BD keynote-video
PPT/演示文稿 → 播报视频。交互式内容评估 + LLM讲稿生成 + 风格化口语重写 + 方案确认后自动合成。v2.0 架构：LLM管内容，脚本管技术。 支持7种风格：新闻播报/资讯快报/技术汇报/技术培训/故事讲述/商业演讲/轻松闲聊。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
ProcedureAI and agentsMedia and videoInfrastructuretype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
- 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: 10. 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") - note
frontmatter-keyunknown frontmatter key "read_when"
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1426 tokens
- 100Running it twice. No mutating operations
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
- -216 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 120: enough signal without eating the budget
- +4Structure: 19 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (15 code blocks)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.
External checks
ClawHub: suspicious
The skill appears purpose-built for turning presentations into narrated videos, but it needs Review because it runs local shell commands with weak argument isolation and may send presentation-derived narration to an online TTS service without clear disclosure.
LLM: suspicious (high) · VirusTotal: · 29 May 2026