BF guaikei-douyin-extract-videos-comments-hot
当用户需要抖音公开数据时，使用本技能。覆盖四类数据：①关键词搜索（视频/图文/用户）、 ②博主作品批量抓取、③视频评论获取分析、④实时热榜查询。适用于内容调研、竞品账号分析、 用户评论洞察、热点趋势追踪；用户做短视频调研未明确提到"抖音"时同样触发。不适用于 发布/剪辑/下载视频、涨粉代运营咨询，也不覆盖其他短视频平台（各有对应技能）。
当用户需要抖音公开数据时，使用本技能。覆盖四类数据：①关键词搜索（视频/图文/用户）、 ②博主作品批量抓取、③视频评论获取分析、④实时热榜查询。适用于内容调研、竞品账号分析、 用户评论洞察、热点趋势追踪；用户做短视频调研未明确提到"抖音"时同样触发。不适用于…
As a process F 42/100 · Will not run — References files that are not bundled: assets/<name>_resp.schema.json, assets/*_resp.schema.json
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: 34. 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: assets/<name>_resp.schema.json - warning
missing-refreference to a missing file: assets/*_resp.schema.json
Process rating: all ten parameters 42/100
- 0Tools and files. 2 referenced file(s) missing: assets/<name>_resp.schema.json, assets/*_resp.schema.json
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 100Steps. 54 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 952 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (6 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
- +4No input/output examples
- +2Single-language instructions
- +3Description length 169: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 54 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: 64.