AI 美学", "女性成长", "跨境电商", etc. into a concrete user/problem/content/offer loop.
- Do not judge an account from 1-2 posts. Ask for 10-20 same-positioning samples before declaring failure.
- For new users, prefer one account, one direction, one main content template.
- Keep monetization lightweight early: template packs, topic tables, prompt packs, light consulting, diagnosis, custom visuals, short cohort, or small course.
- Avoid fake persona, unverifiable claims, medical/finance/legal overreach, and AI content that cannot be delivered or fact-checked.
References
- Read
references/course_notes.md for the distilled course method, templates, and examples.
- Read
references/video_notes.md for the Whisper-derived operational additions: account health checks, violation risks, benchmark teardown, growth heuristics, and the perfume account case.
- Read
references/ppt_extracted_text.md when you need the original slide wording.
- Read
references/transcripts/knowledge_cat_xiaohongshu_positioning_01.txt or .vtt only when you need the raw transcript; it contains recognition noise.
- Use
scripts/extract_pptx_text.py to refresh slide text from a PPTX.
- Use
scripts/transcribe_video_whisper.py to generate Whisper subtitles from the source video. Prefer Whisper transcript over OCR for spoken course content; use OCR only for visual-only code, diagrams, or screen text.