anime-recommend

name	anime-recommend
description	Use when the user wants anime recommendations, taste profile analysis, or asks "what should I watch" / "summarize my taste". Also triggers passively — record user taste signals during normal conversation.

anime-recommend

Passive: Record Taste Signals

During normal conversation, update anime_taste.md via memory tool when the user:

• Rates or reviews an anime ("孤独摇滚真好看", "这番太无聊了")
• Marks done/dropped/on-hold via bangumi/update_collection
• States a preference ("我喜欢日常番", "不看机甲")

Append only. Dedup. Move items between Liked/Disliked on attitude change.

Active: Recommend

Step 1 — Get all data (single call)

Call anime-recommend/default.

Script does: Fetches user collections, builds tiered confidence stats, fetches calendar, computes Bayesian weighted scores — all in one call.

Script output has two parts:

Part 1 — Taste Profile:

• If up-to-date: returns existing anime_taste.md content.
• If new/changed: returns a tiered confidence report + titles list.
  • Strong (≥50%) — genre/studio/director in ≥50% of liked titles
  • Weak (30-50%) — in 30-50%
  • Below 30%: already filtered out by the script

Part 2 — Scored Candidates:

• Calendar listing + Bayesian weighted ranking of all airing titles.
• Each candidate has: raw score, votes, weighted score.
• weighted is for your internal ranking. raw is what users see.

Step 2 — Save profile (if new)

If the output says "ACTION REQUIRED", you must save the profile before recommending.

Call memory(action="write", filename="anime_taste.md"):

## Liked (Strong)
- genres: 战斗, 奇幻
- studios: MAPPA

## Liked (Weak)
- genres: 搞笑, 恋爱

## Disliked
- genres: 科幻

## Titles
- GIRLS BAND CRY → 10/10, loved: 作画, 音乐, 角色塑造
- 异世界舅舅 → dropped, disliked: 节奏太慢

CRITICAL: Only transcribe items that appear in the confidence report.

• If "Disliked Genres" section is empty in the report → omit ## Disliked entirely or leave genres blank.
• Do NOT infer preferences from the titles list. The titles list is context only — the confidence report is the source of truth.
• Do NOT copy genres from the example above — use the actual report output.

If profile is already up-to-date (output says "up-to-date"), skip this step.

Step 3 — Pick and present

From Part 2's weighted ranking, pick top 3-5. If taste profile exists, re-rank:

• Strong genre match → boost
• Weak genre match → mild boost
• Disliked genre match → penalty

Show raw score to user, NOT weighted score. Present each pick with: title, raw score, vote count, and why it matches (cite specific preferences). Flag disliked-genre overlaps as caveats. Skip titles already in user's collection.

No profile → present top weighted picks, note it's based on community ratings only.