BD Agnes画影
调用 Agnes AI 免费 API 生成图片/视频。从 AGNES_API_KEY 环境变量读取密钥。
调用 Agnes AI 免费 API 生成图片/视频。从 AGNESAPIKEY 环境变量读取密钥。
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.
- 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 · 3
✓ No critical or high findings
Medium and low: 3
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Read Write Bash AskUserQuestion
-
low Dangerous commands
cmd-shell-rcSKILL.md:56Writes to a shell startup file (documentation table row)| 4b | **配置命令**（据上一步结果选择）：<br>• Linux/macOS + **zsh** → `echo 'export AGNES_API_KEY="sk-..."' >> ~/.zshrc && source ~/.zshrc`<br>• Linux + **bash** → `echo 'export AGNES_API_KEY="sk-..."' >> ~/.bashrc
table -
low Exfiltration
net-credential-useSKILL.md:57Credential used in a network call (verify the destination is the intended service) (documentation table row)| 5 | 验证：`curl -s https://apihub.agnes-ai.com/v1/chat/completions -H "Content-Type: application/json" -H "Authorization: Bearer $AGNES_API_KEY" -d '{"model":"agne…ash","messages":[{"role":"usertable
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription 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
- 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
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (Agnes画影) differs from the folder (agnespaint)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 14 steps
- 100Execution cost. Instruction body is 559 tokens
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 52: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 13 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.