ai-assist-observability-audit

审查 · 60
已收录

17-dimension observability audit with tier activation, health scoring, and cost analysis. Covers logging, metrics, tracing, alerting, SLOs, profiling, security observability, and developer experience. Use when assessing observability posture, identifying telemetry gaps, or optimi

Verified installs0
Stars88
版本1.0.0
质量61/100 · 有潜力
信任60/100 · 仅限沙盒
审计74/100 · 需审查

供给资产档案

研究与知识工作

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

浏览赛道

场景

研究 Agent

I need my agent to research a topic, compare sources, and produce a concise report.

适配 Agent

Claude Code + CLI + Codex

适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。

安装

就绪

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit

维护状态

新鲜

距上次推送 2 天

风险

需审查

许可证不清晰

GitHub 质量

88

61/100 质量 · 68/100 信任

覆盖标签

研究研究 Agent安全agent-skill

审查说明

许可证不清晰 · Financial research output is not financial advice; require human review before any live investment decision

Agent 采用评分卡

一眼查看信任、审计与安装准备度

这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。

质量

有潜力
61

有用的候选项,但采用前应与替代方案比较。

信任

仅限沙盒
60

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

审计

需审查
74

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

OpenAgentSkill 信任评分 v5

安装前需人工审查

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

88 个 GitHub Stars

仓库活跃度

88 个 Star,12 个 Fork

维护状态

距上次推送 2 天

许可证

未知

安装

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit

安装安全性

标准软件包或运行时安装路径

权限范围

文件系统或文档访问

Agent 结果

暂未有 Agent 结果数据

文档

Usable metadata, review docs

风险摘要

生产前审查

  • Repository license is unknown; missing license clarity makes compliance evaluation difficult.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • 许可证不清晰
  • Quality score needs review

安装准备度

安装路径可用

  • 安装路径可用
  • 仓库证据可用
  • 许可证不清晰
  • 暂无 Agent 验证结果证据

Agent 可读元数据

这个 Skill 的机器可读决策数据。

使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。

打开 JSON

适用任务

  • 研究 Agent 工作流
  • Claude Code 团队
  • builders willing to evaluate younger projects
  • 检索来源

适用 Agent

CodexClaude CodeCursorOpenAgentSkill CLICLI

安装决策

命令
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit
策略
审查
人工审查

信任与风险

信任
60/100
审计
74/100
风险级别
需审查

结果闭环

端点
/api/agent/outcome
事件 ID
resolve
结果
5

安装命令

npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit

不适用场景

  • 需要厂商支持 SLA 的团队
  • production agents without a repository review
  • Repository license is unknown; missing license clarity makes compliance evaluation difficult.
  • 许可证不清晰
  • Financial research output is not financial advice; require human review before any live investment decision

Agent 安全 v2

58/100 · 安装前审查

已审查并附权限说明审查

可用候选,但 Agent 在安装前应展示权限与审计说明。

在真实工作区安装前需要人工批准。

通过 API 解析

网络访问

Skill 可能访问远程页面、API、仓库或外部服务。

文件系统访问

Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。

  • 许可证不清晰

安装目标

在你的 Agent 工作流中安装此 Skill

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

skill install

OpenAgentSkill CLI

Resolve policy, run the source installer safely, and report a verified install receipt.

$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install jparkerweb-ai-assist-observability-audit

Agent 解析计划

让 Agent 在安装前验证匹配度。

Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。

打开文本计划

Agent 应检查

  • 从 Resolve API 检查任务匹配与替代方案。
  • 检查审计评分、信任评分和安全策略警告。
  • 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。

复制提示词

Task: Use ai-assist-observability-audit in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-observability-audit%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-observability-audit/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent 交接

把安装路径交给 Agent,而不是再给一个目录页。

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

打开安装 API

Agent 提示词

Use ai-assist-observability-audit for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-observability-audit/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit

