AB muki-fingerprint
MUKI asset fingerprinting tool for red team reconnaissance. Use when performing authorized penetration testing, asset discovery, service fingerprinting, vulnerability scanning, and attack surface mapping. Supports active/passive fingerprinting with 30,000+ signatures, sensitive path detection, and sensitive information extraction. Requires explicit authorization for target systems.
MUKI asset fingerprinting tool for red team reconnaissance.
As a process B 69/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting
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 · 2
✓ No critical or high findings
Medium and low: 2
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low Risky intent
intent-offensive-securitySKILL.md:3Offensive-security / dual-use content (legitimate for authorised testing; review intended use)description: MUKI asset fingerprinting tool for red team reconnaissance. Use when performing authorized penetration testing, asset discovery, service fingerprinting, vulnerability scanning, and attack
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low Risky intent
intent-offensive-securitySKILL.md:19Offensive-security / dual-use content (legitimate for authorised testing; review intended use)MUKI is an active asset fingerprinting tool built for red team operations. It enables security researchers to rapidly pinpoint vulnerable systems from chaotic C-class segments and massive asset lists.
Files scanned: 5. 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 69/100
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 68 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1635 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 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)
- -2localhost URLs: will not work for another user
- -41 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 384: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 68 items
- +3Output format is stated explicitly
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.