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BD 1688-product-analysis

1688 商品全方位分析诊断工具，整合多数据源对指定商品进行深度分析，支持多店铺异常商品汇总、评分分层选品与单品诊断。 覆盖能力：商品数据分析、销售表现诊断、流量问题排查、广告效果评估、商品优化建议、多店铺异常商品汇总、重点品评分分层、关键词搜索商品。 适用场景：用户需要分析商品表现、诊断流量问题、查看多店铺异常商品、圈选重点运营商品、搜索店铺商品、获取商品优化建议时使用。 触发词：分析这个商品、商品诊断、商品表现分析、为什么商品没流量、商品优化建议、最该优化的商品、最应该优化的商品、商品数据分析、多店铺商品、重点品查看、圈选重点品、圈选运营商品、今日运营重点、选品、推荐商品、商品分层、商品优先级、搜索商品、新品没流量怎么办、导出商品体检报告、导出当前已生成的报告、输出商品体检报告 Markdown、在当前对话输出商品体检报告。 本 Skill 的核心流程已由 workflow `1688-product-analysis` 编排覆盖，包含明确商品 ID 诊断、异常商品选择、找问题品并诊断、关键词搜索、正向选品、同款商品分析、商品库推荐行动点与一键优化交接；单接口查询、自由组合或探索性分析回退加载本 SKILL.md。

ClawHub Agent Skills author: 1688AiInfra v1.0.0 MIT-0 51 files body ≈ 9 443 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 40/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureAI and agentstype 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
B
80/100
safety, quality, tests
Safety 60%
94
Quality 40%
60
Run on models
none yet
Process rating
D
40/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. 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 · 6

✓ No critical or high findings

Medium and low: 6
  • low Dangerous commands cmd-eval-dynamic workflow/1688-product-analysis.js:166
    Dynamic code execution from decoded/untrusted input (quoted — discussed, not commanded)
    ? `${program} -c "import base64;exec(base…ode('${toBase64(utf8Bytes(script))}').decode())"${argsPart} > "${outFile}" 2> "${errFile}"`
    quoted
  • low Secrets in code secret-high-entropy-token workflow/1688-product-analysis.js:2280
    High-entropy token-like string (may be an id, hash or a credential)
    function proj…ort(data) {
  • low Secrets in code secret-high-entropy-token workflow/1688-product-analysis.js:2310
    High-entropy token-like string (may be an id, hash or a credential)
    function proj…ent(data) {
  • low Secrets in code secret-high-entropy-token workflow/1688-product-analysis.js:2311
    High-entropy token-like string (may be an id, hash or a credential)
    const v2 = proj…ort(data)
  • low Secrets in code secret-high-entropy-token workflow/1688-product-analysis.js:2335
    High-entropy token-like string (may be an id, hash or a credential)
    const v2 = proj…ort(data)
  • low Secrets in code secret-high-entropy-token workflow/1688-product-analysis.js:4452
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const v2Ac…nce = proj…ent(sameOfferData)
    quoted

Files scanned: 51. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 9443 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 40/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Execution cost. Instruction body is 9443 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 128 steps
  • 100Consistency. Name and required fields are in place
  • low The response is described with custom markup (15 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -216 emoji in the instructions: noise for the model
  • -36 of 6 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 517: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 128 items
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: clean
This is a coherent 1688 seller product-diagnosis skill with authenticated read access, bounded report caching, and user-gated follow-up actions.
LLM: benign (high) · VirusTotal: · 3 Sept 2026