Agent skill

Listing Quality Scorer

by infometa in infometa/workbuddyskills

对已生成或已提供的 Amazon Listing 做证据化质量评分,输出统一 scorePanel、扣分证据和 AI 导购准备度,是唯一评分事实源。用户要求打分、质量报告、before/after 对比,或 listing-audit 需要基础质量分时触发。路由判断:只回答「多少分、差在哪个维度」用本 Skill;要回答「为什么没流量、为什么不转化」并给出改哪些字段和文案之外的运营动作用…

No licenceAuto-check passed

Install Listing Quality Scorer

skills CLI
$ npx skills add infometa/workbuddyskills --skill listing-quality-scorer -a claude-code

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

GitHub CLI
$ gh skill install infometa/workbuddyskills listing-quality-scorer --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/infometa/workbuddyskills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/experts/linkfox-expert-amazon-listing-specialist/skills/listing-quality-scorer .claude/skills/listing-quality-scorer && 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
listing-quality-scorer
GitHub stars
348
Token cost
~1k tokens
SKILL.md length
224 words
Files
14 (incl. scripts, references)
Skills in repo
218
Repo updated
First seen
Licence
None found

At a glance

对已生成或已提供的 Amazon Listing 做证据化质量评分,输出统一 scorePanel、扣分证据和 AI 导购准备度,是唯一评分事实源。用户要求打分、质量报告、before/after 对比,或 listing-audit 需要基础质量分时触发。路由判断:只回答「多少分、差在哪个维度」用本 Skill;要回答「为什么没流量、为什么不转化」并给出改哪些字段和文案之外的运营动作用…

  • Works in 8 steps: 平台合规与风险 → 商品事实与声明可信度 → 搜索匹配与语义可发现性 → …
  • SKILL.md covers 边界, 输入, Canonical 8 Dimensions and Execution, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Listing Quality Scorer is an agent skill from infometa/workbuddyskills. 对已生成或已提供的 Amazon Listing 做证据化质量评分,输出统一 scorePanel、扣分证据和 AI 导购准备度,是唯一评分事实源。用户要求打分、质量报告、before/after 对比,或 listing-audit 需要基础质量分时触发。路由判断:只回答「多少分、差在哪个维度」用本 Skill;要回答「为什么没流量、为什么不转化」并给出改哪些字段和文案之外的运营动作用 listing-audit;要产出新文案用 listing-core mode=rewrite。普通 listing-core 生成只跑本地基础质量门,不默认调用本 Skill。即使输入数据不完整也可输出 N/A 维度,但不得编造分数。

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts and reference files (for example `_meta.json`, `references/scoring-input-contract.md` and `references/scoring-rubric.md`).

The repository describes itself as: WorkBuddy skills / connectors / experts archive for offline study.

Example prompts

  • “/listing-quality-scorer”

Requirements

  • Python 3

Workflow steps

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

  1. 平台合规与风险
  2. 商品事实与声明可信度
  3. 搜索匹配与语义可发现性
  4. 标题点击与首屏识别质量
  5. 五点转化与购买决策支持
  6. 信息完整度与 AI 导购可回答性
  7. 语言质量与站点本地化
  8. 差异化与竞争安全

What it can do on your machine

Read from SKILL.md and the folder at commit 91b77ea. 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 6 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    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

Listing Quality Scorer loads about 1k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 224 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

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

Without a licence we can't republish the file, so here is its outline and opening line. It has 224 words (~1,008 tokens).

“本 Skill 是唯一评分事实源。listing-audit、独立评分报告和 before/after 对比必须复用本 Skill;其他 Skill 不得维护第二套权重、hard gate、grade 或 overall 算法。”

— opening of SKILL.md by infometa
name
listing-quality-scorer

Read the full SKILL.md on GitHub

Files

SKILL.md and 13 other files (scripts, references) in experts/linkfox-expert-amazon-listing-specialist/skills/listing-quality-scorer of infometa/workbuddyskills.

