Skill audit report

annotate-traces-for-review Audit report.

Use this to set up human review and annotation of LLM traces, so people (often domain experts) can label outputs, do error analysis, and build a trustworthy golden dataset. Trigger on "review my LLM outputs", "have an expert label these", "error analysis", "annotate traces", "build a golden dataset", or when automated evals are not enough for a high-stakes or specialized domain. Looking at your data is the highest-ROI thing you can do.

REVIEWED · REVIEWNeeds reviewGenerated Nov 8, 2026Heuristic metadata audit

Audit

Trust

Quality

Security

Maintain

Install

OpenAgentSkill Trust Score

Strong shortlist

OpenAgentSkill Trust Score

The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.

GitHub adoption

WARN

33 GitHub stars

Stars/forks activity

WARN

33 stars, 18 forks; issue activity unavailable in current metadata

Recent maintenance

PASS

2mo since push

License clarity

PASS

CC0-1.0

README/SKILL.md completeness

INFO

Public metadata needs stronger README/SKILL.md context

Dependency/runtime risk

PASS

no major dependency risk hints in public metadata

Install availability

PASS

npx skills add ContextJet-ai/awesome-llm-observability --skill annotate-traces-for-review

Install command safety

PASS

standard package or runtime install path

Permission surface

PASS

database access

Repository evidence

PASS

https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/annotate-traces-for-review

Review status

WARN

AI review approval is missing

Agent Proven outcomes

INFO

No agent outcome data yet

Checks

Install and adoption review

8 Passed · 12 Needs review

Install path

PASS

npx skills add ContextJet-ai/awesome-llm-observability --skill annotate-traces-for-review

Repository

PASS

https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/annotate-traces-for-review

License

PASS

CC0-1.0

Maintenance

PASS

2mo since push

AI review

CHECK

Review approval is missing

README/SKILL.md completeness

PASS

Usable description available

Dependency risk

PASS

no major dependency risk hints in public metadata

Install command safety

PASS

standard package or runtime install path

Permission surface

PASS

database access

Stars/forks activity

FIX

33 stars, 18 forks; issue activity unavailable in current metadata

Adoption

CHECK

33 GitHub stars

Financial decision safety

CHECK

Research-only use: do not treat output as financial advice or execute a position without human approval.

Warnings

Financial research output is not financial advice; require human review before any live investment decision
Low GitHub adoption signal
AI review approval is missing
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
GitHub adoption: 33 GitHub stars
Stars/forks activity: 33 stars, 18 forks; issue activity unavailable in current metadata
Review status: AI review approval is missing

Method

This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.

Install path

Review the report before installing into production agents.

$ npx skills add ContextJet-ai/awesome-llm-observability --skill annotate-traces-for-review

Agent-readable metadata

Machine decision packet

Embedded JSON exposes suited tasks, suited agents, install command, trust score, safety gate, alternatives, and do-not-use conditions for agent review.

Install policy: review
Best agents: Codex + Claude Code
Alternatives: 3

Open agent JSON

Signals

Audit score: 74/100
Quality: 54/100
Trust: 75/100
Install safety: 92/100
Permission surface: 88/100
Maintenance: 2mo since push
Events: 7 events
GitHub stars: 33
Last push: Sep 7, 2026

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.

Financial research output is not financial advice; require human review before any live investment decision
58/100 agent safety score

Network access

medium

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

Database access

medium

Skill may inspect schemas, query databases, or work with persistent stores.

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

Back to skill Alternatives Audit badge SVG

Compare nearby options

Related skills to audit next

Last30days Skill

Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.

64K Stars · Audit report

Academic Research Skills

Academic Research Skills for Claude Code: research → write → review → revise → finalize

38K Stars · Audit report

mono-color

Generate original one-ink or controlled two-ink editorial images from any theme, sentence, article idea, object, or reference photo. Always use this skill when the user asks for 单色海报、双色印刷、单色调视觉、蓝色/绿色孔版印刷、risograph、网点照片、复古或当代编辑排版、zine poster, monochrome editorial poster, duotone print, or asks to use the mono-color style. It uses an adaptive white, gray, or pale-beige substrate, no more than two printing inks, active negative space, terse human language, and strong serif/grotesk/mono typography without making retro styling the default or copying a source composition, wording, logo, or artwork. Produce both the final generation prompt and the generated raster image unless the user explicitly asks for prompt only.

1.9K Stars · Audit report