SKILLEMALL.ai
GitHub ↗ RU Sign in

AB seedream-imagegen

Generate high-quality images using Seedream 4.5 API (Volcengine/火山引擎). Supports text-to-image, image editing, multi-image fusion, and sequential image generation. Triggers include requests like "generate an image", "create a picture", "make a poster", "edit this image", "生成图片", "画一张", "做一个海报", image generation tasks, or any visual content creation request. This skill crafts optimized prompts based on user intent and then calls the Seedream API. Requires ARK_API_KEY environment variable.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 2 679 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Generate high-quality images using Seedream 4.5 API (Volcengine/火山引擎). Supports text-to-image, image editing, multi-image fusion, and sequential image…

As a process B 65/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

GeneratorAI and agentsDesignWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
65/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. 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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 65/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 1 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 83 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2679 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

  • +4Description does not say when NOT to use the skill (false activations)
  • -226 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 491: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 83 items
  • +3Output format is stated explicitly
  • +4Has examples (12 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.