skill Skill
巴逆逆反指標分析。觸發時機：使用者要求追蹤巴逆逆、分析反指標、抓取社群貼文並推送 Telegram 時。
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Why use this skill
skill is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around backend skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Claude Code Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in cablate/banini-tracker, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
--- name: banini-tracker description: | 巴逆逆反指標分析。觸發時機：使用者要求追蹤巴逆逆、分析反指標、抓取社群貼文並推送 Telegram 時。 能力範圍：透過 CLI 抓取 Facebook 貼文、反指標邏輯分析、連鎖效應推導、Telegram 推送。 目標：由 Claude 作為分析引擎，產出直白中文的反指標分析報告。 --- # banini-tracker — 巴逆逆反指標分析 追蹤「股海冥燈」巴逆逆（8zz）的 Facebook 貼文，由你（Claude）進行反指標分析，推送結果到 Telegram。 ## 前置條件 ```bash # 首次使用：初始化設定 npx @cablate/banini-tracker init --apify-token <TOKEN> --tg-bot-token <TOKEN> --tg-channel-id <ID> # 確認設定 npx @cablate/banini-tracker config ``` ## 工作流程 ### Step 1：抓取貼文 ```bash npx @cablate/banini-tracker fetch -s fb -n 3 --mark-seen ``` 輸出是 JSON 陣列，每篇貼文包含： - `id` / `source` - `text`（貼文內容） - `ocrText`（圖片 OCR 文字，可能包含下單截圖） - `timestamp` / `url` / `likeCount` - `mediaType` / `mediaUrl` `--mark-seen` 會自動記錄已讀，下次不重複抓。 ### Step 2：你來分析 讀取 Step 1 的 JSON 後，進行反指標分析。分析要點： **核心邏輯**（方向完全相反，不要搞混）： | 她的狀態 | 反指標解讀 | |---------|-----------| | 買入/加碼 | 該標的可能下跌 | | 被套（還沒賣） | 可能繼續跌（她還沒認輸） | | 停損/賣出 | 可能反彈上漲（她認輸 = 底部訊號） | | 看多/喊買 | 該標的可能下跌 | | 看空/喊賣 | 該標的可能上漲 | **分析原則**： - 只根據貼文明確提到的操作判斷，不要腦補 - 停損 = 她之前買了（做多），現在賣掉認賠。不是「放空」 - 標的用正式名稱（信驊、鈦昇），不用她的暱稱（王、渣男） - 當天貼文最重要，注意時序（她的想法可能幾小時內改變） - 語氣越篤定/興奮 → 反指標信號越強 - 善用 WebSearch 查詢標的最新走勢，豐富分析 **連鎖效應推導**： - 她買油正二被套 → 油價可能繼續跌 → 原物料成本降 → 製造業利多 - 她停損鈦昇 → 鈦昇可能反彈 → IC 設計族群連動上漲 - 她停損賣出油正二 → 油價可能反彈 → 通膨壓力回來 ### Step 3：推送 Telegram 將分析結果寫入暫存檔再推送（多行訊息用 `-m` 會被 shell 截斷，務必用 `-f`）： ```bash # 寫入暫存檔後推送（推薦） npx @cablate/banini-tracker push -f /tmp/report.txt # 短訊息可用 -m npx @cablate/banini-tracker push -m "短訊息" # 純文字（不解析 HTML） npx @cablate/banini-tracker push -f /tmp/report.txt --parse-mode none ``` ## 其他指令 ```bash # 去重管理 npx @cablate/banini-trac ...