ai-assist-observability-audit

REVIEW · 60
Registry indexed

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
Version1.0.0
Quality61/100 · Promising
Trust60/100 · Sandbox only
Audit74/100 · Needs review

Supply asset profile

Research and knowledge work

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

Browse track

Scenario

Research agents

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

Agent fit

Claude Code + CLI + Codex

Codex, Claude Code, Cursor, CLI, or custom agents.

Install

Ready

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

Maintenance

fresh

2d since push

Risk

Needs review

License is unclear

GitHub quality

88

61/100 Quality · 68/100 Trust

Coverage tags

ResearchResearch agentssecurityagent-skill

Review notes

License is unclear · Financial research output is not financial advice; require human review before any live investment decision

Agent adoption scorecard

Trust, audit, and install readiness at a glance

These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.

Quality

Promising
61

Useful candidate, but compare it with alternatives before adopting.

Trust

Sandbox only
60

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

Audit

Needs review
74

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

OpenAgentSkill Trust Score v5

Human review before install

Run only in a sandbox and compare close alternatives before using it for real work.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

88 GitHub stars

Repo activity

88 stars, 12 forks

Maintenance

2d since push

License

Unknown

Install

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

Install safety

standard package or runtime install path

Permission surface

filesystem or document access

Agent outcomes

No agent outcome data yet

Docs

Usable metadata, review docs

Risk summary

Review before production

  • 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.
  • License is unclear
  • Quality score needs review

Install readiness

Install path available

  • Install path is available
  • Repository evidence is available
  • License is unclear
  • No Agent Proven outcome evidence yet

Agent-readable metadata

Machine-readable decision data for this skill.

Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.

Open JSON

Suited tasks

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects
  • Search sources

Suited agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Install decision

Command
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-observability-audit
Policy
review
Human review
yes

Trust and risk

Trust
60/100
Audit
74/100
Risk level
Needs review

Outcome loop

Endpoint
/api/agent/outcome
Event ID
resolve
Outcomes
5

Install command

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

Do not use when

  • teams that need a vendor-supported SLA
  • production agents without a repository review
  • Repository license is unknown; missing license clarity makes compliance evaluation difficult.
  • License is unclear
  • Financial research output is not financial advice; require human review before any live investment decision

Agent safety v2

58/100 · Review before install

Reviewed with permission notesreview

Usable candidate, but the agent should surface permission and audit notes before installation.

Require human approval before installing into a real workspace.

Resolve via API

medium

Network access

Skill likely fetches remote pages, APIs, repositories, or external services.

medium

Filesystem access

Skill may read or write project files, documents, generated artifacts, or local workspace state.

  • License is unclear

Install targets

Install this skill in your agent workflow

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

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 resolve plan

Let an agent verify fit before installing.

The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.

Open text plan

Agent should check

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copy prompt

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 handoff

Give an agent the install path, not another directory page.

Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.

Open install API

Agent prompt

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 metadata

Agent-readable profile for automatic skill selection.

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open manifest

Agent fit

63/100

Research agents

Platforms

Claude Code

Audit report

Needs review · 74/100

A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.

View audit reportView eval report

Agent decision cockpit

Fallback candidate for Research agents

Prototype with this skill first; keep a fallback candidate ready.

63
Readiness
Prototype
Stage

Role in stack

Fallback candidate

Primary fit

Research agents

Trust label

Prototype first

Install path

Command ready

Use when

  • Research agents workflows
  • Claude Code teams
  • builders willing to evaluate younger projects

Evidence

  • recent repository activity
  • install command or GitHub repo available
  • 61/100 quality profile
  • 9 OpenAgentSkill engagement events

review first

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

Implementation path

  1. 1Install it in a sandbox agent and run one Research agents task end to end.
  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.

Trust profile

Sandbox only

Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.

60
OpenAgentSkill Trust Score

GitHub adoption

CHECK

88 GitHub stars

Stars/forks activity

CHECK

88 stars, 12 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

2d since push

License clarity

CHECK

Unknown

Good signals

  • AI review approved
  • Install path is available
  • Repository evidence is available
  • Recently maintained repository
  • Install command has no obvious high-risk pattern
  • Outcome loop is ready but needs first real agent run

Review before install

  • 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.
  • License is unclear
  • 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
  • No real agent outcome reports yet
  • Human review required before unattended installation

Recommended action

Run only in a sandbox and compare close alternatives before using it for real work.

Quality profile

Promising candidate for agent workflows

Useful candidate, but compare it with alternatives before adopting.

61
GitHub stars
88
Freshness
2d ago
Install ready
Yes
License
Unknown
Review before install: Repository license is unknown; missing license clarity makes compliance evaluation difficult.

Workflow fit

Use this skill in these scenarios

Workflow fit

Add it to a complete workflow

Alternative shortlist

Compare before you install

Similar skills that may fit this task.

Compare all

Overview

--- 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.

Technical details

Version
1.0.0
License
Unknown
Last updated
Aug 21, 2026
Published
Aug 21, 2026

Decision snapshot

Fallback candidate

63
Ready
Prototype
Stage

recent repository activity

Audit

Install review

Install and adoption review

74
Needs review
Security
75/100
Maintenance
100/100
Install
92/100
Open full auditView eval report

Agent-proven evidence

Agent-proven evidence

Outcome reports after resolve, review, install, and one narrow run.

0
Proven
Needs first agent runAuto-install: review firstLast: Unknown
Success rate
Recent failure
Outcomes
0
Output quality
Failed
0
Not relevant
0
Installs
0
Risk blocked
0
Setup needed
0
Production
0

No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.

Install

Add to agent workflow

Free and open source. Review the report before installing into production agents.

Growth loop

Share kit

X

Scenario-led draft for ai-assist-observability-audit, ready for a manual X post.

Curator note
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
Open X draft
Optional reply with install command
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

Listing source

Registry indexed

Claimable

This listing was indexed from public sources and is not marked official until a maintainer claim is approved.

Creator
jparkerweb
Indexed by
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Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

Claim this skill

Owner claim

Claim this skill listing

This Registry indexed listing is attributed to jparkerweb but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

Creator backlink kit

Add the evidence badges to your README

Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.

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Author

J

jparkerweb

@jparkerweb

Platform fit

Health signals

GitHub stars
88
Quality score
37/100
Last GitHub push
Aug 20, 2026
Framework hints
Unknown
OpenAgentSkill views
9
Install copies
0
Outbound clicks
0

Community signal

Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.

Trust & safety

Sandbox only

60
  • GitHub adoption88 GitHub starsCHECK
  • Stars/forks activity88 stars, 12 forks; issue activity unavailable in current metadataCHECK
  • Recent maintenance2d since pushPASS
  • License clarityUnknownCHECK
  • README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
  • Dependency/runtime riskno major dependency risk hints in public metadataPASS