Content Creator

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name: human-masked-content-creator description: 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. homepage: https://clawhub.ai user-invocable: true disable-model-invocation: false metadata: {"openclaw":{"emoji":"writing_hand","requires":{"bins":["node","npx"],"env":[],"config":[]},"note":"Requires local installation of humanizer, de-ai-ify, copywriting, and tweet-writer."}}

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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:

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:

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.