--- name: setup description: "Initialize AK-Threads-Booster: import historical posts, normalize them into the tracker schema, auto-generate a personalized style guide, and build a concept library. Run on first use or whenever the user wants to backfill account history." version: "2.0.0" allowed-tools: Read, Write, Edit, Bash, Glob, Grep, WebFetch --- # AK-Threads-Booster Initialization Module (M1 + M2 + M3) You are the initialization guide for the AK-Threads-Booster system. Help the user import account history, normalize it into a stable tracker, generate a style guide, and build a concept library. --- ## Principles & Knowledge Load `knowledge/_shared/principles.md` before running. Follow discovery order in `knowledge/_shared/discovery.md`. For `/setup` specifically: - Always load `data-confidence.md` (to report the dataset gate in the completion report) - Load `psychology.md` when generating `style_guide.md` (Step 3) - Load `ai-detection.md` only if the user asks for a first-pass AI-tone survey during setup Skill-specific addendum: prefer a stable tracker schema over ad-hoc one-off parsing. --- ## Automation Scripts The `scripts/` directory is a sibling of `skills/`. Use Glob to locate: - Glob `**/scripts/fetch_threads.py` — fetch posts via Meta Threads API - Glob `**/scripts/parse_export.py` — parse Meta account data export - Glob `**/scripts/render_companions.py` — render tracker into human-readable markdown - Glob `**/scripts/build_compiled_memory.py` — build low-token compiled memory under `compiled/` Python 3.9+ and the `requests` package are required for the API path. --- ## Execution Flow ### Step 1: Choose Data Import Path **Before presenting options**, Glob for `threads_daily_tracker.json` in the working directory. If one exists, run the Path E detection heuristics first — an existing legacy file means migration, not import. Only offer Paths A–D when no tracker is present or the existing file is already v1-schema. Paths: - **Paths A-D** — full flow in `references/import-paths.md`: A Meta Threads API (recommended), B Meta account data export, C existing data provided directly, D browser-driven profile scrape via `/refresh`. - **Path E** — legacy tracker migration. Full detection heuristics and E.1–E.6 steps (backup, field transform, missing-text handling, companion-markdown enrichment, validate, continue) in `references/migration.md`. After migration, continue to Step 3 + Step 4 using the migrated tracker. ### Step 2: Normalize into the Tracker Schema Regardless of import path, the result must be a valid `threads_daily_tracker.json` that matches the v1 schema in `references/tracker-schema.md` — including `schema_version: 1`, the full post-entry shape, and the required-vs-optional field split (required core: `id`, `text`, `created_at`, `metrics`, `comments`, `content_type`, `topics`). Template reference: Glob `**/templates/tracker-template.json`. After import, read the file, verify it is structurally valid, and report the number of imported posts. ### Step 3: Auto-Generate Style Guide (M2) Follow `references/generation-steps.md` Step 3. Analyze catchphrases, hook types and performance, pronoun density, ending patterns, register, paragraph structure, word-count distribution, content-type mix, emotional arcs, share drivers, topic clusters, freshness budget, and posting-time windows. **Describe what the user's style is, not what it should be** — high-performing patterns are annotated, not turned into commands. Template reference: Glob `**/templates/style-guide-template.md`. ### Step 4: Build Concept Library (M3) Follow `references/generation-steps.md` Step 4. Auto-extract explained concepts, used analogies, repeated concept clusters, and concepts only lightly explained (candidates for deeper treatment later) into `concept_library.md`. Template reference: Glob `**/templates/concept-library-template.md`. ### Step 4.5: Generate Human-Readable Companion Files Follow `references/generation-steps.md` Step 4.5. Default: shell out to `scripts/render_companions.py` with `--lang zh` (or `--lang en` if existing companions use English names — the script auto-detects). Produces `posts_by_date.md`, `posts_by_topic.md`, `comments.md` (or their Chinese-named equivalents). Fallback to inline rendering only when the script is genuinely missing. ### Step 4.6: Generate Low-Token Compiled Memory Run `scripts/build_compiled_memory.py --tracker ./threads_daily_tracker.json` after the tracker and companion files exist. This produces `compiled/account_wiki.md`, `compiled/account_state.md`, `compiled/personal_signal_memory.md`, `compiled/next_move_queue.md`, `compiled/post_feature_index.jsonl`, `compiled/cluster_wiki.json`, `compiled/exemplar_bank.md`, and `compiled/recent_window.md`. Compiled memory is a derived runtime cache, not a new source of truth. If the script is missing or fails, setup still succeeds; report that downstream skills will use tracker-only fallback until compiled memory is built. ### Step 5: Completion Report Report: 1. How many posts were imported. 2. Which import path was used. 3. 2–3 strongest style findings. 4. How many concepts were indexed. 5. Whether the tracker is full-data or partial-data. 6. That `/analyze`, `/predict`, and `/review` can already run, even if some enriched fields are still null. 7. Whether compiled memory was built successfully or tracker-only fallback is active. 8. Proactively ask whether the user wants to enable weekly GitHub update checks for AK-Threads-Booster. Explain that it is opt-in, fast-forward only, and stops instead of overwriting local changes. If the user says yes, route to `skills/update/SKILL.md` to install the automation. If post count is below 20, say the historical base is still limited. If the user has API access, tell them they can later run `scripts/update_snapshots.py` on a schedule to keep metrics snapshots current. Regardless of API access, tell them they can run `scripts/update_topic_freshness.py` to build semantic clusters and estimate topic freshness / fatigue from account history. If they do not have API access, rely on `/review` checkpoints plus `scripts/update_topic_freshness.py`. --- ## Handling Insufficient Data Use the shared rubric at `knowledge/data-confidence.md` (Glob `**/knowledge/data-confidence.md`). Report the dataset-level gate in the completion report so the user knows whether downstream skills will run in Directional / Weak / Usable / Strong / Deep mode. --- ## Output File Checklist After setup, the user's working directory should contain: 1. `threads_daily_tracker.json` — canonical data (machine-readable) 2. `style_guide.md` 3. `concept_library.md` 4. `posts_by_date.md` (or `歷史貼文-按時間排序.md`) — human-readable post archive 5. `posts_by_topic.md` (or `歷史貼文-按主題分類.md`) — topic-grouped index 6. `comments.md` (or `留言記錄.md`) — flat comment log 7. `compiled/` — low-token runtime cache generated from the tracker