Distill creator playbook
Skill ShenyuanNext/wepr-growth-skills/skills/distill-creator-playbook
Executable Agent Skills for PR, SEO/GEO, paid media, Xiaohongshu full-funnel operations, content, brand and global growth｜WEPR 可执行增长技能库
npx -y skills add ShenyuanNext/wepr-growth-skills --skill distill-creator-playbookAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 24 days oldThe repository was created 24 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Analyze public creator or brand-account content and distill reusable, ethical content patterns across positioning, topics, titles, openings, structures, proof, visuals, calls to action, comments, and publishing cadence. Use for 拆解博主, 对标账号, 内容蒸馏, 账号分析, 爆款结构, 创作者研究, 内容方法论, or converting public posts into an original execution playbook.
SKILL.md
3.1 KB, as published. Nobody here has run it
Distill Creator Playbook
Extract transferable decisions, not surface imitation.
Required inputs
Establish the platform, analysis target, whether it is a benchmark or the user's own account, analysis objective, sample window, and available public evidence. If a missing choice would materially change the result, ask for it; otherwise state a reasonable assumption.
Workflow
- Define the question the analysis must answer: positioning, topic selection, packaging, retention, conversion, or operating cadence.
- Build a traceable sample inventory with URL or identifier, date, format, title, topic, observable engagement, and evidence status.
- Separate observation from inference. Mark missing, inaccessible, deleted, or login-gated material.
- Code each item using the same dimensions: audience, job-to-be-done, hook, promise, structure, proof, emotion, visual device, CTA, and comment signal.
- Compare high-, middle-, and low-performing samples when comparable data exists. Do not infer causation from engagement alone.
- Identify recurring choices, controlled variations, exceptions, and possible confounders such as timing, paid distribution, celebrity, or prior audience size.
- Distill principles at three levels: stable positioning, reusable content modules, and testable packaging tactics.
- Translate principles into an original playbook with topic lanes, templates, examples, quality checks, and a 2–4 week test plan.
- Identify what must not be copied: wording, proprietary assets, personal stories, trademarks, signature characters, confidential data, or unverifiable claims.
- Return conclusions with evidence strength and unresolved questions.
Analysis discipline
- Use public content lawfully and respect access controls, privacy, copyright, and platform terms.
- Anonymize ordinary commenters and do not profile sensitive traits.
- Treat likes, saves, comments, and views as partial signals with different meanings.
- Do not call a pattern “爆款公式” unless repeated evidence supports it; prefer “working hypothesis.”
- Never invent inaccessible post content, comments, metrics, or account history.
Route by need
- Read references/coding-framework.md when analyzing samples.
- Read references/playbook-output.md when producing the final playbook or account comparison.
Deliverables
Return the relevant subset of: sample ledger, positioning map, topic architecture, title and opening patterns, content modules, proof and CTA patterns, visual system, comment insights, confidence notes, anti-copy boundaries, experiment backlog, and 30-day execution playbook.