Instrument Data To Allotrope
aws-samples/amazon-bedrock-agents-healthcare-lifesciences
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV.
亚马逊 Sif 关键词情报侦察系统（Skill 1）。用户输入 ASIN，获取 Sif 三张报表（关键词调研/反查流量词/查广告词）， Python 完成分层、机会评级、缺口分析，三表交叉输出 PD 主攻词单。支持「用户手动导出」与「AI 浏览器导出」两种方式。
Install Sif Keyword Scout
$ npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install binggandata/bggg-skills sif-keyword-scout --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/binggandata/bggg-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sif-keyword-scout .claude/skills/sif-keyword-scout && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sif-keyword-scout" agent skill from https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scout into .claude/skills/sif-keyword-scout/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sif-keyword-scout", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scoutType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install binggandata/bggg-skills sif-keyword-scout --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/binggandata/bggg-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/sif-keyword-scout .agents/skills/sif-keyword-scout && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sif-keyword-scout" agent skill from https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scout into .agents/skills/sif-keyword-scout/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sif-keyword-scout", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install binggandata/bggg-skills sif-keyword-scout --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/binggandata/bggg-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/sif-keyword-scout .cursor/skills/sif-keyword-scout && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sif-keyword-scout" agent skill from https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scout into .cursor/skills/sif-keyword-scout/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sif-keyword-scout", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/binggandata/bggg-skills.git --path sif-keyword-scout--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install binggandata/bggg-skills sif-keyword-scout --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/binggandata/bggg-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/sif-keyword-scout .gemini/skills/sif-keyword-scout && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sif-keyword-scout" agent skill from https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scout into .gemini/skills/sif-keyword-scout/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sif-keyword-scout", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install binggandata/bggg-skills sif-keyword-scoutInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/binggandata/bggg-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/sif-keyword-scout .github/skills/sif-keyword-scout && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sif-keyword-scout" agent skill from https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scout into .github/skills/sif-keyword-scout/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sif-keyword-scout", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install binggandata/bggg-skills sif-keyword-scout --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/binggandata/bggg-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/sif-keyword-scout .opencode/skills/sif-keyword-scout && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sif-keyword-scout" agent skill from https://github.com/binggandata/bggg-skills/tree/main/sif-keyword-scout into .opencode/skills/sif-keyword-scout/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sif-keyword-scout", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sif-keyword-scout- GitHub stars
- 605
- Token cost
- ~2.9k tokens
- SKILL.md length
- 599 words
- Files
- 25 (incl. scripts, references)
- Skills in repo
- 13
- Repo updated
- First seen
- Licence
- MIT
At a glance
亚马逊 Sif 关键词情报侦察系统（Skill 1）。用户输入 ASIN，获取 Sif 三张报表（关键词调研/反查流量词/查广告词）， Python 完成分层、机会评级、缺口分析，三表交叉输出 PD 主攻词单。支持「用户手动导出」与「AI 浏览器导出」两种方式。
- Works in 10 steps: ：工作区与路径初始化（每次运行开头） → ：跑前简报（ASIN + 参数 + 用户确认） → ：检查 ASIN 历史 → …
- Documents & Office work in your project
- SKILL.md covers Agent 角色, 人机协同原则（强制）, 依赖 and 三种输入方式（Agent 自动判断）, plus 13 more sections
- Runs Python scripts from its folder; calls python and pip
What it does
Sif Keyword Scout is an agent skill from binggandata/bggg-skills. 亚马逊 Sif 关键词情报侦察系统（Skill 1）。用户输入 ASIN，获取 Sif 三张报表（关键词调研/反查流量词/查广告词）， Python 完成分层、机会评级、缺口分析，三表交叉输出 PD 主攻词单。支持「用户手动导出」与「AI 浏览器导出」两种方式。 路径通过工作区 .sif-config.json 解析，不写死本机路径。Agent 以亚马逊关键词策略顾问角色解读结果并引导参数。 若 ASIN 有历史记录则自动触发 sif-keyword-tracker。触发：sif关键词、PD备战关键词、ASIN关键词调研、运行skill1。
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 26 other files, including scripts and reference files (for example `README.md`, `references/amazon-expert-guide.md` and `references/browser-export-sop.md`).
It sits in Documents & Office. It works with Python and Microsoft Excel. The repository describes itself as: Open-source Codex skills from BGGG. The licence is MIT.
