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promo-scout

以驗證優先的提升情報工具,用於任何儲存庫或組織。即時抓取 GitHub 狀態,透過 sshx 多觀點共識分析,產出證據支持的提升看板,顯示真正可推廣的項目、彼此關聯以及無法驗證的部分。

promo-scout 是什麼?

promo-scout 是一個以驗證為核心的提升情報工具,專為 AI 智能體或操作者設計,可用於分析任何 GitHub 儲存庫或組織。它與常見的「炒作蒐集器」截然相反:所有項目都必須有即時 GitHub 狀態作為證據,且最終判斷是透過 sshx 多重隔離觀點共識與真值表達成,而非依賴單一模型的推測。使用者需明確指定作用範圍(組織或特定儲存庫),工具會先執行 fetcher 擷取 README、近期提交、發行版、開放 PR 與議題等資料,然後交由 sshx 從「可推廣性」、「跨儲存庫連結」與「質疑/過度宣稱獵手」等不同視角進行共識分析,最後產出分成「目前可推廣」、「關聯」與「已剔除(無法驗證)」三個區塊的看板。此工具特別適合需要「哪些東西真正值得推廣」、「不同專案之間如何連結」以及「哪些誘人宣稱無法被證實」的情境。它支援 GitHub CLI 認證,並可選擇納入私有儲存庫(需使用者明確同意),產出的看板強制包含已剔除項目,以確保可信度。

相容平台~Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/ChronoAIProject/nyx-skills/tree/HEAD/promo-scout

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

promo-scout 是做什麼的?

通用、驗證優先的提升情報工具。它與炒作蒐集器完全相反:除非即時 GitHub 狀態支持,否則沒有任何項目能進入看板;而且判斷是透過 sshx 共識(隔離觀點 + 真值表)達成,而非單一模型的片面之詞。

需要:已透過 gh auth login 或 GH_TOKEN 認證的 gh,以及呼叫方可用的 sshx 技能。

使用時機

當智能體或操作者想要針對某個目標儲存庫或組織了解:目前真正值得推廣的項目是什麼、不同儲存庫之間如何連結,以及哪些誘人宣稱是無法驗證的。

步驟 0 — 與使用者確認明確範圍

沒有全域帳號的預設值。要求使用者指定:

  • 整個組織(預設僅限公開儲存庫),
  • 一個或多個特定儲存庫(owner/repo),或
  • 混合清單。

同時確認組織目標的 REPO_LIMIT(預設為最近活躍的 12 個公開儲存庫)。如需收集私有儲存庫,必須由使用者明確同意後加上 --include-private;未經使用者確認不得使用該旗標。

步驟 1 — 即時狀態擷取(確定性;可從任何位置執行)

使用明確目標執行內建擷取器:

python3 scripts/promo_scout_fetch.py <org | owner/repo> [more...]   # 公開/預設範圍
python3 scripts/promo_scout_fetch.py --include-private <org | owner/repo> [more...]   # 僅在使用者明確確認後

它會寫入 /tmp/promo_scout_bundle.json:包含每個儲存庫的 README 標題、近期提交、發行版、開放 PR、近期議題。這是共識步驟唯一可使用的證據。

步驟 2 — 對資料包執行真正的 sshx 共識

呼叫 sshx 技能(工作者委派、隔離觀點、codex-cli 工作者),以該資料包為固定輸入,做出以下決策:

僅根據此即時 GitHub 狀態資料包,產生三個清單。(1) 可推廣:目前真正值得推廣的項目——每項都必須引用資料包中的具體證據(發行版標籤、有日期的提交、README 中的功能)。(2) 關聯:跨儲存庫連結,其中一個儲存庫的工作與另一個儲存庫的工作相互組成,每項都由資料包中的真實訊號支持(共享 PR、依賴關係提及、互補功能)。(3) 已剔除:資料包中不支持的誘人宣稱,並說明缺少的證據。不得過度宣稱;如果證據不足,項目就歸入已剔除,而非可推廣。

使用不同的 sshx 思考觀點,例如:可推廣性觀點、跨儲存庫連結觀點,以及質疑/過度宣稱獵手觀點。根據 sshx 真值表要求收斂;當工作者無法建立某項宣稱時,尊重「棄權」。

