| 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:
- How many posts were imported.
- Which import path was used.
- 2–3 strongest style findings.
- How many concepts were indexed.
- Whether the tracker is full-data or partial-data.
- That
/analyze, /predict, and /review can already run, even if some enriched fields are still null.
- Whether compiled memory was built successfully or tracker-only fallback is active.
- 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:
threads_daily_tracker.json — canonical data (machine-readable)style_guide.mdconcept_library.mdposts_by_date.md (or 歷史貼文-按時間排序.md) — human-readable post archiveposts_by_topic.md (or 歷史貼文-按主題分類.md) — topic-grouped indexcomments.md (or 留言記錄.md) — flat comment logcompiled/ — low-token runtime cache generated from the tracker