When your agent uses it
- Documents & Office work in your project
Example prompts
- “/sif-keyword-scout”
Requirements
- Python 3
Workflow steps
10 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 1034ee5. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 9 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
pythonpip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Sif Keyword Scout loads about 2.9k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 73 tokens; SKILL.md has 599 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from binggandata/bggg-skills at commit 1034ee5, republished under its MIT licence (© binggandata). 599 words, ~2,856 tokens.
Download SKILL.mdSave it as
.claude/skills/sif-keyword-scout/SKILL.md (or your agent's skills folder). This skill also uses 24 other files; get the full folder from GitHub.- name
- sif-keyword-scout
- description
- 亚马逊 Sif 关键词情报侦察系统（Skill 1）。用户输入 ASIN，获取 Sif 三张报表（关键词调研/反查流量词/查广告词）， Python 完成分层、机会评级、缺口分析，三表交叉输出 PD 主攻词单。支持「用户手动导出」与「AI 浏览器导出」两种方式。 路径通过工作区 .sif-config.json 解析，不写死本机路径。Agent 以亚马逊关键词策略顾问角色解读结果并引导参数。 若 ASIN 有历史记录则自动触发 sif-keyword-tracker。触发：sif关键词、PD备战关键词、ASIN关键词调研、运行skill1。
Sif 关键词情报侦察（Skill 1）
Agent 角色
执行本 skill 时，同时阅读并遵循 references/amazon-expert-guide.md：
- 以亚马逊关键词/PPC 顾问身份解读结果，参数异常时主动引导用户
- 不是只跑脚本；S/A 门槛、类目描述、SS/SSS 数量异常时要说明原因并建议修正
人机协同原则（强制）
禁止「静默跑完全程」。本 skill 是顾问 + 自动化，不是批处理脚本。
| 规则 | 说明 |
|---|---|
| 跑前必聊 | 在 ingest_raw 或任何 --mode process 之前，必须与用户确认 ASIN、阶段、类目、数据来源 |
| 三处卡点 | 见下方「强制暂停点」——未获用户明确确认前，不得进入下一步 |
| 确认方式 | 用户回复「可以 / 继续 / 用自动值 / 按你说的」等明确许可；沉默或只给 ASIN 不算确认 |
| 异常先停 | sanity check 触发时，先解释原因并给出选项，等用户选后再重跑或继续 |
强制暂停点（三处）
| # | 时机 | Agent 做什么 | 用户确认什么 |
|---|---|---|---|
| ① | 表1 compute-thresholds 之后、process 之前 | 展示 S/A 门槛、竞价/集中度中位数；结合阶段 + 类目说明是否偏高/偏低 | 维持自动值，或指定 --s-threshold / --a-threshold |
| ② | 三表 + 交叉 --skip-word 完成后、写 insights_cross 之前 | 解读 SSS/SS/S 数量、表3 缺口/离群词；列出建议主攻 SS 清单与应排除词 | 确认主攻方向与是否重跑表3（改类目/--sv-threshold） |
| ③ | Skill 2 compare_versions --skip-word 完成后、写 insights_tracker 之前 | 解读新增/消失/份额变化；给出试投/暂停/调价建议 | 确认是否按建议调整投放，或仅存档观察 |
话术与解读细则 → references/amazon-expert-guide.md 各节。
依赖
| 依赖 | 说明 |
|---|---|
| Python | pip install pandas openpyxl matplotlib python-docx numpy |
| web-access | 仅方式 B 需要；第三方 eze-is/web-access（作者一泽 Eze，MIT）；路径见 references/path-setup.md |
最终产物：处理结果目录仅保留 Excel（
.xlsx）+ Word（.docx）。 图表由脚本生成 PNG 后内嵌进 Word，不保留charts/目录。 Agent 写的insights_*.md是中间稿（供 Word 渲染 AI 分析段），生成 Word 后自动删除。
三种输入方式（Agent 自动判断）
| 方式 | 用户怎么说 | Agent 做什么 |
|---|---|---|
| C 直接给路径（最快） | 发来 3 个 xlsx 路径，或一个下载文件夹 | ingest_raw.py 识别表类型 → 复制到标准目录 → 跑处理 |
