Content Creator

Meta-skill for orchestrating humanizer, de-ai-ify, copywriting, and tweet-writer to produce high-quality, platform-ready content that sounds authentic and human while preserving factual integrity. Use when users need persuasive posts and thread adaptations with anti-generic voice editing and engagement-focused structure.

Purpose

Create content that is:

• persuasive and high-signal,
• natural in voice,
• platform-appropriate,
• non-generic and non-template-like.

This skill coordinates upstream writing/editing skills; it does not claim guaranteed virality.

Required Installed Skills

• humanizer (inspected latest: 1.0.0)
• de-ai-ify (inspected latest: 1.0.0)
• copywriting (inspected latest: 0.1.0)
• tweet-writer (inspected latest: 1.0.0)

Install/update:

bash

npx -y clawhub@latest install humanizer
npx -y clawhub@latest install de-ai-ify
npx -y clawhub@latest install copywriting
npx -y clawhub@latest install tweet-writer
npx -y clawhub@latest update --all

Verify:

bash

npx -y clawhub@latest list

Requested Scenario Profile

Example scenario:

• User needs a LinkedIn post about remote work.
• The post should feel authentic and engagement-oriented.
• The final output should also include an X thread adaptation (5 tweets).

Inputs the LM Must Collect First

• topic (example: remote work)
• platform_primary (linkedin)
• target_audience (example: managers, founders, ICs)
• goal (reach, comments, shares, leads)
• voice_preferences (direct, reflective, contrarian, practical)
• author_context (first-hand experience, examples, proof points)
• hard_constraints (length, tone, banned claims/words)
• thread_required (yes/no, default yes for this scenario)

Do not draft copy before these are explicit.

Tool Responsibilities

humanizer

Use as first-pass anti-pattern editor:

• remove common AI writing signals,
• replace inflated/formulaic language with specific concrete phrasing,
• preserve meaning while increasing naturalness.

Important behavior:

• strongly pattern-based rewrite guidance,
• output is rewritten text + change summary,
• no guaranteed numeric score in the base humanizer skill.

de-ai-ify

Use as voice pass:

• reduce robotic transitions and hedging,
• simplify buzzword-heavy language,
• increase conversational rhythm,
• enforce direct, human cadence.

Important behavior:

• style/voice correction layer after humanizer,
• useful for adding opinionated nuance and natural texture.

copywriting

Use as persuasion structure pass:

• apply AIDA/PAS/FAB where appropriate,
• strengthen opening hook,
• sharpen value proposition,
• add one clear engagement CTA.

Important behavior:

• persuasive framework selection by goal,
• avoid over-salesy tone for social posts.

tweet-writer

Use as X/Twitter adaptation layer:

• convert long-form message into scroll-stopping tweet/thread format,
• optimize hooks, pacing, and mobile readability,
• enforce concise tweet structure.

Important boundary:

• this is X-oriented optimization, not LinkedIn-native optimization.

Canonical Pipeline

Use this order unless user requests otherwise.

Stage 1: Base draft (message-first)

Create a clean first draft for LinkedIn:

• one strong claim/opinion
• one concrete example
• one practical takeaway
• one question for comments

Avoid list-heavy, sterile, template-first drafting.

Stage 2: Humanizer pass (pattern cleanup)

Run the draft through humanizer logic:

• remove inflated symbolism and generic conclusions
• reduce over-structured AI cadence
• replace vague claims with specifics

Output target:

• same core meaning,
• lower obvious AI-pattern density,
• still readable and coherent.

Stage 3: De-AI-ify pass (voice)

Apply de-ai-ify voice shaping:

• remove excessive transitions and hedging
• tighten to direct, natural language
• introduce human rhythm (short + long sentence variation)

Output target:

• sounds like a person with a point of view,
• not like policy copy.

Stage 4: Copywriting pass (engagement architecture)

Apply copywriting frameworks to final LinkedIn post:

• opening: strong hook (bold thesis, tension, or contrarian angle)
• body: concise value block (problem -> insight -> implication)
• close: one engagement question (comments-oriented CTA)

Rule:

• one CTA only.

Stage 5: X adaptation (5-tweet thread)

Use tweet-writer principles to convert the same core argument into exactly 5 tweets:

• Tweet 1: hook
• Tweet 2: context/problem
• Tweet 3: key insight
• Tweet 4: practical framework/example
• Tweet 5: question CTA

Hard constraints:

• no external links in the main tweets unless user explicitly requests
• short, mobile-readable lines
• keep continuity and avoid repeating the same sentence across tweets

Causal Chain (Scenario Mapping)

For the scenario "LinkedIn post about remote work":

1. Agent drafts initial post on remote-work thesis.
2. humanizer flags typical AI-like signals and rewrites for specificity.
3. de-ai-ify adds conversational nuance and less robotic cadence.
4. copywriting strengthens hook and adds one engagement question.
5. tweet-writer transforms core message into a 5-tweet thread.

Output Contract

Always return:

• LinkedInPost_Final

  • final LinkedIn copy

• VoiceEdits_Summary

  • key changes from humanizer + de-ai-ify

• PersuasionStructure

  • framework used (AIDA/PAS/FAB) and why

• XThread_5Tweets

  • exactly five tweets, numbered 1/5 ... 5/5

• OptionalVariants

  • 2 alternative hooks
  • 2 alternative closing questions

Quality Gates

Before final output, verify:

• authenticity: text does not read like a rigid template
• specificity: at least one concrete detail/example included
• rhythm: sentence lengths vary naturally
• persuasion: one clear hook + one clear CTA
• platform fit: LinkedIn readable + X thread concise
• integrity: no fabricated data, experiences, or citations

If any gate fails, return Needs Revision with explicit reasons.

Guardrails

• Do not fabricate personal anecdotes or fake proof.
• Do not claim guaranteed virality or guaranteed reach outcomes.
• Do not hide factual uncertainty when claims are unverified.
• Keep persuasive language ethical and non-manipulative.
• Prioritize reader trust over stylistic gimmicks.

Known Limits from Inspected Upstream Skills

• Base humanizer is rewrite-focused and does not define a strict numeric AI score output.
• If numeric AI-likeness scoring is required (for example "85% AI"), this may need the optional ai-humanizer variant or explicit custom scoring rubric.
• tweet-writer optimizes for X, not LinkedIn ranking mechanics.
• These tools improve quality and naturalness but cannot guarantee SEO outcomes or detection immunity.

Treat these limits as required disclosure when presenting results.