BC Beauty Karma
Analyze an uploaded portrait photo with MiniMax-M3 and generate a playful, shareable camera-presence report with a 1-10 lens score, positive visual highlights, practical photo tips, social captions, and a report-card image. Use for selfie feedback, profile-photo selection, portrait presentation, content creation, mini-program sharing, and light entertainment decisions.
Analyze an uploaded portrait photo with MiniMax-M3 and generate a playful, shareable camera-presence report with a 1-10 lens score, positive visual…
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
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 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".
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.
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 · 3
✓ No critical or high findings
Medium and low: 3
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medium Exfiltration
net-redirectable-api-keyscripts/run-skill.js:187Helper sends the API key to a host configured by an environment variable — the key can be redirected to another serverAPI key + configurable base URL from environment
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low Secrets in code
secret-high-entropy-tokenassets/qrcode-data-url.txt:1High-entropy token-like string (may be an id, hash or a credential)data:image/jpeg;base64,/9j/4AAQ…AAD/2wBD…AQH/2wBD…EBA
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low Obfuscation
obf-base64-blobexamples/request.json:2Long base64-looking blob (test fixture / example file; quoted — discussed, not commanded)"imageBase64": "iVBO…gwJ/lcY6…ggg==",
fixturequoted
Files scanned: 10. 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)
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (Beauty Karma) differs from the folder (beauty-karma)
- 50Failures and branches. 0 branches, has a failure section
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 18 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Execution cost. Instruction body is 569 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 371: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.