| A 手动导出 | 「我自己下好了」或放到指定目录 | 发 manual-export-guide.md，或 ingest_raw --from-dir |
| B 浏览器导出 | 「你帮我下」 | web-access + browser-export-sop.md |
用户直接给文件路径时：不必再问下载方式，先 resolve_workspace → 跑前简报（Step 1）获确认 → 调 ingest_raw → 从 Step 5 继续。
# 三个路径
python "{scripts.ingest_raw}" --asin {ASIN} --output-dir "{output_dir}" --stage {阶段} --product-category "{类目}" \
--product-type {标品|非标品} \
--t1 "D:/Downloads/Sif关键词调研-....xlsx" \
--t2 "D:/Downloads/asinKeywords_....xlsx" \
--t3 "D:/Downloads/asinAdKwView_....xlsx"
# 或一个文件夹（自动识别）
python "{scripts.ingest_raw}" --asin {ASIN} --output-dir "{output_dir}" --from-dir "D:/Downloads"Step 0：工作区与路径初始化（每次运行开头）
python "{SKILL_SCOUT}/scripts/resolve_workspace.py"解析 JSON，得到 workspace、config_path、output_dir、download_dir、scripts.*、web_access 等。
| 情况 | 动作 |
|---|---|
workspace_found: false | 让用户指定工作区根目录，或复制 .sif-config.example.json → .sif-config.json |
needs_output_dir: true | 必读 first-time-setup.md，向用户询问输出根目录，禁止填他人路径 → --init-output-dir |
needs_download_dir: true | 仅方式 B：询问浏览器下载目录 → --init-download-dir（方式 A/C 可跳过） |
返回 first_time_guide | 按其中 steps 逐条引导，配置完成后再跑 ASIN |
路径是用户自己的：打包给他人时只发 .sif-config.example.json（空字段）；每人首次使用自己指定 output_dir。
详述 → references/path-setup.md · 首次对话脚本 → references/first-time-setup.md
Step 1：跑前简报（ASIN + 参数 + 用户确认）
在调用 ingest_raw 或处理脚本之前，完成以下对话并等待用户确认：
- ASIN：用户消息含 ASIN → 直接使用；否则先问
- 必问参数（不可跳过）：
1. 产品阶段：新品期 / 成长期 / 成熟期？
2. **产品类型**：标品 / 非标品？（决定默认门槛，见 `references/threshold-presets.md`）
3. 精确产品类目（影响表3 缺口词，如「不锈钢保温杯」而非笼统「水杯」）？跑前简报（用自然语言概括，勿只贴命令）：
- 本次将产出 4 组 Excel + Word（表1/2/3 + PD 主攻词单）
- 若有历史 → 额外产出词库更新报告（Skill 2）
- 数据来源：方式 A / B / C（若用户已给文件路径则说明「直接接入」）
- 预计会在 暂停点 ①② 与你确认门槛与主攻词方向
确认门：用户明确回复后，才进入 Step 2~3。
类目引导规则 → references/amazon-expert-guide.md
Step 2：检查 ASIN 历史
python "{scripts.check_history}" --asin {ASIN} --output-dir "{output_dir}"保存 has_history、prev_path 供 Step 10 使用。
Step 3：建目录 + 接入原始表
方式 C（已有文件路径）：Step 0 后直接调 ingest_raw（见上），本步合并完成。
方式 A/B（尚未有标准命名文件）：
python "{scripts.ingest_raw}" --asin {ASIN} --output-dir "{output_dir}" --stage {阶段} --product-category "{精确类目}" --product-type {标品|非标品}解析 stdout JSON：
| 字段 | 含义 |
|---|---|
date / run_id | YYYYMMDD，用于文件名后缀 |
run_path | {ASIN}/{date} |
raw_dir | 原始数据目录 |
result_dir | 处理结果目录 |
raw_files | 三张原始表标准文件名 |
datetime_display | 写入历史表的实际跑库时刻（含时分） |
目录结构：
{output_dir}/{ASIN}/{DATE}/
├── 原始数据/
│ ├── 关键词调研_{DATE}.xlsx
│ ├── 反查流量词_{DATE}.xlsx
│ └── 查广告词_{DATE}.xlsx
└── 处理结果/ ← 最终仅保留 xlsx + docx
├── {ASIN}_关键词分层分析_{DATE}.xlsx
├── {ASIN}_关键词调研分析报告_{DATE}.docx
├── {ASIN}_竞品弱点分析_{DATE}.xlsx
├── {ASIN}_竞品弱点分析报告_{DATE}.docx
├── {ASIN}_竞品缺口分析_{DATE}.xlsx
├── {ASIN}_竞品广告缺口分析报告_{DATE}.docx
├── {ASIN}_PD主攻词单_{DATE}.xlsx
