巴逆逆（8zz）反指標追蹤器 — 抓取 Threads 貼文並進行台股反指標分析。Use when user says '/banini', '巴逆逆', '反指標', '冥燈' or similar.
巴逆逆（8zz）反指標追蹤器 — 抓取 Threads 貼文並進行台股反指標分析。Use when user says '/banini', '巴逆逆', '反指標', '冥燈' or similar.
npx skills add https://github.com/KerberosClaw/kc_ai_skills --skill banini이 명령을 Claude Code에 복사하여 붙여넣어 스킬을 설치하세요
Use when the user wants to build a consistent-identity LoRA for an original character — defining the character, generating a face/body-consistent multi-angle dataset (via the gpt-image-gen skill for codex image generation), captioning it, doing base-specific homework, training on a chosen base (Pony / Z-Image / others) on a local GPU, and producing a usable LoRA. This skill ORCHESTRATES the end-to-end pipeline and gates every expensive/irreversible step; it delegates actual image generation to gpt-image-gen and never improvises training settings from memory.
Use when the user asks to generate an image via GPT/Codex (e.g. 「叫 gpt 生圖」「幫我用 gpt 生圖」「gpt 畫一個 X」). The skill drafts a Chinese + English prompt pair, iterates with the user until they explicitly approve, then dispatches Codex CLI ($imagegen skill, codex built-in image_gen) in the background, monitors progress, converts the result to a jpg in the current working directory, and writes a sidecar prompt log. Does text-to-image AND img2img — drop a reference image (on-disk file) and it runs Codex `-i` to lock a face/character across scenes.
Convert a Markdown report into a presentation-quality .pptx via interactive design decisions + hand-coded build script. Use when user says '/md2ppt', 'markdown to pptx', 'make slides from this md', '簡報', '做投影片', or similar. Optional self-check loop: if LibreOffice available, renders pptx → PNG per slide and checks for overflow / tiny font / emoji / misaligned content before user review. NOT a generic auto-converter — drives a per-slide layout dialogue then emits a reusable build script. Brand-template integration is supported as ad-hoc primitives (helpers exist) but is intentionally not a prescribed workflow — every brand template differs and is best handled by direct LLM-user dialogue.
Use when the user attended one or more sessions of a conference (with audio recordings + slide photos) and needs help building (1) faithful per-session reconstructions in markdown (slide visuals + speaker transcript with Whisper hallucination annotations), and (2) a downstream report deliverable whose scope and format are decided interactively with the user. Pipeline phase (raw → mlx_whisper Chinese SRT → per-slide multimodal reconstruction → official agenda cross-check via Playwright if available) is deterministic. Report phase is interactive — always quiz user on scope (single-session / single-day / multi-day synthesis), format (existing template / free-form fallback), recipient (formal / informal), and any business workstream mapping before drafting.
Use when the user wants to lint a repo following the Karpathy LLM Wiki pattern (raw/ + wiki/ + SCHEMA.md / index.md / log.md). The skill detects path, scans wiki pages + schema layer, reports frontmatter gaps, source traceability breaks, stale claims, orphan pages, missing topics, data gaps, cross-page contradictions, and SCHEMA-vs-reality drift. Read-only — never auto-fixes, never writes to log.md, never touches the network.
Use when the user wants to lint a Claude Code memory directory (~/.claude/memory or custom path) for index inconsistency, stale project state, duplicate / conflicting feedback rules, naming convention violations, frontmatter gaps, and oversized files. The skill detects path, scans root-level *.md, reports findings by severity. Read-only — never auto-fixes, never deletes, never merges.
