BC security-radar
安全情报雷达为 AI Agent 提供智能化的漏洞与威胁情报订阅能力。它聚合 NVD CVE、GitHub Security Advisory、社区恶意技能通报等多源数据，并按资产关联度与可利用性双重排序，把每天数十上百条告警压缩到只剩必须处理的两三条。 核心能力：多源情报聚合（CVE/GHSA/恶意技能）、资产清单自动关联、可利用性优先级评分、增量去重推送、离线降级与缓存、严格速率限制。 适用场景：Agent 心跳巡检、CI 流水线依赖扫描、技能市场安全门禁、个人开发者漏洞订阅、团队安全日报生成。 差异化：相比只做"下载 feed 并展示"的原始方案，本技能新增资产关联过滤（只推送影响已安装技能/依赖的告警）、双维度优先级矩阵（严重度×可利用性）、增量状态机（避免重复推送）、离线缓存降级（网络故障时用上次快照）、以及分级通知策略（critical 即时推送、low 静默归档）。 触发关键词：安全, 漏洞, CVE, 情报, 告警, 订阅, advisory, vulnerability, threat, security, radar, feed
安全情报雷达为 AI Agent 提供智能化的漏洞与威胁情报订阅能力。它聚合 NVD CVE、GitHub Security Advisory、社区恶意技能通报等多源数据，并按资产关联度与可利用性双重排序，把每天数十上百条告警压缩到只剩必须处理的两三条。…
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: 0. 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") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "tools"
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. Tools declared in frontmatter
- 100Steps. 24 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2334 tokens
- 100Running it twice. No mutating operations
- low 17 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
- +2Single-language instructions
- +3Description length 485: enough signal without eating the budget
- +4Structure: 34 headings
- +3Step-by-step instructions: 24 items
- +4Has examples (18 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.