Real Skill packageSource verifiedClawHub registry

AI Observability（AI可观测性监控）

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。

Identity and source

Publisher attributionzhaoxinghua09-cellregistry owner unverified by skillvetai
Functional categoryDevOps, Cloud & Observabilityautomatically inferred · 63% rule confidence
Package forminstruction with code19 recorded files
Canonical sourceClawHub registryclawhub:zhaoxinghua09-cell:ai-observability
Open canonical source ↗

Platform declarations

These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.

OpenClawnative officialProvenance: registry distribution

Independent structural checks

These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.

Claude Codepasses structure
Checker 0.1.0 · agent-skills-2026-08-13+claude-code-docs-2026-08-13 · 8/27/2026.claude/skills/ai-observability

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

OpenAI Codexpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+codex-docs-2026-08-13 · 8/27/2026.agents/skills/ai-observability

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

OpenClawpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+openclaw-docs-2026-08-13 · 8/27/2026skills/ai-observability

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

Installation and inspection

This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.

clawhub install @zhaoxinghua09-cell/ai-observability
clawhub inspect @zhaoxinghua09-cell/ai-observability --version 1.0.0

Security evidence

SkillVetAI static result: no findings detected

This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.

Status
completed
Coverage
full text content
Files
18 / 19 inspected as text
Checked
8/27/2026, 10:18:59 PM
Scanner
0.1.3
Policy
1.0.3
3 inferred permission indicators
  • shell execution — automatically inferred
  • network access — automatically inferred
  • filesystem write — automatically inferred
2 dependency and API indicators
  • api: clawhub.ai
  • api: www.w3.org
External clawhub result: clean

This is registry-supplied evidence for the recorded release, not an independent SkillVetAI scan. Check the canonical source for the full report, scanner versions, scope, and current moderation state.

Evidence checked
8/27/2026, 5:28:37 PM
Release binding
Matches this record
  • vt: clean
  • skillspector: clean
  • llm: clean

Recorded files

The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.

Package content hashsha256:25fccb39f4a3062e1c7eeda26845a4b06fc1b0b5ff9ac201e42227d2e61aada1
Show up to 19 recorded paths
  • ATTESTATION.md
  • LICENSE.md
  • manifest.json
  • references/01-可观测性全景.md
  • references/02-调用追踪与日志.md
  • references/03-质量监控指标.md
  • references/04-性能与成本监控.md
  • references/05-护栏与安全监控.md
  • references/06-告警体系.md
  • references/07-平台与落地.md
  • references/08-FAQ.md
  • SECURITY_AUDIT.md
  • skill-card.md
  • SKILL.md
  • tools/observability_toolkit.py
  • verify/gen_security_radar.py
  • verify/observability_quality_test.py
  • verify/security_results.json
  • verify/security-radar.svg

Source changelog

v1.0.0: hands-on playbook for AI application observability and production monitoring - three pillars plus AI-specific observation targets, call tracing and logging standards (span design, session-level tracing, sensitive-info filtering), quality monitoring metrics (hallucination rate, refusal rate, satisfaction, human-handoff), performance and cost monitoring (TTFT/TPS/latency, token cost real-time), guardrail and security monitoring (hit rate, injection detection, data leakage), tiered alerting with threshold design and alert-fatigue governance, platform adoption (OpenTelemetry/Prometheus/Grafana, instrumentation standards, canary monitoring); 5-command zero-dependency toolkit