CC byted-las-vlm-video
Analyzes and understands video content using Volcengine LAS Doubao vision-language models (VLM). Multimodal AI video analysis, video comprehension, and visual understanding of video clips and footage. Performs video question answering (video Q&A) — ask questions about what happens in a video and get AI answers. Scene recognition and scene description, object recognition and object detection, action recognition and action detection from video frames. Generates video descriptions, video captions, video summaries, video annotations, and content summarization. Visual frame analysis for identifying people, objects, actions, and events in video. Auto-compresses video to 50MB before inference. Synchronous single-call processing. Use this skill when the user wants to analyze or understand video content using VLM/AI, do video Q&A (ask questions about a video), describe what happens in a video, recognize objects/actions/scenes in video frames, generate video captions/descriptions/summaries, annotate or label video content, get AI-powered visual understanding of video clips, or perform multimodal video analysis with vision-language models.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Shorten the description to 1024 characters.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
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high Dangerous commands
cmd-install-from-urlscripts/env_init.sh:41Installs a package from an untrusted URL / archivepip install --quiet --upgrade https://las-ai-cn-beijing-online.tos-cn-beijing.volces.com/operator_cards_serving/public/skills/sdk/las_…whl
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1146 chars, limit 1024
Process rating: all ten parameters 59/100
- 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
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 28 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 926 tokens
- 100Running it twice. No mutating operations
- medium 3 test cases, all positive: not one "should refuse" or "should ask first"
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)
- +3Description length 1146: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -32 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +4Structure: 11 headings
- +3Step-by-step instructions: 28 items
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.