Registry 元数据

用于自动选择 Skill 的 Agent 可读档案。

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。

打开 Manifest

适配 Agent

63/100

研究 Agent

平台

Claude Code

审计报告

需审查 · 74/100

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

查看审计报告查看评估报告

Agent 决策面板

Fallback candidate for Research agents

先用此 Skill 做原型验证,并保留备选方案。

63
就绪度
原型验证
阶段

栈中角色

备选候选

主要匹配

研究 Agent

信任标签

先做原型验证

安装路径

命令已就绪

适用场景

  • 研究 Agent 工作流
  • Claude Code 团队
  • builders willing to evaluate younger projects

证据

  • 仓库近期活跃
  • 已提供安装命令或 GitHub 仓库
  • 61/100 质量档案
  • 9 个 OpenAgentSkill 交互事件

先审查

  • Repository license is unknown; missing license clarity makes compliance evaluation difficult.

实施路径

  1. 1在沙盒 Agent 中安装它,并端到端完成一次研究 Agent任务。
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

信任档案

仅限沙盒

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

60
OpenAgentSkill 信任评分

GitHub 采用度

检查

88 个 GitHub Stars

Star/Fork 活跃度

检查

88 个 Star,12 个 Fork; 当前元数据中没有议题活跃度信息

近期维护

通过

距上次推送 2 天

许可证清晰度

检查

未知

积极信号

  • AI 审查已通过
  • 安装路径可用
  • 仓库证据可用
  • 近期维护的仓库
  • 安装命令未发现明显高风险模式
  • 结果闭环已就绪,但需要首次真实 Agent 运行

安装前审查

  • Repository license is unknown; missing license clarity makes compliance evaluation difficult.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • 许可证不清晰
  • Quality score needs review
  • GitHub adoption: 88 GitHub stars
  • Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
  • License clarity: Unknown
  • 暂未有真实 Agent 结果报告
  • 无人值守安装前需要人工审查

建议操作

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

质量档案

有潜力 适用于 Agent 工作流的候选

有用的候选项,但采用前应与替代方案比较。

61
GitHub Stars
88
新鲜度
2 天前
安装就绪
许可证
未知
安装前审查: Repository license is unknown; missing license clarity makes compliance evaluation difficult.

工作流匹配

在这些场景使用此 Skill

工作流匹配

加入完整工作流

替代方案短名单

安装前对比

可能适合该任务的相近 Skill。

对比全部

概览

--- name: ai-assist-observability-audit description: "17-dimension observability audit with tier activation, health scoring, and cost analysis. Covers logging, metrics, tracing, alerting, SLOs, profiling, security observability, and developer experience. Use when assessing observability posture, identifying telemetry gaps, or optimizing observability costs." argument-hint: "[dimension or scope]" ---

# OBSERVABILITY AUDIT

**Objective:** Produce a tier-activated, cost-aware observability posture assessment with health score and prioritized improvement plan across 17 dimensions. **When to use:** Assessing observability posture, identifying telemetry gaps, auditing cost efficiency, preparing for production readiness, optimizing observability spend.

Start all responses with '📡 [Obs Audit Step X: Name]'

## Role

Senior observability engineer auditing 17 dimensions — foundational telemetry, operational readiness, security observability, cost governance, and developer experience. Ensure exactly the right amount of observability: not more (waste), not less (blind spots).

## Context

**AGENTS.md check:** If `./AGENTS.md` exists, read it for observability-relevant conventions and deployment patterns. If missing, warn and proceed with standard practices.

**Spec awareness:** If `specs/` has active work, verify observability changes don't conflict with in-progress implementation.

**Stack detection:** Detect from imports/configs: logging, metrics, tracing, APM vendor, profiling, service mesh, MQ, databases. Research best practices and cost models for detected stack.

**Input:** `$ARGUMENTS` — optional dimension name/group and scope (directory, service, or "full"). Default: full audit, all activated dimensions.

