Laporan audit skill
agent-observability-eval-pipeline Laporan audit.
End-to-end Agent Observability pipeline for an instrumented ml_app — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", "onboard me to datasets and experiments", "walk me through experiments", "I have an ml_app, now what", "Agent Observability onboarding", "guided experiment setup", "from traces to experiments", or wants a deterministic, narrated tour from produ
Trust Score OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adopsi GitHub
Info62
158 star GitHub
Aktivitas star/fork
Peringatan57
158 star dan 25 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Pemeliharaan terbaru
Lulus88
2 bulan sejak push
Kejelasan lisensi
Lulus86
MIT
Kelengkapan README/SKILL.md
Lulus86
Metadata memuat konteks penggunaan dan alur kerja yang cukup
Risiko dependensi/runtime
Peringatan46
command execution surface, credential or environment access
Ketersediaan pemasangan
Lulus92
npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline
Keamanan perintah pemasangan
Info68
dynamic command execution, standard package or runtime install path
Cakupan izin
Gagal22
secrets or environment access, shell or command execution
Bukti repositori
Lulus86
https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline
Status peninjauan
Info66
Data tinjauan AI tersedia
Hasil terbukti Agent
Info54
Belum ada data hasil Agent
Pemeriksaan
Tinjauan pemasangan dan adopsi
Jalur pemasangan
92
npx skills add datadog-labs/agent-skills --skill agent-observability-eval-pipeline
Repositori
88
https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-eval-pipeline
Lisensi
86
MIT
Pemeliharaan
88
2 bulan sejak push
Tinjauan AI
55
The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.
Kelengkapan README/SKILL.md
86
Usable description available
Risiko dependensi
46
command execution surface, credential or environment access
Keamanan perintah pemasangan
68
dynamic command execution, standard package or runtime install path
Cakupan izin
22
secrets or environment access, shell or command execution
Aktivitas star/fork
57
158 star dan 25 fork; aktivitas issue tidak tersedia dalam metadata saat ini
Adopsi
68
158 star GitHub
Peringatan
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- The skill invokes sub-skills (agent-observability-session-classify, etc.) that are not included in this repository; they must be separately installed for the pipeline to work.
- Phase 5 executes a generated Python script. While this is a core feature, ensure that any user-supplied data (e.g., dataset content) cannot inject unintended code or commands into the generated script.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 158 stars, 25 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Metode
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.
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