└── {ASIN}_PD主攻词单报告_{DATE}.docx中间产物（stats JSON、
insights_*.md、charts/）在 Word 生成后由脚本cleanup_intermediate_files自动删除。
Step 4：获取三张原始表（仅 A/B 且 Step 3 尚未 ingest 时）
| 方式 | 动作 |
|---|---|
| C 已有路径 | 跳过，Step 3 的 ingest_raw 已完成 |
| A 手动 | 发 manual-export-guide.md；用户下完后 ingest_raw --from-dir 或 --t1/--t2/--t3 |
| B 浏览器 | web-access + browser-export-sop.md；下完后同样 ingest_raw |
处理前必读 → references/table-schema.md
Step 5：处理表1 → 生成 Word 报告
计算基准：
python "{scripts.process_table1}" --mode compute-thresholds --input "{raw_dir}/关键词调研_{DATE}.xlsx"向用户展示基准，按 amazon-expert-guide.md 话术询问是否调整 S/A 门槛。
暂停点 ①：用户确认门槛（或明确「用自动值」）后，才执行下方
process。
第一轮（Excel + stats，跳过 Word）：
python "{scripts.process_table1}" --mode process --input "{raw_dir}/关键词调研_{DATE}.xlsx" --output-dir "{result_dir}" --asin {ASIN} --date {DATE} --stage {阶段} --product-type {标品|非标品} [--s-threshold N --a-threshold N --s-conc-max 0.30] --skip-wordAgent 写 AI 分析段（必须做）：
- 读取
*_t1_stats_{DATE}.json - 以亚马逊顾问身份写
insights_t1.md（仅分析文字，格式见下方「insights 写作规范」） - 不要写图表节，图表由 Word 自动内嵌
第二轮（生成 Word，自动清理中间文件）：
python "{scripts.process_table1}" --mode process ... --insights-file "{result_dir}/insights_t1.md"产出：{ASIN}_关键词分层分析_{DATE}.xlsx + {ASIN}_关键词调研分析报告_{DATE}.docx
Show full SKILL.md (246 more words)Show less
Step 6 / 7 / 8：表2、表3、交叉分析
流程同表1：先 --skip-word → 写 insights_t2.md / insights_t3.md / insights_cross.md → 带 --insights-file 重建 Word。
# 表2
python "{scripts.process_table2}" --input "..." --output-dir "{result_dir}" --asin {ASIN} --date {DATE} --skip-word
# → 写 insights_t2.md → 去掉 --skip-word 加 --insights-file
# 表3
python "{scripts.process_table3}" --input "..." --output-dir "{result_dir}" --asin {ASIN} --date {DATE} --product-category "{精确类目}" --product-type {标品|非标品} [--sv-threshold 1000] --skip-word
# 交叉（输入必须是**处理后的**三表 Excel，不是原始数据目录）
python "{scripts.cross_analysis}" \
--t1 "{result_dir}/{ASIN}_关键词分层分析_{DATE}.xlsx" \
--t2 "{result_dir}/{ASIN}_竞品弱点分析_{DATE}.xlsx" \
--t3 "{result_dir}/{ASIN}_竞品缺口分析_{DATE}.xlsx" \
--output-dir "{result_dir}" --asin {ASIN} --date {DATE} --skip-word| 参数 | 何时调 | 说明 |
|---|---|---|
--product-type | 每次 ASIN | 默认非标品；标品/非标品预设见 threshold-presets.md |
--sv-threshold | 小类目缺口词过少 | 0=用预设（非标2000/标品3000），可降至 500–1000 |
--product-category | 每次必问 | 影响词类型判定 |
交叉 --skip-word 完成后做 sanity check（缺口词为 0、SSS 为 0 等）→ 见 amazon-expert-guide。
暂停点 ②：向用户汇报交叉结果与建议主攻 SS 清单；确认后写
insights_cross.md并生成 PD Word。若需重跑表3，在此停止并调整参数。
表2、表3 的 insights_t2/t3 可在暂停点 ② 之前并行撰写；PD 报告与 cross Word 必须在暂停点 ② 之后。
insights 写作规范（供 Word 渲染）
| 写法 | 说明 |
|---|---|
## 标题 | 分节标题，会渲染为 Word 二级标题 |
- 列表项 | 无序列表 |
**加粗** | 加粗文字 |
| 列 | 列 | | 可选，会转为 Word 表格 |
 | 禁止 — 图表由脚本插入 Word |