## Rules

1. **Observability has real cost.** Every log, metric, trace costs money — evaluate cost/benefit for every finding. 2. **Log levels are a cost lever.** Production WARN+. DEBUG/INFO only in dev or behind dynamic flag. 3. **Cardinality kills budgets.** Calculate label products (e.g., 1K x 20 x 10 x 3 = 600K series). Flag high-cardinality. 4. **Traces should be sampled.** Head/tail-based sampling per traffic volume. 100% sampling in prod is almost always wrong. 5. **Sensitive data in telemetry is ALWAYS Critical.** PII/credentials/tokens in logs, traces, labels — no exceptions, no downgrades. 6. **Structured logs only.** JSON/logfmt, one line per event. Unstructured logging is a finding. 7. **Gaps as important as waste.** Missing observability on critical paths = incident response failures. 8. **Tier activation mandatory.** Match dimensions to detected project tier — never audit non-applicable dimensions. 9. **Standards are the benchmark.** Research current versions of OpenTelemetry, Prometheus, OpenSLO, DORA, NIST logging guidance, OpenCost at audit time. Never assume a specific version is current. 10. **Cross-cutting cost analysis mandatory.** Dedicated cost step across ALL telemetry types — not optional. 11. **Alert-readiness matters.** Observability without actionable alerts is data hoarding. 12. **Chat-only output.** Present ALL findings in chat. Never create files without explicit user permission.

## Process

### Step 1: Context & Stack Detection

1. Read AGENTS.md, run `git status`, detect stack from imports and configs 2. Detect: logging framework, metrics library, tracing SDK, APM vendor, profiling tools, message queues, databases, service mesh 3. Research best practices and cost models for detected stack; parse arguments for focus/scope

> 📡 [Obs Audit Step 1: Context & Stack Detection] Stack: [logging] + [metrics] + [tracing]. Vendor: [APM]. Tier: [tier]. Conditional: [none/MQ/DB].

### Step 2: Tier Activation & Audit

Read `references/dimensions.md` for the tier activation table, tier detection signals, and per-dimension check definitions.

1. Classify project tier using detection signals from dimensions.md 2. Build activated dimension list based on tier 3. Audit each activated dimension in order: UNIVERSAL, SERVICE, DISTRIBUTED, Conditional

> 📡 [Obs Audit Step 2: Tier Activation & Audit] Tier: [TIER]. Active: [N]/17. Maturity: [Foundation/Advanced].

### Step 3: Cost Analysis (Cross-Cutting)

Read `references/scoring.md` for the cost analysis framework, vendor rate ranges, and estimation methodology.

1. Aggregate costs across logging, metrics, tracing, profiling, infrastructure 2. Identify top 5 highest-cost sources with file:line references 3. Recommend: log level changes, label reduction, sampling adjustments, retention tiering 4. Present before/after estimates where data supports it

### Step 4: Findings Report & Score

Read `references/scoring.md` for health score calculation, group weights, and severity definitions.

Read `references/output-template.md` for finding format, summary table, positive observations, improvement plan, fix options, and session-end format.

1. Calculate health score using group weights and N/A redistribution 2. Rank findings by severity (Critical → Warning → Suggestion) 3. Present: stack summary, dimension findings with evidence, summary table, positive observations (3-5), health score, improvement plan (P1/P2/P3 with cost impact), fix options

### Self-Verification Checklist

> Canonical version in `references/output-template.md`. Brief version here for quick reference.

