Agent skill

Zach Search Term Report Analyzer

by zach22-1999 in zach22-1999/amazon-skills

分析 Amazon Ads SP / SB / SD 搜索词报告。确定性脚本负责清洗、时间窗聚合、词根聚类和决策计算,AI 助手或人工负责词根级语义分类;通过词根继承减少长尾词的待判定比例,输出 Markdown、CSV、HTML 和 JSON 六类结果。

MITAuto-check: notesDocuments & Office

Install Zach Search Term Report Analyzer

skills CLI
$ npx skills add zach22-1999/amazon-skills --skill zach-search-term-report-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zach22-1999/amazon-skills zach-search-term-report-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zach22-1999/amazon-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/zach-search-term-report-analyzer .claude/skills/zach-search-term-report-analyzer && rm -rf skills-src

Use ~/.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/

Facts

Skill name
zach-search-term-report-analyzer
GitHub stars
209
Token cost
~1.1k tokens
SKILL.md length
236 words
Files
33 (incl. scripts, references, assets)
Skills in repo
7
Repo updated
First seen
Licence
MIT

At a glance

分析 Amazon Ads SP / SB / SD 搜索词报告。确定性脚本负责清洗、时间窗聚合、词根聚类和决策计算,AI 助手或人工负责词根级语义分类;通过词根继承减少长尾词的待判定比例,输出 Markdown、CSV、HTML 和 JSON 六类结果。

  • Works in 5 steps: 禁止使用 uncertain_term,必须给出 category 和… → 所有待分类词根必须覆盖,缺一个 Stage C 都会失败。 → needs_listing_check 只用于少数确实依赖页面能力才能判断的词根。 → …
  • Tasks that involve E-commerce operations
  • SKILL.md covers 工作方式, 需要的输入, 本地参考 and Stage A:准备分析工作簿, plus 6 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Zach Search Term Report Analyzer is an agent skill from zach22-1999/amazon-skills. 分析 Amazon Ads SP / SB / SD 搜索词报告。确定性脚本负责清洗、时间窗聚合、词根聚类和决策计算,AI 助手或人工负责词根级语义分类;通过词根继承减少长尾词的待判定比例,输出 Markdown、CSV、HTML 和 JSON 六类结果。 使用时机:判断搜索词是否应该否定、控成本、继续测试或放量,分析 7/14/30 天 CVR 与 ACOS 变化,或者提炼可反馈给 Listing 的属性词和场景词。 触发词:/zach-search-term-report-analyzer

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 36 other files, including scripts, reference files and assets (for example `README.md`, `examples/listing-context-sample.md` and `examples/root-classifications-sample.json`).

It sits in Documents & Office, covering E-commerce operations and CSV and tabular files. The repository describes itself as: Open-source Agent Skills for Amazon sellers: product research, feature validation, listing audits, ads search-term analysis, and CVR diagnostics. 亚马逊跨境电商 Skills。 The licence is MIT.

When your agent uses it

  • Tasks that involve E-commerce operations
  • Tasks that involve CSV and tabular files

Example prompts

  • “/zach-search-term-report-analyzer”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash, Glob, Grep

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. 禁止使用 uncertain_term,必须给出 category 和 relevance。
  2. 所有待分类词根必须覆盖,缺一个 Stage C 都会失败。
  3. needs_listing_check 只用于少数确实依赖页面能力才能判断的词根。
  4. 只有成员词明显偏离词根语义时才写 term_overrides。
  5. 否词判断必须同时考虑相关性、样本量和 Listing 承接,不因一次点击机械否定。

What it can do on your machine

Read from SKILL.md and the folder at commit 5c790ea. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash
    • Glob
    • Grep

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Zach Search Term Report Analyzer loads about 1.1k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 236 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Bash, Glob, Grep

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 zach22-1999/amazon-skills at commit 5c790ea, republished under its MIT licence (© zach22-1999). 236 words, ~1,147 tokens.

Download SKILL.mdSave it as .claude/skills/zach-search-term-report-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 32 other files; get the full folder from GitHub.
name
zach-search-term-report-analyzer
description
分析 Amazon Ads SP / SB / SD 搜索词报告。确定性脚本负责清洗、时间窗聚合、词根聚类和决策计算,AI 助手或人工负责词根级语义分类;通过词根继承减少长尾词的待判定比例,输出 Markdown、CSV、HTML 和 JSON 六类结果。 使用时机:判断搜索词是否应该否定、控成本、继续测试或放量,分析 7/14/30 天 CVR 与 ACOS 变化,或者提炼可反馈给 Listing 的属性词和场景词。 触发词:/zach-search-term-report-analyzer
allowed-tools
Read, Write, Edit, Bash, Glob, Grep
triggers
/zach-search-term-report-analyzer
user-invocable
true
risk-level
medium

Amazon 搜索词报告分析(v2)