***文字*** | 禁止 — 会显示为乱码星号 |
# 总标题 | 禁止 — Word 已有封面标题 |
TOP5 / 逐词点评（必须）：每条 = 数据括号 + 策略句（竞争判断、阶段是否投、匹配方式、与 listing 关系）。禁止「词名：词名，搜索量…」式复读。
反例：- **sofa cover**：sofa cover，搜索量 58321，竞价 $1.04
正例：- **sofa cover**（S级，搜索量 58321，竞价 $1.04，集中度 0.22）：核心品类词，集中度低于类目中位；新品期精准小预算试投，与三座规格 listing 匹配再加码。
最终产出（处理结果目录仅保留）
{ASIN}_关键词分层分析_{DATE}.xlsx+_关键词调研分析报告_{DATE}.docx{ASIN}_竞品弱点分析_{DATE}.xlsx+_竞品弱点分析报告_{DATE}.docx{ASIN}_竞品缺口分析_{DATE}.xlsx+_竞品广告缺口分析报告_{DATE}.docx{ASIN}_PD主攻词单_{DATE}.xlsx+_PD主攻词单报告_{DATE}.docx
Step 9：更新历史（每次跑完必做，追加一行）
python "{scripts.update_history}" --asin {ASIN} --output-dir "{output_dir}" --date {DATE} --run-path "{run_path}"ASIN历史记录.xlsx → Sheet 「运行日志」：每次运行追加一行，不覆盖、不合并日期。
| 列 | 说明 |
|---|---|
| 运行时间 | YYYY-MM-DD HH:MM |
| 数据日期 | YYYYMMDD（文件夹日期） |
| 快照路径 | {ASIN}/{DATE}/{HHmm}，同日多次跑库可区分 |
旧版汇总表可用 --migrate-only 一次性迁移：
python "{scripts.update_history}" --migrate-only --output-dir "{output_dir}"Step 10：条件触发 Skill 2（1~7 天窗口对比）
当 check_history 显示该 ASIN 已有 ≥2 次运行 → 执行 Skill 2。
python "{scripts.check_history}" --asin {ASIN} --output-dir "{output_dir}" --curr-date {DATE}自动选对比基准：优先 1~7 天窗口内最近一次；同日多次则对比同日前一次。
Skill 2 两轮流程（与 Skill 1 相同）：
# 1) 对比 + stats
python "{scripts.compare_versions}" --auto --output-root "{output_dir}" --output-dir "{result_dir}" --asin {ASIN} --curr-date {DATE} --skip-word
# → 暂停点 ③：解读变化，与用户确认投放策略
# 2) Agent 写 insights_tracker.md → 重建 Word
python "{scripts.compare_versions}" --auto ... --insights-file "{result_dir}/insights_tracker.md"产出：{ASIN}_词库更新报告_{DATE}.docx（含数据表 + AI 策略段）
Step 11：汇报
汇报产出路径、SS/SSS 主攻词摘要、是否建议调整参数；首次运行说明下次同 ASIN 会自动对比。
错误处理
| 错误 | 处理 |
|---|---|
| Python 报错 | 对照 table-schema.md 检查表结构，再问用户是否重导出 |
| Sif 未登录 | 方式 B：请用户浏览器登录后继续；或改方式 A |
| 找不到下载文件 | 确认 download_dir 或改方式 A |
| 类目/阶段明显不对 | 顾问模式：暂停并引导修正参数 |
脚本清单（scripts/）
| 脚本 | 用途 |
|---|---|
resolve_workspace.py | 解析配置与全部脚本绝对路径 |
ingest_raw.py | 建目录 + 接入用户文件（任意路径/文件夹） |
check_history.py / update_history.py | 历史 ASIN 查询与更新 |
process_table1/2/3.py | 三表处理 |
cross_analysis.py | 三表交叉 → PD 主攻词单 |
run_context.py / report_utils.py | 内部模块，不直接调用 |
batch_regenerate_word.py | 开发/批跑：重建 Word；加 --full-pipeline 可一键跑完 Skill1+2 |
create_mock_sif_exports.py | 仅开发测试用 mock 数据 |
参考文档索引
| 文件 | 用途 |
|---|---|
first-time-setup.md | 首次使用：output_dir / download_dir 引导（Agent 必读） |
path-setup.md | 工作区、配置、路径解析 |
manual-export-guide.md | 方式 A 用户操作指引 |
browser-export-sop.md | 方式 B URL/按钮 SOP |
table-schema.md | 三张表结构（防出错） |
amazon-expert-guide.md | 顾问角色与参数引导 |
sop-tables.md | 分级/交叉规则细节 |
© binggandata, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 24 other files (scripts, references) in sif-keyword-scout of binggandata/bggg-skills.
