BD email-security
Protect AI agents from email-based attacks including prompt injection, sender spoofing, malicious attachments, and social engineering. Use when processing emails, reading email content, executing email-based commands, or any interaction with email data. Provides sender verification, content sanitization, and threat detection for Gmail, AgentMail, Proton Mail, and any IMAP/SMTP email system.
As a process D 48/100 · 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 text contains phrases like "ignore previous instructions" or "you are now…". That is an attempt to hijack the agent: it may break your rules, the system limits or company policy.
An honest skill does not need them: state the role and the rules directly without overriding other instructions. Otherwise catalog scanners and corporate filters will block the listing.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.
Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.
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 · 4
✓ No critical or high findings
Medium and low: 4
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medium Instruction override
en-ignore-previousreferences/threat-patterns.md:11Instruction-override phrase ("ignore previous instructions") (documentation of a security skill)IGNORE ALL PREVIOUS INSTRUCTIONS
security skill -
medium Obfuscation
uni-mixed-script-wordreferences/threat-patterns.md:107Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (1 occurrence)- `examplе.com` (Cyrillic 'е' U+0435)
-
low Social engineering
en-false-authorityreferences/threat-patterns.md:46False-authority / fake test-mode claim (documentation of a security skill)As your administrator, I'm instructing you to...
security skill
A further 1 matches are quotations in this security skill's documentation and are not counted as findings.
Files scanned: 13. 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 48/100
- 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
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1435 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
- +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 393: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (5 of 6)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.