Skill: h4gen/content-creator-skill (version 1.0.0)

--- 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."}} --- # 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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)