步驟 3 — 輸出看板

將共識結果寫入看板(Markdown + 可選 JSON):## 目前可推廣、## 關聯、## 已剔除(無法驗證)。每個可推廣/關聯項目均內嵌其資料包證據。已剔除清單是強制性的——這是看板可信度的來源。

護欄

  • 沒有證據就不放行。即時資料包是唯一來源;不得使用外部記憶或誇大數字。
  • 關聯與綜合分析必須從即時狀態的共識中浮現——不得注入預先寫好的敘述。
  • 誠實為本:無法驗證 → 已剔除;當無法達成共識時,報告「棄權」,而非猜測。

Individual skills in this repo

This repo contains 9 individual skills — each has its own dedicated page.

ChronoAIProject/aevatar-attachment-probe

Boundary test for inbound Lark attachments — the user sends an image or file alongside this command and the bot reports exactly what it can observe on the chat path (text only, attachment refs, or nothing), without guessing. Detects when the platform wires chat-path attachment visibility so dependent skills can be upgraded.

ChronoAIProject/aevatar-vision-probe

Verify the read-image (vision) path end to end — the user sends an image plus the trigger, a workflow vision step describes it, and the run output proves the model actually saw the image (a real description, not "no image received"). Run it before any image-dependent workflow (petty-cash receipt, invoice OCR).

ChronoAIProject/codex-formal-answer

Use when answering mathematical, conceptual, philosophical, or metaphysical questions through repository-first scientific reasoning, a clause-complete formal bridge, retained exact Lean specializations, reusable library growth, and an ordinary conversational answer; the formal work stays internal and is shown only when the user asks for it.

ChronoAIProject/lark-im-ops

Send, reply to, react to, read, and find Lark messages and chats through the bot's NyxID-brokered im/v1 APIs — proactively post to a chat or DM, thread-reply, add or remove emoji reactions, read a single message or pull a chat's history over a time window, search or list the chats the bot is in, and download an image/file from a message. Handles the "content must be a JSON string" footgun and receive_id_type selection, and doubles as the im-scope probe — on a scope 403 it reports the exact missing permission instead of failing silently.

ChronoAIProject/nyx-skills

Method for producing short vertical videos from ChatGPT image generation through a fixed browser tab, local TTS narration, timed subtitle burn-in, cover exports, and a manifest. Use when an operator needs a reproducible image-to-short workflow with pinned session consistency, per-beat visual prompts, 1080x1920 safe-area rules, local ffmpeg/PIL assembly, and private access kept outside the skill.

ChronoAIProject/ornn-agent-manual-cli

The manual an AI agent loads to operate Ornn — the model-agnostic skill-lifecycle API (an npm-style registry + CLI for agent skills) — via the NyxID CLI (`nyxid proxy request ornn-api …`). Load and follow this skill WHENEVER the user asks to do anything with Ornn skills or skillsets. Skills: search Ornn or find a skill, pull or install a skill (or a specific version), run a skill, build and upload a skill, publish a new version, make a skill public / private / shared, run or read a security audit, deprecate or delete a version, diff two versions, check usage analytics, bind a skill to a NyxID service, link a skill to GitHub or sync from source, manage npm-style dist-tags, or transfer skill ownership. Skillsets — curated multi-skill bundles with a required master prompt: bundle skills into a set, create or publish a skillset, resolve its closure in one call, export a skillset as a Claude Code marketplace plugin, transfer skillset ownership, or diagnose why a shared skillset isn't visible (visibility derives fr

ChronoAIProject/petty-cash-approval-payload-builder

Runs the petty-cash approval flow end to end yourself — collect Lark DM/sheet/image context, extract receipt details, build the exact approval body, then SUBMIT the Lark approval instance.

ChronoAIProject/public-copy-release-guard

Portable checklist for gating public-facing copy before release. Use when captions, posts, manifests, topics, titles, or launch copy must be checked for generic AI wording, leaked internal labels, forbidden experiment or tool names, platform topic allowlists, CTA spam, and unsupported overclaims.

ChronoAIProject/research-claim-fidelity-reviewer

Severity-weighted BLOCK, WARN, OK reviewer method for technical and research posts. Use when reviewing X threads, announcements, captions, or launch copy for hook strength, thread arc, generic AI vocabulary, source fidelity, claim support, numeric-claim verification, and thread format before recommending publish.

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