工作方式

v2 将确定性计算与语义判断分开:

text
搜索词报告
  → Stage A:清洗、7/14/30 天聚合、词根聚类、硬标签
  → Stage B:AI 助手或人工完成词根语义分类
  → Stage C:严格校验、词根决策继承、六类结果渲染

词根继承用于处理低样本长尾词:当单个搜索词样本不足、但所属词根样本足够时,该词继承词根级判断;词与词根样本都不足时进入低量长尾池 pool,汇总监控但不伪装成待判定。

需要的输入

参数必须默认值说明
搜索词报告是—CSV / XLSX / XLSM / XLS
ASIN是—一次只分析一个 ASIN
品牌是—用于品牌词硬标签与输出命名
目标 ACOS是—使用小数,例如 0.20
站点否US用于可选的 Listing 上下文抓取
报告类型否自动识别SP / SB / SD
时间窗否7,14,30用逗号分隔
Listing 上下文否空可传入本地 Markdown / 文本快照

如果报告包含多个 ASIN,先从清洗元数据中列出候选,再让用户选定一个;不要混合分析。目标 ACOS、品牌或 ASIN 缺失时必须补齐,不能用隐藏默认值代替。

本地参考

  • references/architecture.md — v2 管线、数据契约与决策顺序
  • references/field_mapping.md — SP / SB / SD 字段映射
  • references/decision_rules.md — 决策规则的运营解释
  • references/term_classification.md — Stage B 分类枚举与 JSON schema
  • references/output_template.md — 六类输出与完成信号
  • scripts/prepare_search_term_analysis.py — Stage A
  • scripts/finalize_search_term_report.py — Stage C
  • scripts/clean_search_term_report.py — 清洗底层
  • scripts/fetch_listing_context.py — 可选 Listing 上下文抓取

Stage A:准备分析工作簿

bash
python3 skills/zach-search-term-report-analyzer/scripts/prepare_search_term_analysis.py \
  <input_file> \
  --asin B0XXXXXXXX \
  --brand ExampleBrand \
  --site US \
  --target-acos 0.20 \
  --windows 7,14,30 \
  --listing-context-file <optional-listing-context.md> \
  --output-dir outputs/search-term-report-analyzer/ExampleBrand/intermediate/

--listing-context-file 与 --report-type 均为可选参数,不使用时删除对应命令行。

Stage A 只做可复现计算:

  • 标准化字段、搜索词和数值格式
  • 识别无法解析的非空数值,禁止静默清零
  • 按 7/14/30 天窗口聚合并重新计算 CTR、CVR、ACOS、ROAS
  • 聚类搜索词词根
  • 标记确定性的 asin_term 与 brand_term

它会在中间目录生成:

  • workbook.json:term、root、窗口指标和分类请求
  • roots_for_review.md:按花费排序的待分类词根表

Stage B:完成词根分类

读取 roots_for_review.md、workbook.json 中的 Listing 上下文和 references/term_classification.md,为 classification_request.roots_to_classify 中每一个词根填写:

  • category
  • relevance
  • 一句话 note
  • 可选的 needs_listing_check

输出 root_classifications.json。示意结构:

json
{
  "asin": "B0XXXXXXXX",
  "classified_by": "ai_assistant",
  "listing_context_source": "workbook.meta.listing_context",
  "roots": {
    "portable karaoke": {
      "category": "core_category_term",
      "relevance": "high",
      "note": "与目标商品的核心用途直接一致",
      "needs_listing_check": false
    }
  },
  "term_overrides": {}
}

分类纪律:

  1. 禁止使用 uncertain_term,必须给出 category 和 relevance。
  2. 所有待分类词根必须覆盖,缺一个 Stage C 都会失败。
  3. needs_listing_check 只用于少数确实依赖页面能力才能判断的词根。
  4. 只有成员词明显偏离词根语义时才写 term_overrides。
  5. 否词判断必须同时考虑相关性、样本量和 Listing 承接,不因一次点击机械否定。

Stage C:生成正式结果

bash
python3 skills/zach-search-term-report-analyzer/scripts/finalize_search_term_report.py \
  outputs/search-term-report-analyzer/ExampleBrand/intermediate/workbook.json \
  --classifications outputs/search-term-report-analyzer/ExampleBrand/intermediate/root_classifications.json \
  --output-dir outputs/search-term-report-analyzer/ExampleBrand/

Stage C 启动时会严格校验分类覆盖率和枚举值。校验通过后,每个搜索词得到一个主决策、一个决策依据层级 basis、置信度和原因。

输出

输出目录建议为 outputs/search-term-report-analyzer/{brand}/:

文件用途
..._搜索词报告分析.md主报告
..._搜索词分析明细.csv全词明细
..._否词清单.csvexact 否词候选与 root 级 phrase 建议
..._搜索词分析操作台.html可筛选、排序、勾选和导出 CSV 的交互工作台
..._搜索词分析汇报.htmlKPI、决策分布和花费去向静态汇报页
..._run_summary.json验收指标与文件清单

