BD ai-video-auto-generator
AI 短视频全自动流水线：从想法到成片，一键出视频。脚本生成→自动修复→资产生成→视频→音频→字幕，全自动无人值守。| AI video auto pipeline: from idea to final video, one command. Script generation → auto repair → assets → video → audio → subtitles, fully automated.
AI 短视频全自动流水线：从想法到成片，一键出视频。脚本生成→自动修复→资产生成→视频→音频→字幕，全自动无人值守。| AI video auto pipeline: from idea to final video, one command.
As a process D 46/100 · Unfinished process — 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.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-agent-memory-dump.workbuddy/memory/2026-07-15.mdAgent memory / workspace files bundled with the skill (2) — likely a workspace dump with personal data or tokens.workbuddy/memory/2026-07-15.md, .workbuddy/memory/2026-07-16.md
Files scanned: 33. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 209 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 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 (bash, web) that frontmatter does not declare
- 100Steps. 41 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2338 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
- -218 emoji in the instructions: noise for the model
- -34 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 209: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (11 of 13)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 65.