| name | banini |
| description | 巴逆逆（8zz）反指標追蹤器 — 抓取 Threads 貼文並進行台股反指標分析。Use when user says '/banini', '巴逆逆', '反指標', '冥燈' or similar. |
| version | 0.1.0 |
| headless-prompt | Run ~/.claude/skills/banini/.venv/bin/python ~/.claude/skills/banini/scripts/scrape_threads.py banini31 5, parse the JSON output, then perform 反指標 contrarian analysis. Rules: 買入=可能跌, 停損=可能反彈, 被套=續跌, 看多=可能跌, 看空=可能漲, 買put=可能飆漲. Output a CONCISE report in Traditional Chinese, optimized for Telegram reading. Format: 1) 一句話總結（今日冥燈狀態） 2) 冥燈指數 N/10 3) 標的表格（標的 | 她的動作 | 反指標方向 | 信心），每列一行，不要用 markdown table 4) 一段 2-3 句的綜合判斷 5) 分隔線後，附上 3-5 則關鍵原文摘錄（只取投資相關的），格式：「原文節錄」— 讚數 End with 僅供娛樂參考，不構成投資建議. Keep total output under 3000 chars. |
追蹤「反指標女神」巴逆逆（8zz）的 Threads 貼文，直接進行反指標分析。
/banini [username]
banini31執行前確認 venv 與 Playwright 環境：
${CLAUDE_SKILL_DIR}/.venv/bin/python -c "from playwright.async_api import async_playwright; from nested_lookup import nested_lookup; from parsel import Selector; print('OK')"
缺少 venv 或套件時顯示安裝指令並停止（首次安裝會建立隔離 venv，避免踩到 PEP 668）：
python3 -m venv ${CLAUDE_SKILL_DIR}/.venv
${CLAUDE_SKILL_DIR}/.venv/bin/pip install playwright parsel nested-lookup jmespath
${CLAUDE_SKILL_DIR}/.venv/bin/python -m playwright install chromium
用本 skill 自帶的爬蟲腳本抓取貼文：
${CLAUDE_SKILL_DIR}/.venv/bin/python ${CLAUDE_SKILL_DIR}/scripts/scrape_threads.py banini31 5
5 是捲動次數，通常能抓 10-20 篇拿到 JSON 後，讀取並整理為以下格式供分析用：
貼文 N【今天/昨天/MM/DD】（YYYY/MM/DD HH:MM）
{貼文文字}
讚: {likes} | 回覆: {reply_count}
taken_at 為 Unix timestamp，轉換為台北時間（UTC+8）你（Claude）就是分析引擎。讀完整理好的貼文後，依以下邏輯進行分析。
| 她的狀態 | 反指標解讀 | 原因 |
|---|---|---|
| 買入/加碼 | 該標的可能下跌 | 她買什麼跌什麼 |
| 持有中/被套（還沒賣） | 該標的可能繼續跌 | 她還沒認輸，底部還沒到 |
| 停損/賣出 | 該標的可能反彈上漲 | 她認輸出場 = 底部訊號 |
| 看多/喊買 | 該標的可能下跌 | 她看好的通常會跌 |
| 看空/喊賣 | 該標的可能上漲 | 她看衰的通常會漲 |
| 空單/買 put | 該標的可能飆漲 | 她空什麼就漲什麼 |
使用以下格式輸出：
## 巴逆逆反指標分析報告
**分析時間：** {現在時間}（台北時間）
**資料來源：** Threads @{username}，共 {N} 篇貼文（{最舊日期} ~ {最新日期}）
---
### 提及標的
（對每個辨識出的標的，輸出以下格式）
#### {↑↓→} {標的名稱}（{類型}）— `{日期 HH:MM}`
> 「{原文摘錄}」
- **她的操作：** {操作描述}
- **反指標：** {反轉推導}
- **信心：** {高/中/低} — {原因}
- **相關標的：** {如適用}
---
### 連鎖推導
（2-4 點，用 1. 2. 3. 編號）
### 建議方向
（1-2 段文字）
### 冥燈指數：{N}/10
{一句話解釋}
---
*僅供娛樂參考，不構成投資建議*
↑ = 反指標看漲、↓ = 反指標看跌、→ = 方向不明本 skill 支援透過 skill-cron 進行排程執行與 Telegram 推播。
SKILL.md frontmatter 已包含 headless-prompt，可直接用 skill-cron 註冊：
/skill-cron add banini "7,37 9-12 * * 1-5" 盤中
/skill-cron add banini "3 23 * * *" 盤後
/banini，非互動排程用 skill-cron（claude -p 不支援 /skill 語法）