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元情 —— 跨智能体的威胁情报 IOC 提取与规范化技能:零依赖自研从文本 / 日志 / 报告中提取 IP(IPv4/IPv6)、域名、URL、邮箱、哈希(MD5/SHA1/SHA256/SHA512)与 CVE 编号,识别并还原 defang 写法,去重、归一化后输出 CSV / JSON / STIX-lite。触发:用户给出含可疑 IP / 域名 / URL / 哈希的威胁情报文本、恶意样本分析报告、钓鱼邮件或日志,要提取 IOC、规范化、去重、转格式、共享情报时。边界:纯本地离线提取与规范化;不联网查证、不下载样本、不主动扫描任何系统;仅用于已获授权 / 自有资产 / 教学环境的安全分析。

ClawHub Agent Skills author: YottaMeta v0.2.0 MIT-0 16 files · 1 script body ≈ 958 tokens Open the sourceclawhub.ai analyzed 3 d ago

元情 —— 跨智能体的威胁情报 IOC 提取与规范化技能:零依赖自研从文本 / 日志 / 报告中提取 IP(IPv4/IPv6)、域名、URL、邮箱、哈希(MD5/SHA1/SHA256/SHA512)与 CVE 编号,识别并还原 defang 写法,去重、归一化后输出 CSV / JSON /…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureData and analyticsSecuritytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 29 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 958 tokens
  • 100Running it twice. No mutating operations
  • low 10 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)
  • +3Output format is not stated: the model decides each time
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 302: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 29 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
The skill's IOC extraction is coherent and local, but its installers can broadly modify agent skill directories and have unsafe overwrite/symlink handling that users should review before installing.
LLM: suspicious (high) · 9 Sept 2026