Laporan audit skill
agent-observability-replay-trace Laporan audit.
Use when a developer wants to iterate on ONE specific Agent Observability / LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as /agent-observability-replay-trace <trace-id> [changes to test]. Signals: "replay this trace"; "iterate on a trace"; "this trace's output is wrong, fix it and re-run"; "re-run trace <id> with <change>"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace / LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser "Replay" button (that's agent-observability-replay-experiment), building an experiment from a dataset/CSV, writing
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-replay-trace
Keamanan perintah pemasangan
Lulus92
Jalur pemasangan paket atau runtime standar
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-replay-trace
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-replay-trace
Repositori
88
https://github.com/datadog-labs/agent-skills/tree/main/agent-observability/agent-observability-replay-trace
Lisensi
86
MIT
Pemeliharaan
88
2 bulan sejak push
Tinjauan AI
55
The provided excerpt is incomplete; full SKILL.md and references were not fully reviewed, but the visible content is consistent and detailed.
Kelengkapan README/SKILL.md
86
Usable description available
Risiko dependensi
46
command execution surface, credential or environment access
Keamanan perintah pemasangan
92
Jalur pemasangan paket atau runtime standar
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
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Peringatan
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- The provided excerpt is incomplete; full SKILL.md and references were not fully reviewed, but the visible content is consistent and detailed.
- The parsed metadata description shows '>-' which appears to be a YAML folding artifact, not a content defect.
- Financial research output is not financial advice; require human review before any live investment decision.
- 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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