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reality-show

Keep serialized build-in-public content continuous: read one canon file of season, arcs, beats and loops and say which narrative beat to advance next, so episodes chain into a series. Pairs with the episode adapter, which does the writing. Triggers: "/reality-show", "season state", "what arcs are open".

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REALITY-SHOW v2 — the season continuity layer on top of a SINGLE CANON

GOAL: make the stream of episodes read like a SERIES. /episode writes one episode with dramaturgy (palette [[style-reality-show]]); THIS skill remembers the SEASON and suggests the next narrative move.

SOURCE OF TRUTH = the canon: $OBSIDIAN_VAULT/04-Projects\show-canon\ (season-01.md · arcs\ · beats\ · loops\ · rules; the map is _SHOW-CANON.md). ⛔ season-state.json is FROZEN as of 2026-07-10 (_imports\content-factory\season-state.json + season_state.py) — it was the third diverging spine after THREADS/SHOW-STATE; decision decision-single-canon-story-state. Do NOT read it as truth, do NOT update it.

MODES

status (default, read-only, 0 LLM tokens)

Read season-01.md (the season question, active arcs, open loops, the board) + the headers of arcs\*.md (status, current_state) + loops\*.md (open?) → a one-line digest for the owner: the season + arcs with their stakes + dangling cliffhanger loops + story_day. Nothing is changed.

next — propose the beat for the next episode (authorial model, cheap)

  1. Read: season-01.md + open loops + fresh beats in beats\ (happened, latest by occurred_on) + the 📝 inbox _imports\content-factory\triage\posts.md (fresh post material). Do NOT read the whole corpus.
  2. Judgement (top-tier model): which OPEN arc today's material should advance; which loop to close or sharpen; which motif to bring back (running gag). It must connect to a REAL beat — never invent events. beat_kind hints at the dramaturgy: 🌀 twist / 💥 fail = cliffhanger; 🏆 milestone = payoff; 📡 external = fuel for the season's intrigue.
  3. Output 1-2 options: "arc X → sharpen (loop '…'), bring back the motif '…', anchored on beat-YYYY-MM-DD-slug". After that /episode writes the episode.

intake — process the beats inbox (sensor candidates → canon)

At night this is done by the routine canon-writer-nightly (01:00); here it is the same thing on demand. python $IMPORTS_ROOT/content-factory\canon_intake.py list → judge fitness and the ARC (only from the live dictionary!) → ... accept <beat_id> --arcs arc-a,arc-b or ... reject <beat_id> --why "...". The script itself: validates arc_id against the arc files, sets the derived story_day, moves it into beats\, appends the back-link into the arc, cleans _README. Never move beats by hand. canon_intake.py verify — the detector of half-finished intake (a beat sits in beats\ while the arc does not reference it: a break, or a race between two writers). Runs at the end of the nightly pass; exit 1 = fix it.

board — the season board (derived, not handwritten)

python $IMPORTS_ROOT/content-factory\show_canon_sync.py board (before→after) / board --apply (write it). season-01.active_arcs / open_loops are rebuilt from the status: fields in the arc/loop files. Rule: the board is never edited by hand — otherwise the copy falls behind the truth (the BOARD-DRIFT incident 07-27: an arc lived in the files for 16 days while the board did not show it). show_canon_sync.py arcs prints the live arc_id dictionary — the contract for the sensors.

Updating the state = EDITING THE CANON (with explicit actions, not silently)

Everything that used to be written into json is now an edit of canon cards, from the templates:

  • a new line → arcs\arc-<slug>.md (modelled on the existing ones); escalation/closure → edit the arc's current_state/status;
  • open/close a cliffhanger → loops\loop-<slug>.md (status: open/closed + current_best_answer);
  • link an episode → append consequences to the anchoring beat ("episode <slug> published") and to the arc's beats[]. After the edits: python $IMPORTS_ROOT/content-factory\canon_render.py (the GitHub registries update themselves; the linter will complain about broken fields).

recap --arc <id> — "previously on"

The arc's beats (beats[] from the arc card, by occurred_on) + its open loops → a 1-2 line recap for the new episode.

check — the weekly health scorecard of the series

A deterministic engine (0 tokens): python $IMPORTS_ROOT/content-factory\show_canon_check.py check (+ status for context). Axes: (1) Continuity — the season question is set + ≥3 beats happened; (2) Change — the share of active arcs that have beats; (3) Loops — more than 4 open = a jam, more than 2 in one arc = overload; (4) Trust (manual, receipts; the script reports the share of fresh beats carrying evidence_refs). Plus board drift (season-01.open_loops vs the real loop files) and a jammed beats inbox. Exit 0=OK · 1=FLAGS (a diagnosis) · 2=could not measure. The judgement verdict belongs to the top model / the owner. Weekly (routine reality-show-health-weekly) or on demand.

HOW IT WIRES INTO THE PIPELINE

An event → a BEAT into the canon (template beats\_TEMPLATE-beat.md) → /reality-show next (pick the move) → /episode writes the episode → publication through the gates → /reality-show records the consequences IN THE CANON → canon_render.py refreshes the public registries.

BOUNDARIES

  • Read-only by default (status/recap/next/check); canon edits happen through explicit, announced actions.
  • The owner's voice = the top-tier model (if the session runs a weaker one, delegate the next judgement to a subagent with model:'opus').
  • Draft-first outbound: /episode publishes on a "+". Tier-2 outbound → pause and ask.
  • Privacy: secrets OUT; respect the reveal axis (never burn live_hold/spoiler_until in outbound recaps). Serialization comes from REAL beats, never from invention.
  • AK-47: one SKILL.md, no separate store any more (the canon IS the store). Canon laws: decision-single-canon-story-state + _SHOW-CANON.md.
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Like this skill? It is part of second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.

Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).

Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_

Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.

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