  • SKILL.md
  • _meta.json
  • references/scoring-input-contract.md
  • references/scoring-rubric.md
  • scripts/linkfox_save.py
  • scripts/listing_spec.py
  • scripts/normalize_listing_input.py
  • scripts/render_scored_report.py
  • scripts/save_quality_score_output.py
  • scripts/score_quality.py
  • tests/test_listing_input_contract.py
  • tests/test_render_scored_report.py
  • tests/test_save_quality_score_output.py
  • tests/test_score_quality.py

Open the folder on GitHubat commit 91b77ea

Compare with similar skills

Listing Quality Scorer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Listing Quality Scorer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Listing Quality Scorer this skillinfometa/workbuddyskills348—~1kAutomated safety check: PassNone
Amazon Listing Competitor Analysisbrowser-act/skills6.1k1 repos~3.2kAutomated safety check: PassMIT
Amazon Listing Optimizationnexscope-ai/Amazon-Skills7441 repos~4.3kAutomated safety check: PassMIT
Amazon Listing Imagesnexscope-ai/Amazon-Skills744—~5.5kAutomated safety check: PassMIT
Amazon Listing Builderliangdabiao/amazon-sorftime-research-MCP-skill959—~1.7kAutomated safety check: PassNone
Amazon International Listingsnexscope-ai/Amazon-Skills744—~467Automated safety check: PassMIT

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Questions about Listing Quality Scorer

What does Listing Quality Scorer do?

对已生成或已提供的 Amazon Listing 做证据化质量评分,输出统一 scorePanel、扣分证据和 AI 导购准备度,是唯一评分事实源。用户要求打分、质量报告、before/after 对比,或 listing-audit 需要基础质量分时触发。路由判断:只回答「多少分、差在哪个维度」用本 Skill;要回答「为什么没流量、为什么不转化」并给出改哪些字段和文案之外的运营动作用…. Listing Quality Scorer is an agent skill from infometa/workbuddyskills.

How do I install Listing Quality Scorer in Claude Code?

Run `npx skills add infometa/workbuddyskills --skill listing-quality-scorer -a claude-code`. Or copy the skill folder (experts/linkfox-expert-amazon-listing-specialist/skills/listing-quality-scorer in infometa/workbuddyskills) into .claude/skills/listing-quality-scorer in your project. Claude Code loads it when a task matches its description.

How do I install Listing Quality Scorer in Codex?

Run `npx skills add infometa/workbuddyskills --skill listing-quality-scorer -a codex`. Or copy the skill folder (experts/linkfox-expert-amazon-listing-specialist/skills/listing-quality-scorer in infometa/workbuddyskills) into .agents/skills/listing-quality-scorer in your project. Codex loads it when a task matches its description.

Can I use Listing Quality Scorer 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 infometa/workbuddyskills --skill listing-quality-scorer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/listing-quality-scorer, .gemini/skills/listing-quality-scorer, .github/skills/listing-quality-scorer and .opencode/skills/listing-quality-scorer in your project.

What does Listing Quality Scorer need to run?

Going by SKILL.md and its folder, Listing Quality Scorer needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Listing Quality Scorer 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 Listing Quality Scorer 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 Listing Quality Scorer use?

No licence was found for Listing Quality Scorer or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.

How many tokens does Listing Quality Scorer use?

About 1k tokens (SKILL.md is roughly 4k 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 5.2k tokens, read only when the agent opens those files.

What are the alternatives to Listing Quality Scorer?

Skills that share tags, products or a category with Listing Quality Scorer: Amazon Listing Competitor Analysis (browser-act/skills, 6.1k stars), Amazon Listing Optimization (nexscope-ai/Amazon-Skills, 744 stars), Amazon Listing Images (nexscope-ai/Amazon-Skills, 744 stars) and Amazon Listing Builder (liangdabiao/amazon-sorftime-research-MCP-skill, 959 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Listing Quality Scorer?

infometa (a GitHub user) maintains it in infometa/workbuddyskills, which has 348 GitHub stars. The repository holds 218 skills in this directory. The repository was last updated on October 9, 2026.

Source: infometa/workbuddyskills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.