BD 1688-shop-daily-report
1688 店铺经营日报 —— 生成指定日期的店铺经营日报。 工具能力：展示店铺主要经营数据（GMV、询盘、订单量）、流量数据（UV、PV、CTR、跳失率）、用户数据分析，并进行异常提醒和经营建议。日期不包括今天，默认输出昨天的日报。本 Skill 的流程已由 workflow 编排覆盖，命中触发词时直接执行 workflow。如果 workflow 无法完成任务（如纯能力问答、单命令调用、探索性使用），加载本 SKILL.md 进行推理。 触发词：日报、经营报告、店铺分析、店铺日报、生成日报、经营数据。
1688 店铺经营日报 —— 生成指定日期的店铺经营日报。 工具能力：展示店铺主要经营数据（GMV、询盘、订单量）、流量数据（UV、PV、CTR、跳失率）、用户数据分析，并进行异常提醒和经营建议。日期不包括今天，默认输出昨天的日报。本 Skill 的流程已由 workflow 编排覆盖，命中触发词时直接执行…
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Dangerous commands
cmd-eval-dynamicworkflow/1688-shop-daily-report.js:84Dynamic code execution from decoded/untrusted input (quoted — discussed, not commanded)redirectedCmd = `${prog} -c "import base64;exec(base…ode('${toBase64(utf8Bytes(script))}').decode())"${argsPart} > "${outF}" 2> "${errF}"`quoted
Files scanned: 54. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 6451 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 49/100
- 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
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Execution cost. Instruction body is 6451 tokens
- 100Steps. 122 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (8 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
- -245 emoji in the instructions: noise for the model
- -35 of 6 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 254: enough signal without eating the budget
- +4Structure: 45 headings
- +3Step-by-step instructions: 122 items
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.