AC osint-personal-profiler
开源情报个人画像收集器，基于OSINT方法论从网络公开信息中系统性地收集、关联、推断任意个人全维度画像的完整能力。核心能力：①目标发现与源映射（标识符强度分级消歧→8级搜索查询模板[精确搜索/用户名派生/深搜已有源/反向图片/缓存]→优先级排序）②12维逐维收集（实体层维1-6直接从公开信息提取+工商记录交叉验证、社会层维7-8通过社交图谱分析构建、抽象层维9-12通过行为模式分析+简历文本分析+职业轨迹模式+薪资市场映射推断）③跨源关联与置信度（去重→冲突解决→置信度评分→缺口识别，含5类缺口原因+零结果情报）④推断引擎（基于写作风格/代码模式/简历文本模式/职业轨迹/薪资市场基准推断元操作/动机/动态）⑤法律合规守护（每步G守护前置、来源合法性标注、非法来源拒收）。7域23任务。覆盖10类25+信息源。触发词：OSINT画像、公开信息收集、个人信息挖掘、12维重建、人物背景调查、数字足迹分析、digital footprint profiling、social engineering defense、信息暴露面评估、个人信息安全审计、开源情报收集、osint profiler、meta-skill-system。
开源情报个人画像收集器，基于OSINT方法论从网络公开信息中系统性地收集、关联、推断任意个人全维度画像的完整能力。核心能力：①目标发现与源映射（标识符强度分级消歧→8级搜索查询模板[精确搜索/用户名派生/深搜已有源/反向图片/缓存]→优先级排序）②12维逐维收集（实体层维1-6直接从公开信息提取+工商记录交叉验证、社…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 95 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1381 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
- +1No license
- +2Single-language instructions
- +3Description length 516: enough signal without eating the budget
- +4Structure: 38 headings
- +3Step-by-step instructions: 95 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (2 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.