analyze
Decision-first analysis for a finished Threads post: style matching, psychology analysis, algorithm alignment, upside drivers, suppression risks, and AI-tone…
Launch or prepare the optional local visual panel for AK-Threads-Booster. Use when the user asks for a dashboard, visual panel, local UI, data cockpit, or quick way to view tracker/compiled data.
$ npx -y skills add akseolabs-seo/AK-Threads-booster --skill panel --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/panelContext preview
The summary Claude sees to decide when to auto-load this skill.
Launch or prepare the optional local visual panel for AK-Threads-Booster. Use when the user asks for a dashboard, visual panel, local UI, data cockpit, or quick way to view tracker/compiled data.
name: panel description: "Launch or prepare the optional local visual panel for AK-Threads-Booster. Use when the user asks for a dashboard, visual panel, local UI, data cockpit, or quick way to view tracker/compiled data." version: "2.0.0" allowed-tools: Read, Bash, Glob
This module is the optional zero-token UI layer. It helps users inspect their tracker and compiled memory before asking the agent for deeper analysis.
Use this module when the user asks to:
Do not run `/analyze`, `/topics`, `/draft`, `/predict`, or `/review` unless the user asks for AI interpretation after viewing data.
Read these small files only:
You do not need the runtime budget prompt because opening the panel itself uses no model tokens beyond the current conversation.
Preferred command from the workspace root:
python scripts/panel_server.py --open
If the user's tracker and companion files live outside the skill folder, pass that folder as `--data-root`:
python scripts/panel_server.py --data-root "<user data folder>" --open
If browser opening is unavailable, run:
python scripts/panel_server.py
Then give the user the printed local URL.
The server searches `--data-root` recursively for:
Every user should see useful panel data in this order:
1. If `threads_daily_tracker.json` exists, the panel computes core analysis from the tracker alone: totals, median views, recent average, P90 threshold, performance distribution, time slots, content types, topic ranking, top posts, low performers, and conversation signals. 2. If companion files exist, the panel adds readable archives: posts by date, posts by topic, comments, brand voice, and style guide. 3. If compiled memory exists, the panel adds next-move and account-state blocks. 4. If optional files are missing, the panel still loads and shows source/companion coverage so the user knows which layers are present. 5. If the user clicks rebuild, the local server runs `scripts/build_compiled_memory.py` and writes `compiled/` beside the discovered tracker.
Do not promise that every optional block will be populated for every user. Promise that the tracker-only analysis layer will appear whenever a valid tracker exists.
If Python is unavailable, tell the user to open:
panel/index.html
Folder access may require a Chromium browser. File import still works without folder access.
Keep the response short:
1. Confirm the panel is local-only and zero-token at rest. 2. Give the local URL or file path. 3. Mention that AI analysis starts only when they ask for it.
AK-Threads-Booster 是這個 skill 的內部代號與安裝 id。 AK-Threads-Booster 是一套給 Threads 創作者用的 AI skill 系統。 它不是要幫你亂寫一堆貼文,而是幫你把「選題、起草、分析、預測、復盤」變成一套有資料依據的工作流,讓你更容易發出值得被分享、收藏、討論的內容。 如果你平常的痛點是這些: 不知道下一篇到底該寫什麼 有很多題目,但分不出哪個更值得先發 文章不是寫不好,只是常常撞題、老梗、沒新鮮度 想讓內容更像自己,不想一看就很 AI
Decision-first analysis for a finished Threads post: style matching, psychology analysis, algorithm alignment, upside drivers, suppression risks, and AI-tone…
Self-contained compound loop: read threads_skill_learnings.log, cluster the misses, propose concrete sub-skill rule edits, and apply them with the user's…
Estimate likely 24-hour post performance from the user's historical data. Use after the user writes a post and wants a range estimate, upside view, or…
Refresh threads_daily_tracker.json. Prefer the Threads API when available; fall back to authenticated browser profile scraping when API access is not…
Post-publish feedback loop: collect actual metrics, compare against predictions, update the tracker, refresh style conclusions carefully, and learn from…
Initialize AK-Threads-Booster: import historical posts, normalize them into the tracker schema, auto-generate a personalized style guide, and build a concept…