- [ ] All activated dimensions audited; N/A documented - [ ] Tier activation justified with codebase signals - [ ] Cardinality cost analysis for all custom metrics with labels - [ ] Sensitive data scan: logs, trace attributes, metric labels - [ ] Gap analysis: missing observability on critical paths - [ ] Cross-cutting cost analysis across ALL telemetry types - [ ] Every finding has file:line and cost impact where applicable

### Session End

``` 📡 [Obs Audit Complete]

**Score:** [XX]/100. Tier: [tier]. Dims: [N]/17. Cost impact: [summary]. ```

**Next steps (ask user — do not auto-execute):** - Save report to `specs/audit-reports/obs-audit-<date>.md`? - Implement fixes? (by priority) - Related: `/ai-assist-security-audit`, `/ai-assist-tech-debt`, `/ai-assist-test-audit`

## Recovery

| Issue | Solution | |-------|----------| | No observability stack detected | Critical gap; recommend stack for project type and language | | Cannot estimate costs without vendor info | Report cardinality/volume without dollar amounts; note limitation | | Microservices with different stacks | Audit each separately; aggregate in summary | | No production config visible | Audit code patterns; note limitation | | Tier unclear | Default SERVICE; note ambiguity | | Too many dimensions for context | Prioritize Telemetry Foundation + Sensitive Data |

## Important Reminders

**Response format:** Every response starts with `📡 [Obs Audit Step X: Name]`

**Hard rules:** Observability has real cost. Sensitive data in telemetry is ALWAYS Critical. Cardinality: always calculate series count. Tier activation mandatory.

**Process rules:** Cost analysis mandatory and cross-cutting. Gaps as important as waste. Standards: OpenTelemetry, Prometheus, OpenSLO, DORA, NIST logging guidance, OpenCost — research current versions at runtime.

**Related:** `/ai-assist-security-audit` for security posture, `/ai-assist-tech-debt` for codebase health, `/ai-assist-test-audit` for test coverage gaps.

技术详情

版本
1.0.0
许可证
Unknown
最近更新
2026年8月21日
发布时间
2026年8月21日

决策摘要

备选候选

63
就绪
原型验证
阶段

仓库近期活跃

审计

安装审查

安装与采用审查

74
需审查
安全性
75/100
维护状态
100/100
安装
92/100
打开完整审计查看评估报告

Agent 验证证据

Agent 验证证据

来自解析、审查、安装和一次小范围运行后的结果报告。

0
已验证
Needs first agent run自动安装: 先审查最近: 未知
成功率
近期失败
结果
0
输出质量
失败
0
不相关
0
安装次数
0
风险拦截
0
需要配置
0
生产环境
0

暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。

安装

加入 Agent 工作流

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增长闭环

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X

为 ai-assist-observability-audit 准备的场景化草稿,可手动发布到 X。

策展说明
ai-assist-observability-audit: 17-dimension observability audit with tier activation, health scoring, and cost analysis. Cov...

88 stars

https://www.openagentskill.com/skills/jparkerweb-ai-assist-observability-audit?ref=x
打开 X 草稿
可选:带安装命令的回复
Listing + install path for ai-assist-observability-audit:
https://www.openagentskill.com/skills/jparkerweb-ai-assist-observability-audit?ref=x

Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit
打开回复草稿

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Registry 收录

可认领

此列表来自公开来源,维护者认领获批前不会标记为官方。

创作者
jparkerweb
收录方
OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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所有者认领

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这条 Registry 收录 列表归属于 jparkerweb,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。

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将证据徽章加入你的 README

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/jparkerweb-ai-assist-observability-audit?metric=listed&label=Listed)](https://www.openagentskill.com/skills/jparkerweb-ai-assist-observability-audit)
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作者

J

jparkerweb

@jparkerweb

平台适配

健康信号

GitHub Stars
88
质量评分
37/100
最近 GitHub 推送
2026年8月20日
框架提示
未知
OpenAgentSkill 浏览量
9
复制安装命令
0
跳转点击
0

社区信号

告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。

信任与安全

仅限沙盒

60
  • GitHub 采用度88 个 GitHub Stars检查
  • Star/Fork 活跃度88 个 Star,12 个 Fork; 当前元数据中没有议题活跃度信息检查
  • 近期维护距上次推送 2 天通过
  • 许可证清晰度未知检查
  • README/SKILL.md 完整度公开元数据需要更完整的 README/SKILL.md 上下文信息
  • 依赖与运行时风险公开元数据中未发现主要依赖风险提示通过