BC video-to-article
将视频、字幕文件或大型课程目录转换为结构化图文笔记。仅当用户明确要求执行完整的视频转文章工作流，例如下载视频、生成图文文章、写入 Obsidian 或批量导入课程时使用；如果用户只想做摘要、问答、术语提取或轻量分析，应优先采用只读分析流程。
将视频、字幕文件或大型课程目录转换为结构化图文笔记。仅当用户明确要求执行完整的视频转文章工作流，例如下载视频、生成图文文章、写入 Obsidian 或批量导入课程时使用；如果用户只想做摘要、问答、术语提取或轻量分析，应优先采用只读分析流程。
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Terminalallowed-tools: Read Write Terminal File Vision Search
Files scanned: 8. 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 51/100
- 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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 9 mutating operations with no state check
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 81 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2132 tokens
- low The response is described with custom markup (7 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
- -44 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 120: enough signal without eating the budget
- +4Structure: 38 headings
- +3Step-by-step instructions: 81 items
- +4Has examples (15 code blocks)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.