TV Show Recommendation Skill

Step 0: Refresh Trakt history (MANDATORY — do not skip)

Before reading trakt-history.json, refresh it:

mcp__nanoclaw__fetch_trakt_history()

Writes fresh /workspace/group/trakt-history.json. Skipping recommends already-watched shows. On MCP failure, proceed with the existing file but add this staleness note as the FIRST line of Step 5's output (before pitches): <i>⚠️ Trakt refresh failed — using cached history as of {mtime, ISO date}. Recommendations may include already-watched titles.</i>. Do NOT fabricate freshness; do NOT bury the note.

Data Sources

• /workspace/group/netflix-history.csv — Netflix history. Format: "Show Name: Season X: Episode Name", "date" — split on : . Single-part titles are movies (skip). Group by show name.
• /workspace/group/imdb-ratings.csv — Explicit ratings (Const, Your Rating 1–10, Title, Title Type, Year, Genres). ~160 entries.
• /workspace/group/trakt-history.json — Trakt watch history. Format: [{"show": {"title": ...}, "episode": {"season": N, "number": N}, "watched_at": ...}]. Refreshed in Step 0 — trust Trakt over CSVs for recency. On Step 0 failure, Step 1a still applies and Step 5's staleness preamble discloses the age.
• /workspace/group/watchlist.json — Upcoming tracked shows. Check before web research (Step 4a).

Filter out kids' content: animated children's shows, preschool series, toy-brand cartoons.

Step 1a: Source priority

Trakt is the primary source — live and synced across all platforms. The CSVs are static exports that go stale.

If Baruch reports "уже видел" for a recommendation, Trakt sync hadn't finished — note and pivot.

Step 2: Classify shows

For each show, compute episodes_watched / total_episodes (search for total if needed). Derive thresholds from actual data distribution before applying these defaults:

• Completed: ratio > 0.8, or last episode of final season in history. Ongoing "caught up" = no gap in recent seasons.
• Abandoned: ratio < 0.15 AND episodes_watched ≤ 3 AND no return after initial cluster.
• In progress: Everything else — do not re-recommend unless a new season exists.

Shows with 1–3 plays that never resumed are strong abandonment signals. IMDB ratings (imdb-ratings.csv): high-rated genres = confirmed loves; low-rated = genres that don't click.

Step 3: Taste profile

Derive from data files using Step 2 classifications and IMDB ratings:

• Top genres — most frequent in high-rated, high-completion shows
• Avoided genres — frequent in abandoned or low-rated shows
• Style signals — patterns from comparing completed vs abandoned (language, episode structure, tone)
• Quality calibration — use median and percentiles of Baruch's own IMDB ratings as the floor

Intermediate output format (emit before Step 4 to anchor recommendations):

## Taste Profile (derived from data)

**Top genres:** Crime/Thriller, Dark Comedy, Sci-Fi (hard), Drama
**Avoided genres:** Reality TV, Procedural, Romantic Comedy
**Language signal:** Non-English shows rate 0.4 pts higher on average (lean into it)
**Episode structure:** Prefers serialized > episodic (completion rate 78% vs 41%)
**Quality floor:** Baruch's median IMDB rating = 7.8 → use 7.5 as recommendation floor
**Completed shows (top):** [Show A, Show B, Show C]
**Abandoned shows:** [Show X, Show Y]

Step 4a: Check watchlist for tracked shows

Before web research, read /workspace/group/watchlist.json. For notified: false entries, search for release status:

• Released → include as top recommendation, note watchlist origin
• Not yet → mention briefly as "coming soon" at end

Step 4: Check new releases (web search required)

Training cutoff is stale; always search. Derive queries from Step 3's taste profile — top genres and themes from completed/high-rated shows. Do NOT hardcode genre names.

Cross-reference against all three data files; if present in any, he's seen it. Flag started-but-not-in-history shows as "new to you."

Quality filter (mandatory)

Use thresholds from Step 3. If sparse, fall back to IMDB ≥ 7.5 / RT ≥ 75%. Search for ratings before recommending if unavailable. Always include ratings.

Step 5: Generate recommendations

Prioritize:

1. New seasons of completed shows (not yet in history)
2. Shows similar to his top-completed (genre/vibe/style from Step 3)
3. Patterns from data (e.g., non-English content rates high → lean in)

Avoid: Step 3 abandoned/low-rated genres, previously abandoned shows, shows below quality threshold.

Targeted pitch format (Telegram HTML, no Markdown):

<b>[Show Name]</b> ([Year], [Seasons]) — IMDB X.X | RT XX%
[1-2 sentence targeted pitch: connect to something he already loves]
[Where to watch] | [Status: ongoing/finished]

Max 3–5 recommendations.

Step 6: Save announced/upcoming shows to watchlist

For announced-but-not-released shows (or just-announced new seasons) matching taste, add to /workspace/group/watchlist.json under tracking if not present:

{
  "title": "Show Name",
  "platform": "Platform",
  "expected": "YYYY or YYYY-QN",
  "reason": "Why it matches Baruch's taste",
  "added": "YYYY-MM-DD",
  "notified": false
}

Read existing watchlist.json first, merge, write back. No duplicates.