BD tiktok-influencer
通过 Gecho Bridge MCP 采集 TikTok 创作者的公开视频，返回视频元数据、文案、互动指标、发布时间和链接。需要安装 Gecho Chrome 扩展、保持有效的 TikTok 登录会话，并配置 Gecho Bridge MCP 服务。
通过 Gecho Bridge MCP 采集 TikTok 创作者的公开视频，返回视频元数据、文案、互动指标、发布时间和链接。需要安装 Gecho Chrome 扩展、保持有效的 TikTok 登录会话，并配置 Gecho Bridge MCP 服务。
As a process D 38/100 · 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.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 126 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill
Process rating: all ten parameters 38/100
- 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
- 0Consistency. Frontmatter name (tiktok-influencer) differs from the folder (tiktok-influencer-zh-cn)
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 60 steps
- 100Execution cost. Instruction body is 1990 tokens
- 100Running it twice. No mutating operations
- low 17 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
- +1No license
- +2Single-language instructions
- +3Description length 126: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 60 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.