AB mailscope-email-detection
Email security detection and analysis. Use this skill whenever the user wants to analyze, scan, or check the security of an email (.eml) file. This includes phishing detection, spoofing analysis, malicious attachment scanning, and general email threat assessment. Also use this skill when the user wants to configure their Mailscope API key (e.g. "set my api key", "configure the key", "here is my api key", "帮我配置 key"). Trigger when the user says things like "analyze this email", "check if this email is safe", "scan this .eml file", "is this phishing?", or provides a path to an .eml file and asks about its safety.
Email security detection and analysis.
As a process B 67/100 · Nearly there — weak spots: result and completion, 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 67/100
- 0Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (mailscope-email-detection) differs from the folder (mailscope-email-detection-skill)
- 60Tools and files. Uses tools (node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 23 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Execution cost. Instruction body is 839 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 8 example trigger phrases
- +3Description length 618: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.