ポスト添削くん-AI Post Optimizer

ポスト添削くん — Post Editing & Engagement Optimizer

ポスト添削くん is an assistant built to analyze, revise, and optimize social-media posts so they win more attention ("熱量" / engagement heat) on the platforms you use. Its design purpose is threefold: (1) measure a post's current engagement potential using two complementary heat frameworks (a fast 6-level triage and a fine-grained 16-level diagnostic), (2) produce step-by-step edits and multiple rewritten variants that increase that "heat," and (3) recommend publishing and distribution tactics (timing, format, hashtags, seeding) that make a post spread more effectively. Core concept of "heat": heat is a composite score capturing emotional intensity, clarity of the hook, call-to-action strength, shareability, novelty/timeliness, and structural signals (length, formatting, visual pairing). The 6-level framework is coarse and fast (for “quick go / no-go” decisions). The 16-level framework is granular and diagnostic; each of the 16 levels is evaluated across three diagnostic axes (tone signals, structural signals, and distribution triggers) so you get actionable surgery — not just a number. Examples and short scenarios: • Product launch tweet: Original copy reads bland and getsポスト添削くん紹介 a low 6-level score. ポスト添削くん diagnoses a weak hook and missing specificity, then returns 3 alternative hooks (one curiosity hook, one data hook, one social-proof hook), a tightened CTA, suggested image alt text, and recommended posting windows. Result: clearer message + higher predicted share rate. • Personal story thread: A creator drafts a multi-tweet thread that feels rambling. The 16-level analysis highlights inconsistent emotional arc (level subtype: "mixed-tone drift") and low micro-structure (no cliffhangers between tweets). The assistant rewrites the thread into 8 tightly sequenced tweets with micro-hooks and a sticky closing CTA that invites replies, plus suggestions for the first-line hook to pin. • Controversial take / crisis: A brand needs to post a sensitive apology. The system flags reputation risk triggers in the 16-level scan, proposes a compassionate tone template, gives a safe headline that admits responsibility without legal exposure, and lists rapid distribution steps (who to notify, which channels to prioritize) to reduce blowback. Practical deliverables you’ll get from one request: a heat score (6-level + mapped 16-level indicators), a short diagnosis (what’s weak/strong), 2–4 rewrite variants (different tones), step-by-step editing notes explaining why each change improves heat, recommended image/caption pairings, hashtags and first-comment options, and recommended posting windows for the target audience (timezone aware).