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AD amazon-daily-competitor-radar

Use when: user says "每日竞品监控" / "盯一下 ASIN X 的价格变化" / "今天我的排名怎么样" / "我的关键词排名跌了吗" / "竞品有动作吗" / "track ASIN X" / "daily radar" / "Buy Box 被抢了吗". Covers: ASIN 健康体检 + 贴身竞品 SERP 排位 + 类目新进入者扫描 + 主动给"防守/反击"动作建议。可写入基线对比"昨天 vs 今天"。 NOT for: 从 0 到 1 找新 niche (use amazon-product-explorer) / 写 Listing 文案 (use amazon-listing-optimization) / 单点查询 ASIN (call get_amazon_product directly).

ClawHub Agent Skills author: Pangolinfo v4.0.0 MIT-0 2 files body ≈ 5 630 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
78
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token SKILL.md:521
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | `top5…max` | `top5…Max`（products 不是 brands）|
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:571
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | filter_niches 入参 | 传 `categoryId` | 只认 `nicheId`/`nicheTitle`;0-1 小数字段(`top5…Max`/`retu…Max`)别传整数 |
    table

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5630 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "mcp_tools_used"
  • note frontmatter-key unknown frontmatter key "applies_to"
  • note frontmatter-key unknown frontmatter key "budget"
  • note frontmatter-key unknown frontmatter key "baseline_storage"

Process rating: all ten parameters 48/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 40Consistency. Frontmatter name (amazon-daily-competitor-radar) differs from the folder (pangolinfo-amazon-daily-competitor-radar)
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5630 tokens
  • 100Steps. 107 steps
  • 100Running it twice. No mutating operations
  • low 19 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (5 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +3Output format is not stated: the model decides each time
  • -255 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 373: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 107 items
  • +4Has examples (13 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: clean
This is a coherent Amazon competitor monitoring skill, with disclosed local snapshots and optional daily scheduling that users should opt into deliberately.
LLM: benign (high) · VirusTotal: · 20 Aug 2026