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README.md

darkseoking — SEO & Threads Algorithm Skills

Three-layer skill set built on the methodology of @darkseoking — an SEO practitioner and Threads creator with 10+ years of hands-on experience, 17k+ followers grown through algorithm-informed content strategy. He spent real money on controlled experiments and reverse-engineered Meta/Google algorithm patents to understand how content distribution actually works. Every insight in these skills is backed by patent filings or tested data — no speculation, no "industry consensus" parroting.

Architecture

Knowledge        →  darkseoking-mindset         8 mental models + 8 reference docs
Operations       →  darkseoking-post-optimizer   Pre-publish checklist (PASS/WARN/FAIL)
Prediction       →  darkseoking-post-predictor   Engagement ceiling estimation

Skills

darkseoking-mindset

Triggers: SEO algorithm questions, content strategy, Threads growth, GEO optimization, vendor evaluation

The knowledge layer. Eight core mental models (reverse-engineer from patents, pay to test, question consensus, cross-platform logic, boundary conditions, minimize cost, toolify knowledge, account positioning) plus eight deep-dive reference docs covering algorithm analysis, content strategy, Threads growth, GEO/AI citation, vendor evaluation, AI tooling, experiment design, and operational philosophy.

darkseoking-post-optimizer

Triggers: Writing/reviewing Threads posts, post-viral strategy, posting timing

The operations layer. Runs an 8-item pre-publish checklist — each item rated PASS/WARN/FAIL with specific reasoning:

  1. First Line (info density, no clickbait)
  2. Semantic Precision (domain-specific terms)
  3. Topic Distance (diversity penalty avoidance)
  4. External Links (authority + relevance)
  5. Thread Structure (standalone main + value-add threads)
  6. Timing (self-competition avoidance)
  7. Post-Viral Strategy (only after viral post)
  8. Topic Suggestion (only when asked "what to write")

Example: Optimizer in Action

A marketing consultant's account focuses on SEO and AI tools. Their last post (12 hours ago) about "5 AI writing tools" got 800 likes — well above their usual 200. They now want to post this:

Draft:

AI is changing everything about how we do SEO. Here are 3 more tools that will blow your mind and transform your content workflow forever. Trust me, these are game-changers! 🚀🔥

Link: bestaitools.xyz/my-affiliate-list

Checklist result:

# Check Rating Reason
1 First Line FAIL "AI is changing everything" is a textbook AI-typical opener with zero info density. "Blow your mind" and "game-changers" are clickbait filler.
2 Semantic Precision FAIL Dominated by generic terms ("AI", "SEO", "content workflow"). No domain-specific nouns — which tools? what kind of SEO? Nothing makes the target audience think "this is for me."
3 Topic Distance FAIL Same topic (AI tools) + same angle (listicle) as the post 12 hours ago. Diversity mechanism (US9336553B2) will penalize same-source density.
4 External Links FAIL Unknown domain (bestaitools.xyz), likely affiliate link. Low authority + content relevance unclear.
5 Thread Structure WARN "3 tools" has enough content for threads, but is packed into a single post. Splitting into 1 tool per thread reply would add independent information value per reply.
6 Timing FAIL Last post was 12 hours ago and is still in active diffusion (800 likes = well above baseline). Posting now competes with yourself (US8402094B2).
7 Post-Viral Strategy FAIL Last post went semi-viral. Should be engaging with comments on that post, not rushing a follow-up on the same topic.

Result: 1 WARN, 6 FAIL — do not publish.

Optimizer recommendations:

  1. Wait 24-48h. Let the 800-like post finish its diffusion cycle. Use that time to reply to every comment on it instead.
  2. Change topic. If the account covers SEO + AI tools, the next post should lean into SEO (different topic, same niche) rather than another AI tools listicle.
  3. Kill the affiliate link. Either remove it entirely (safest) or replace with a high-authority source that directly supports the content.
  4. Rewrite the opener. Lead with a specific, counterintuitive finding — not "AI is changing everything."

Rewritten (after waiting 48h):

I used to mass-produce SEO articles with AI — 50 per week. Rankings went up for 2 months, then every single one dropped out of the top 100. Here's what the data showed was killing them, and the 3-step fix that brought 80% back.

[Thread 1] The first signal was... [specific data] [Thread 2] The fix that worked... [Thread 3] What I'd do differently now...

Changed topic (AI content quality, not tools listicle), led with a counterintuitive first-hand result, removed affiliate link, added thread structure for depth.


darkseoking-post-predictor

Triggers: Predicting post performance, analyzing history, estimating engagement ceiling

The prediction layer. Estimates engagement ceiling for a draft, with or without personal post history data. With data (15+ posts): builds personal baseline, extracts patterns, predicts against them. Without data: uses darkseoking's 84-post benchmark as fallback for directional guidance.

Shared Data

  • darkseoking-all-posts.csv — 84 posts with engagement metrics (likes, reposts, comments, thread count, content), used as benchmark data across all three skills

Key Patents Referenced

Patent Mechanism
US10579688B2 Semantic vector matching for content distribution
US9336553B2 Diversity mechanism (penalizes same-source density)
US10558714B2 Creator Embedding (account positioning)
US9378529B2 Expected Engagement Value
US8402094B2 Self-competition penalty for rapid posting
US10565267B1 Rapid posting distribution penalty
US20140172877A1 Priority distribution pool after pause
EP2977948A1 Domain authority evaluation for links
US10268763B2 Content relevance scoring for links

Setup

New to Claude Code skills? See the ai-toolkit main README for general setup instructions.

  1. Copy the three skill directories (darkseoking-mindset/, darkseoking-post-optimizer/, darkseoking-post-predictor/) into your project's .claude/skills/
  2. Place darkseoking-all-posts.csv where the skills can reference it (e.g., alongside the skill directories, then adjust paths in SKILL.md)
  3. Claude Code will auto-detect the skills based on their description triggers — no manual activation needed

Each skill works independently but they complement each other:

  1. mindset gives you the "why" — algorithm mechanics and strategy principles
  2. optimizer gives you the "check" — systematic review before publishing
  3. predictor gives you the "what if" — performance estimation before committing