BF keyword-research
亚马逊关键词深度调研与智能分类分析。基于 Sorftime MCP 数据采集 2000+ 关键词，通过 LLM Agent 按 8 维度智能分类（否定词、品牌词、材质词、场景词、属性词、功能词、核心词、其他），生成 Markdown 报告、CSV 词库和 HTML 仪表板。触发方式：/keyword-research {ASIN} {SITE}
As a process F 31/100 · Will not run — References files that are not bundled: references/sorftime-keyword-api.md, references/classification-rules.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
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 · 0
✓ No critical or high findings
Files scanned: 18. 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
missing-refreference to a missing file: references/sorftime-keyword-api.md - warning
missing-refreference to a missing file: references/classification-rules.md
Process rating: all ten parameters 31/100
Will not run. References files that are not bundled: references/sorftime-keyword-api.md, references/classification-rules.md
- 0Tools and files. 2 referenced file(s) missing: references/sorftime-keyword-api.md, references/classification-rules.md
- 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
- 40Consistency. Frontmatter name (keyword-research) differs from the folder (amazon-sorftime-research-keywords-skill)
- 100Steps. 50 steps
- 100Execution cost. Instruction body is 2650 tokens
- 100Running it twice. No mutating operations
- low 14 top-level sections: this looks like several domains in one skill
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
- -213 emoji in the instructions: noise for the model
- -44 reference files, but SKILL.md never points to them: the model will not open them
- -38 of 10 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 173: enough signal without eating the budget
- +4Structure: 49 headings
- +3Step-by-step instructions: 50 items
- +4Has examples (22 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.
External checks
ClawHub: clean
This skill is a disclosed Amazon keyword research workflow that sends product and keyword queries to Sorftime, uses optional LLM classification, and writes local reports; it has privacy and credential-handling caveats but no evidence of hidden or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026