AF alibabacloud-mining-attack-diagnosis
Alibaba Cloud Security Center cryptomining (cryptojacking) diagnosis skill. Use when the user reports or suspects a mining infection, receives a mining alert, or asks for mining alert detection, IOC extraction (pool IPs, malicious domains, sample hashes, malicious processes), affected-asset scoping, attack-surface analysis, entry-vector analysis, risk assessment, prioritized remediation reporting, cryptomining incident response, or mining risk detection. Triggers: "mining", "cryptomining", "cryptojacking", "coin mining", "coin-mining", "miner", "mining alert", "mining alerts", "mining pool", "pool IPs", "malicious domains", "sample hashes", "malicious processes", "mining IOC", "IOC extraction", "mining alert detection", "affected-asset scoping", "attack-surface analysis", "entry-vector analysis", "risk assessment", "prioritized remediation reporting", "cryptomining incident response", "mining risk detection", "xmrig", "kdevtmpfsi", "kinsing", "sysrv", "minerd", "Security Center", "ECS high CPU mining"
Alibaba Cloud Security Center cryptomining (cryptojacking) diagnosis skill.
As a process F 67/100 · Will not run — References files that are not bundled: scripts/query_*.py
How to improve
- The text references files that are not there: add them or drop the references.
- 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-securityreferences/module3_affected_assets.md:6Offensive-security / dual-use content (legitimate for authorised testing; review intended use)whether the miner is spreading (worm-style lateral movement).
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low Risky intent
intent-offensive-securityreferences/module3_affected_assets.md:48Offensive-security / dual-use content (legitimate for authorised testing; review intended use)- **>1 asset affected** → possible lateral movement / worm (e.g. sysrv, kinsing
Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: scripts/query_*.py
Process rating: all ten parameters 67/100
- 0Tools and files. 1 referenced file(s) missing: scripts/query_*.py
- 30Running it twice. 3 mutating operations with no state check
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 25 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2776 tokens
- 100Progress reporting. Reports progress
- 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
- low The response is described with custom markup (5 tags): a typed call is more reliable
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)
- +3Description length 1016: 120–800 characters recommended
- -36 of 9 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 27 example trigger phrases
- +4Structure: 14 headings
- +3Step-by-step instructions: 25 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
- +4Reference files are cited in the instructions (8 of 10)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.