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BD doubao-image

使用字节跳动豆包 Doubao SeeDream 模型生成高质量图片。支持文生图、AI 绘图、插画创作等功能。

ClawHub Agent Skills author: yy756127197 v2.0.0 MIT-0 11 files · 2 scripts body ≈ 2 146 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
B
78/100
safety, quality, tests
Safety 60%
83
Quality 40%
70
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Exfiltration medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

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.

For the author

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

  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 · 5

✓ No critical or high findings

Medium and low: 5
  • medium Exfiltration net-credential-use OPTIMIZATION_REPORT.md:87
    Credential used in a network call (verify the destination is the intended service)
    curl -s -X POST "$API_URL" -H "Authorization: Bearer $ARK_API_KEY" -d "$BODY"
  • medium Dangerous commands cmd-shell-rc README.md:510
    Writes to a shell startup file
    echo 'export ARK_API_KEY="your_api_key"' >> ~/.bashrc
  • medium Dangerous commands cmd-shell-rc SKILL.md:122
    Writes to a shell startup file
    echo 'export ARK_API_KEY="your_key"' >> ~/.bashrc
  • low Secrets in code secret-password-literal OPTIMIZATION_REPORT.md:293
    Hard-coded password / key literal (may be an example) (placeholder value)
    api_key="sk-1…def"
    placeholder
  • low Exfiltration read-dotenv README.md:329
    Reads a .env file
    source .env

Files scanned: 11. 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 46/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (doubao-image) differs from the folder (yy756127197-doubao-image)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 100Steps. 72 steps
  • 100Execution cost. Instruction body is 2146 tokens
  • 100Running it twice. No mutating operations
  • low 17 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 55: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -238 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 51 headings
  • +3Step-by-step instructions: 72 items
  • +4Has examples (22 code blocks)
  • +3All 3 scripts are documented

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

External checks

ClawHub: suspicious
This image-generation skill has a coherent purpose, but it needs review because it can send prompts to a third-party API, partially expose API keys in diagnostics, and includes unsafe advice about bypassing content filters.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026