BC linkfox-kalodata-tiktok-video
通过kalodata数据搜索TikTok电商热门带货视频榜单并查询指定视频的详细数据，支持按地区、货币、语言与日期范围查看高排名/高播放/热销带货视频，并可用videoId获取播放、点赞、评论、分享、销售额、GPM及广告投放指标。当用户提到TikTok视频搜索、TikTok视频榜单、TikTok视频排行、TikTok热门视频、TikTok带货视频排行、TikTok爆量视频、TikTok视频详情、TikTok带货视频数据、视频播放量、视频互动数据、TikTok video search, TikTok video ranking, TikTok viral video chart, TikTok video detail, video analytics, kalodata video search/detail时触发此技能。即使用户未明确提及"kalodata"，只要其需求涉及查看TikTok平台热门带货视频榜单或某个TikTok视频的详细带货与互动数据，也应触发此技能。
通过kalodata数据搜索TikTok电商热门带货视频榜单并查询指定视频的详细数据，支持按地区、货币、语言与日期范围查看高排名/高播放/热销带货视频，并可用videoId获取播放、点赞、评论、分享、销售额、GPM及广告投放指标。当用户提到TikTok视频搜索、TikTok视频榜单、TikTok视频排行、TikTok…
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 3
✓ No critical or high findings
Medium and low: 3
-
low Dangerous commands
cmd-shell-rcreferences/onboarding.md:13Writes to a shell startup file (quoted — discussed, not commanded)- macOS zsh：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.zshrc && source ~/.zshrc`
quoted -
low Dangerous commands
cmd-shell-rcreferences/onboarding.md:14Writes to a shell startup file (detector / deny-list definition)- Linux bash：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.bashrc && source ~/.bashrc`
detector -
low Secrets in code
secret-high-entropy-tokenscripts/onboarding.py:49High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)or "eyJh…iJ9")
quoted
Files scanned: 7. 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")
Process rating: all ten parameters 57/100
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 32 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1896 tokens
- 100Running it twice. No mutating operations
- low 10 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
- -31 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 442: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.