Imported from manhgg22/agentFullDukick (agents/dukick-pm-8769/skills/content-engine/SKILL.md). Install upstream with npx skills add manhgg22/agentFullDukick --skill content-engine. Copyright stays with the author.

Content Engine

Build platform-native content without flattening the author's real voice into platform slop.

When to Activate

• writing X posts or threads
• drafting LinkedIn posts or launch updates
• scripting short-form video or YouTube explainers
• repurposing articles, podcasts, demos, docs, or internal notes into public content
• building a launch sequence or ongoing content system around a product, insight, or narrative

Non-Negotiables

1. Start from source material, not generic post formulas.
2. Adapt the format for the platform, not the persona.
3. One post should carry one actual claim.
4. Specificity beats adjectives.
5. No engagement bait unless the user explicitly asks for it.

Source-First Workflow

Before drafting, identify the source set:

• published articles
• notes or internal memos
• product demos
• docs or changelogs
• transcripts
• screenshots
• prior posts from the same author

If the user wants a specific voice, build a voice profile from real examples before writing. Use brand-voice as the canonical workflow when voice consistency matters across more than one output.

Voice Handling

brand-voice is the canonical voice layer.

Run it first when:

• there are multiple downstream outputs
• the user explicitly cares about writing style
• the content is launch, outreach, or reputation-sensitive

Reuse the resulting VOICE PROFILE here instead of rebuilding a second voice model. If the user wants Affaan / ECC voice specifically, still treat brand-voice as the source of truth and feed it the best live or source-derived material available.

Hard Bans

Delete and rewrite any of these:

• "In today's rapidly evolving landscape"
• "game-changer", "revolutionary", "cutting-edge"
• "here's why this matters" unless it is followed immediately by something concrete
• ending with a LinkedIn-style question just to farm replies
• forced casualness on LinkedIn
• fake engagement padding that was not present in the source material

Platform Adaptation Rules

X

• open with the strongest claim, artifact, or tension
• keep the compression if the source voice is compressed
• if writing a thread, each post must advance the argument
• do not pad with context the audience does not need

LinkedIn

• expand only enough for people outside the immediate niche to follow
• do not turn it into a fake lesson post unless the source material actually is reflective
• no corporate inspiration cadence
• no praise-stacking, no "journey" filler

Short Video

• script around the visual sequence and proof points
• first seconds should show the result, problem, or punch
• do not write narration that sounds better on paper than on screen

YouTube

• show the result or tension early
• organize by argument or progression, not filler sections
• use chaptering only when it helps clarity

Newsletter

• open with the point, conflict, or artifact
• do not spend the first paragraph warming up
• every section needs to add something new

Repurposing Flow

1. Pick the anchor asset.
2. Extract 3 to 7 atomic claims or scenes.
3. Rank them by sharpness, novelty, and proof.
4. Assign one strong idea per output.
5. Adapt structure for each platform.
6. Strip platform-shaped filler.
7. Run the quality gate.

Deliverables

When asked for a campaign, return:

• a short voice profile if voice matching matters
• the core angle
• platform-native drafts
• posting order only if it helps execution
• gaps that must be filled before publishing

Quality Gate

Before delivering:

• every draft sounds like the intended author, not the platform stereotype
• every draft contains a real claim, proof point, or concrete observation
• no generic hype language remains
• no fake engagement bait remains
• no duplicated copy across platforms unless requested
• any CTA is earned and user-approved

Related Skills

• brand-voice for source-derived voice profiles
• media-generation for generating images, videos, and audio for posts

Appendix A: Platform Distribution Reference

Platform Comparison

• X (Twitter): 280 chars/post, threads, images/video, polls, Spaces. Fastest iteration.
• LinkedIn: 3k chars/post, articles, newsletters, documents. Professional context.
• Telegram: Unlimited text, channels/groups, polls, reactions. Direct subscriber model.
• Discord: Rich formatting, threads, roles, bots. Niche communities.

Multi-Language Workflow

1. Write in primary language
2. Identify cultural references, idioms, humor
3. Translate literally first
4. Adapt for cultural context
5. Check character limits per language
6. Preserve CTA and key metrics
7. Review with native speaker if possible
8. Schedule with timezone consideration

Platform-Specific Formatting

X:

Hook line (no emojis in first line)
Body with line breaks
Thread indicator: "(1/7)"
Media: 1 image or 1 video per post

LinkedIn:

First line = scroll stopper
Body with short paragraphs
Hashtags: 3-5 relevant
Tag relevant people
Call to action at end

Telegram:

Bold headers with **markdown**
Bullet points for scannability
Links with preview
Images/gifs inline

Discord:

Use embeds for announcements
Thread names: descriptive
Use roles for @mentions
Rich formatting: ```code```, > quotes

Appendix B: Direct X/Twitter API Integration

Use for advanced posting beyond built-in tools. Requires OAuth 1.0a tokens: TWITTER_API_KEY, TWITTER_API_SECRET, TWITTER_ACCESS_TOKEN, TWITTER_ACCESS_TOKEN_SECRET.

Python Example (OAuth1Session)

from requests_oauthlib import OAuth1Session
import json

client = OAuth1Session(
    consumer_key, client_secret=consumer_secret,
    resource_owner_key=access_token,
    resource_owner_secret=access_token_secret
)

response = client.post(
    "https://api.twitter.com/2/tweets",
    json={"text": "Hello from the X API!"}
)

Media Upload (v1.1)

media = open("image.png", "rb")
media_upload = client.post(
    "https://upload.twitter.com/1.1/media/upload.json",
    files={"media": media}
)
media_id = media_upload.json()["media_id_string"]

response = client.post(
    "https://api.twitter.com/2/tweets",
    json={"text": "Check this out!", "media": {"media_keys": [media_id]}}
)

Thread Posting

tweets = ["First tweet", "Second tweet", "Third tweet"]
prev_id = None
for text in tweets:
    payload = {"text": text}
    if prev_id:
        payload["reply"] = {"in_reply_to_tweet_id": prev_id}
    resp = client.post("https://api.twitter.com/2/tweets", json=payload)
    prev_id = resp.json()["data"]["id"]

Error Handling

• 403 Forbidden — check token permissions (needs tweet.write)
• 429 Too Many Requests — rate limit exceeded; implement exponential backoff
• 401 Unauthorized — token expired or invalid; re-authenticate

Rate Limits (v2)

• Tweet creation: 200 per 15 min per user
• Media upload: 500 per 24 hours
• Read timeline: 180 per 15 min

Analytics

response = client.get(
    "https://api.twitter.com/2/users/me/tweets",
    params={"tweet_fields": "public_metrics,created_at", "max_results": 10}
)
for tweet in response.json()["data"]:
    print(f"{tweet['text'][:50]}... | Likes: {tweet['public_metrics']['like_count']}")

Appendix C: Cronjob Scheduling

Schedule recurring content distribution via cronjob.

Daily LinkedIn post:

prompt: "Draft and post a daily update to LinkedIn using content-engine voice and formatting."
enabled_toolsets: ["web", "send_message"]
schedule: "0 9 * * *"

Thread campaign:

prompt: "Post the next tweet in the #BuildInPublic thread series. Reference previous tweets via the X API."
enabled_toolsets: ["web", "terminal"]
schedule: "0 10 * * 1,3,5"