- SKILL.md
- README.md
- references/amazon-expert-guide.md
- references/browser-export-sop.md
- references/first-time-setup.md
- references/manual-export-guide.md
- references/path-setup.md
- references/sop-tables.md
- references/table-schema.md
- references/threshold-presets.md
- scripts/.gitignore
- scripts/batch_regenerate_word.py
- scripts/check_history.py
- scripts/create_mock_sif_exports.py
- scripts/cross_analysis.py
- scripts/ingest_raw.py
- scripts/process_table1.py
- scripts/process_table2.py
- scripts/process_table3.py
- … and 6 more
Open the folder on GitHubat commit 1034ee5
Compare with similar skills
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| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sif Keyword Scout this skillbinggandata/bggg-skills | 605 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Instrument Data To Allotropeaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | 2 repos | ~2.7k | Automated safety check: Pass | Apache-2.0 | |
| Doc Cleanernotoriouslab/doc-cleaner | 309 | — | ~712 | Automated safety check: Pass | MIT | |
| MineruNebutra/MinerU-Skill | 123 | — | ~504 | Automated safety check: Pass | MIT | |
| XLSXzzhonglei/GeoCode-Release | 189 | — | ~3.1k | Automated safety check: Pass | MIT | |
| Python Bridgetmustier/pi-for-excel | 434 | — | ~820 | Automated safety check: Pass | MIT |
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Questions about Sif Keyword Scout
What does Sif Keyword Scout do?
亚马逊 Sif 关键词情报侦察系统（Skill 1）。用户输入 ASIN，获取 Sif 三张报表（关键词调研/反查流量词/查广告词）， Python 完成分层、机会评级、缺口分析，三表交叉输出 PD 主攻词单。支持「用户手动导出」与「AI 浏览器导出」两种方式。. Sif Keyword Scout is an agent skill from binggandata/bggg-skills.
When should I use Sif Keyword Scout?
Sif Keyword Scout fits situations like: documents & Office work in your project.
How do I install Sif Keyword Scout in Claude Code?
Run `npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a claude-code`. Or copy the skill folder (sif-keyword-scout in binggandata/bggg-skills) into .claude/skills/sif-keyword-scout in your project. Claude Code loads it when a task matches its description.
How do I install Sif Keyword Scout in Codex?
Run `npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a codex`. Or copy the skill folder (sif-keyword-scout in binggandata/bggg-skills) into .agents/skills/sif-keyword-scout in your project. Codex loads it when a task matches its description.
Can I use Sif Keyword Scout in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add binggandata/bggg-skills --skill sif-keyword-scout -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sif-keyword-scout, .gemini/skills/sif-keyword-scout, .github/skills/sif-keyword-scout and .opencode/skills/sif-keyword-scout in your project.
What does Sif Keyword Scout need to run?
Going by SKILL.md and its folder, Sif Keyword Scout needs Python for the scripts in its folder and the command-line tools its instructions call (python and pip). Our summary lists: Python 3.
Does Sif Keyword Scout access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is Sif Keyword Scout safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Sif Keyword Scout use?
Sif Keyword Scout is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sif Keyword Scout use?
About 2.9k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.9k tokens, read only when the agent opens those files.
What are the alternatives to Sif Keyword Scout?
Skills that share tags, products or a category with Sif Keyword Scout: Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Doc Cleaner (notoriouslab/doc-cleaner, 309 stars), Mineru (Nebutra/MinerU-Skill, 123 stars) and XLSX (zzhonglei/GeoCode-Release, 189 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sif Keyword Scout?
binggandata (a GitHub user) maintains it in binggandata/bggg-skills, which has 605 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 13, 2026.
Source: binggandata/bggg-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.