BF kuai-shou-ai-feed
快手公开数据采集与竞品分析技能：按关键词搜索快手视频、获取博主主页作品列表、拉取视频评论，输出结构化 JSON 数据，用于爆款选题、竞品监控、KOL 筛选、评论舆情与趋势洞察。当用户要“搜快手视频 / 看快手博主作品 / 分析快手评论 / 做快手数据分析”时使用；仅支持快手平台公开数据，不支持抖音、小红书、B站，不获取私密或需登录的数据。Kuaishou public data fetcher: keyword video search, creator posts listing, comment scraping, competitor analysis, KOL discovery, trend insight.
快手公开数据采集与竞品分析技能：按关键词搜索快手视频、获取博主主页作品列表、拉取视频评论，输出结构化 JSON 数据，用于爆款选题、竞品监控、KOL 筛选、评论舆情与趋势洞察。当用户要“搜快手视频 / 看快手博主作品 / 分析快手评论 /…
As a process F 35/100 · Will not run — References files that are not bundled: scripts/kuaishou/...
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.
- 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: 22. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: 快手公开数据采集与竞品分析技能：按关键词搜索快手视频、获取博主主页作品列表、拉取视频评论，输出结构化 JSON 数据，用于爆款选题、… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
missing-refreference to a missing file: scripts/kuaishou/...
Process rating: all ten parameters 35/100
- 0Tools and files. 1 referenced file(s) missing: scripts/kuaishou/...
- 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
- 100Steps. 68 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1613 tokens
- 100Running it twice. No mutating operations
- low 11 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
- +4No input/output examples
- -226 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 313: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 68 items
- +4Reference files are cited in the instructions (2 of 2)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.