By name: darkseoking-post-predictor description: Use when predicting Threads post performance, analyzing post history patterns, estimating engagement ceiling for a draft, or deciding what content type to write next. Works with or without personal data — uses darkseoking benchmark as fallback.
Threads Post Predictor
Analyzes historical Threads post data using algorithm knowledge from darkseoking-mindset to predict post performance and recommend optimal content strategy.
How to Use
With personal profile (most precise)
- Load personal-profile.md — pre-built account baselines, quadrant data, persona tags
- Load prediction-model.md — run V2 dual-stage prediction using personal data
- Present findings: quadrant diagnosis, Views/ER ranges, optimization path
With post history CSV but no profile (build one)
- User provides post history — see Data Format below
- Load historical-analysis.md — build personal baseline and patterns
- Save output as
references/personal-profile.mdfor future use - Load prediction-model.md — predict using freshly built profile
Without any personal data (benchmark only)
- Skip historical-analysis.md and personal-profile.md
- Load prediction-model.md — use darkseoking benchmark patterns directly
- Predict based on content type hierarchy, thread structure, and algorithm rules
- Note: predictions are directional (which content type has higher ceiling) rather than numeric. Cannot distinguish distribution-driven vs conversion-driven success without views data.
Data Format
Minimum useful data per post: content (or topic summary) + at least one engagement metric (likes, comments, or reposts).
For V2 dual-stage prediction (strongly recommended): views + likes per post. Without views, prediction falls back to V1 single-stage and cannot distinguish distribution-driven vs conversion-driven success.
Ideal CSV columns: content, likes, views, replies, reposts, shares, engagement_rate, media_type, is_quote, created_at
Minimum posts: 15+ for meaningful baseline. 30+ for pattern extraction. Under 15 — use darkseoking benchmark with caveats.
Accepted formats: CSV, pasted list, verbal description of recent 5-10 posts with approximate engagement numbers.
Personal profile: If references/personal-profile.md exists, load it to skip re-running full historical analysis. If it doesn't exist and user provides CSV, run historical-analysis.md and save the output as references/personal-profile.md. Profiles should be refreshed when new data is available (e.g. monthly).
Scenes
| Scenario | Action |
|---|---|
| User provides full post history | Run complete analysis + prediction |
| User asks "what should I write next?" with data context | Run content-type recommendation |
| User wants to know ceiling before posting | Run prediction on draft + historical context |
| User wants to understand why a post underperformed | Run gap analysis against historical patterns |
| User has no data, just wants general guidance | Use darkseoking benchmark patterns from prediction-model.md |
References
- historical-analysis.md — how to extract patterns from post data
- prediction-model.md — how to predict ceiling and recommend strategy (V2: dual-stage Views × ER)
- personal-profile.md — personal baseline, quadrant profile, persona tags (gitignored; built from user's post data)
- Benchmark data →
darkseoking-mindset/references/darkseoking-all-posts.csv - Algorithm knowledge →
darkseoking-mindsetskill