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)
• INLINECODE2 (inspected latest: 1.0.0)
• INLINECODE4 (inspected latest: 0.1.0)
• INLINECODE6 (inspected latest: 1.0.0)

Install/update:

CODEBLOCK0

Verify:

CODEBLOCK1

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)
• INLINECODE9 (linkedin)
• INLINECODE11 (example: managers, founders, ICs)
• INLINECODE12 (reach, comments, shares, leads)
• INLINECODE13 (direct, reflective, contrarian, practical)
• INLINECODE14 (first-hand experience, examples, proof points)
• INLINECODE15 (length, tone, banned claims/words)
• INLINECODE16 (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. 1. Agent drafts initial post on remote-work thesis.
2. INLINECODE24 flags typical AI-like signals and rewrites for specificity.
3. INLINECODE25 adds conversational nuance and less robotic cadence.
4. INLINECODE26 strengthens hook and adds one engagement question.
5. INLINECODE27 transforms core message into a 5-tweet thread.

Output Contract

Always return:

• - INLINECODE28

• - INLINECODE29

• - INLINECODE30

• - INLINECODE31

• - INLINECODE32

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.
• INLINECODE36 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.