BD ai-observability
AI 应用可观测性与生产监控实操手册——AI 进生产后的"仪表盘与探照灯"：可观测性全景（三大支柱：日志/指标/追踪 + AI 特有观测对象）、调用追踪与日志规范（LLM 调用追踪、Span 设计、会话级追踪、敏感信息过滤）、质量监控指标（幻觉率/拒答率/满意度/转人工率实时看板）、性能与成本监控（延迟/吞吐/Token 成本实时追踪）、护栏与安全监控（护栏命中率/注入检测/敏感数据泄漏监控）、告警体系（分级告警/阈值设计/通知路由/告警疲劳治理）、监控平台与落地（埋点规范/工具选型/灰度期监控）。附零依赖本地工具一键出三大支柱清单、监控指标表、告警设计、追踪规范与落地路线。面向 AI 平台、SRE、运维与质量负责人——与 LLM 评测（离线质量）互补，本技能管线上运行质量。
AI 应用可观测性与生产监控实操手册——AI 进生产后的"仪表盘与探照灯"：可观测性全景（三大支柱：日志/指标/追踪 + AI 特有观测对象）、调用追踪与日志规范（LLM 调用追踪、Span…
As a process D 46/100 · Unfinished process — 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.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 18. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 342 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "display_name" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "title" - note
frontmatter-keyunknown frontmatter key "description_en"
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. 15 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 537 tokens
- 100Running it twice. No mutating operations
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 342: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 15 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.