BC ai-image-video-llm-api
AI image generation, video generation, and LLM chat API — call Nano Banana 2, Seedream, Kling, Seedance, Qwen, DeepSeek and more through one unified API key via Atlas Cloud. Use this skill when the user needs to: generate images with AI (text-to-image, image editing, Nano Banana, Imagen, Seedream, Flux, DALL-E, Qwen-Image), generate videos with AI (text-to-video, image-to-video, Kling, Seedance, Vidu, Wan), call LLM chat APIs (Qwen, DeepSeek, GLM, Kimi, MiniMax, OpenAI-compatible format), integrate AI generation into their project, compare AI model pricing, or find a cheap AI API. Also trigger when users ask about AI API integration, serverless AI inference, or need a single API for multiple AI providers. Even if Atlas Cloud is not mentioned by name, consider this skill whenever the user wants to call AI generation or LLM APIs.
AI image generation, video generation, and LLM chat API — call Nano Banana 2, Seedream, Kling, Seedance, Qwen, DeepSeek and more through one unified API key…
As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
How to improve
- 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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "source" - note
frontmatter-keyunknown frontmatter key "homepage"
Process rating: all ten parameters 55/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (ai-image-video-llm-api) differs from the folder (atlas-cloud-ai-api)
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 12 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 2058 tokens
- low 10 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)
- +3Description length 839: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 23 headings
- +3Step-by-step instructions: 12 items
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.