两个 HTML 都是自包含单文件,数据内联,无 CDN、Webfont、外链图片或运行时 fetch,可直接用浏览器打开。

验收

读取 run_summary.json 并核对:

  1. pending_ratio_terms 与 pending_ratio_spend 均不高于 0.10;超标必须解释。
  2. pool 的词数、点击、花费和订单在报告中单独披露,且不计入 pending。
  3. 六类正式输出全部存在,两个 HTML 不包含“payload 未注入”提示。
  4. 决策分布、花费去向、主报告和 CSV 相互一致。
  5. 报告区分数据事实与分析推断,并标注来源文件和时间范围。

风险与边界

  • 本 skill 只输出建议,不自动修改广告预算、bid、匹配类型或否词。
  • 字段不足时不强行生成 ACOS / CVR 结论;SB / SD 缺少订单或销售字段时,只做可由现有字段支持的判断并声明限制。
  • ASIN 串号、产品混杂、分类覆盖不全、核心字段缺失或 pending 超标时,必须升级人工复核。
  • 任何真实广告修改都应在用户确认后通过对应广告平台执行。

旧版兼容入口

scripts/analyze_search_term_decisions.py 暂时保留,供已有自动化过渡使用,但已弃用。新任务只使用 Stage A → Stage B → Stage C;旧入口将在后续大版本移除。

© zach22-1999, 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 32 other files (scripts, references, assets) in skills/zach-search-term-report-analyzer of zach22-1999/amazon-skills.

  • SKILL.md
  • README.md
  • assets/console_template.html
  • assets/report_template.html
  • examples/listing-context-sample.md
  • examples/root-classifications-sample.json
  • examples/search-term-report-sample.csv
  • references/architecture.md
  • references/decision_rules.md
  • references/field_mapping.md
  • references/output_template.md
  • references/term_classification.md
  • requirements.txt
  • scripts/analyze_search_term_decisions.py
  • scripts/browser_utils.py
  • scripts/clean_search_term_report.py
  • scripts/fetch_listing_context.py
  • … and 16 more

Open the folder on GitHubat commit 5c790ea

Compare with similar skills

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Questions about Zach Search Term Report Analyzer

What does Zach Search Term Report Analyzer do?

分析 Amazon Ads SP / SB / SD 搜索词报告。确定性脚本负责清洗、时间窗聚合、词根聚类和决策计算,AI 助手或人工负责词根级语义分类;通过词根继承减少长尾词的待判定比例,输出 Markdown、CSV、HTML 和 JSON 六类结果。. Zach Search Term Report Analyzer is an agent skill from zach22-1999/amazon-skills.

When should I use Zach Search Term Report Analyzer?

Zach Search Term Report Analyzer fits situations like: tasks that involve E-commerce operations; tasks that involve CSV and tabular files.

How do I install Zach Search Term Report Analyzer in Claude Code?

Run `npx skills add zach22-1999/amazon-skills --skill zach-search-term-report-analyzer -a claude-code`. Or copy the skill folder (skills/zach-search-term-report-analyzer in zach22-1999/amazon-skills) into .claude/skills/zach-search-term-report-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Zach Search Term Report Analyzer in Codex?

Run `npx skills add zach22-1999/amazon-skills --skill zach-search-term-report-analyzer -a codex`. Or copy the skill folder (skills/zach-search-term-report-analyzer in zach22-1999/amazon-skills) into .agents/skills/zach-search-term-report-analyzer in your project. Codex loads it when a task matches its description.

Can I use Zach Search Term Report Analyzer 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 zach22-1999/amazon-skills --skill zach-search-term-report-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zach-search-term-report-analyzer, .gemini/skills/zach-search-term-report-analyzer, .github/skills/zach-search-term-report-analyzer and .opencode/skills/zach-search-term-report-analyzer in your project.

What does Zach Search Term Report Analyzer need to run?

Going by SKILL.md and its folder, Zach Search Term Report Analyzer needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash, Glob, Grep.

Does Zach Search Term Report Analyzer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Zach Search Term Report Analyzer safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. 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 Zach Search Term Report Analyzer use?

Zach Search Term Report Analyzer 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 Zach Search Term Report Analyzer use?

About 1.1k tokens (SKILL.md is roughly 4.6k 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 12k tokens, read only when the agent opens those files.

What are the alternatives to Zach Search Term Report Analyzer?

Skills that share tags, products or a category with Zach Search Term Report Analyzer: Data Table Manager (n8n-io/n8n, 207k stars), Instrument Data To Allotrope (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Abuse Hunter (nexu-io/harness-engineering-guide, 664 stars) and Intelligence Requirements Builder (TracecatHQ/tracecat, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zach Search Term Report Analyzer?

zach22-1999 (a GitHub user) maintains it in zach22-1999/amazon-skills, which has 209 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on August 20, 2026.

Source: zach22-1999/amazon-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.