skills.rest
drewid74
drewid74COMMUNITY
Explore all skills by this author →
GitHub

karpathy-trace-infrastructure

Audit agent trace infrastructure for meta-agent auto-improvement readiness.

8|1|Updated Mar 30, 2026
Search Tags:#agent observability#session replay#reasoning traces#evaluation harness#trace auditing#tool call logging#meta-agent readiness
One-click install
npx skills add https://github.com/drewid74/ai_skills --skill karpathy-trace-infrastructure
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: karpathy-trace-infrastructure
Source: https://github.com/drewid74/ai_skills/tree/main/karpathy-trace-infrastructure
Command: npx skills add https://github.com/drewid74/ai_skills --skill karpathy-trace-infrastructure

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you determine whether your agent observability and trace logging are sufficient for a meta-agent to diagnose failures, replay sessions, and make targeted improvements safely.

Core Features & Use Cases

  • Trace Infrastructure Audit: Evaluates your current setup against requirements like reasoning-trace completeness, tool-call granularity, and decision-point visibility.
  • Auto-Improvement Readiness Assessment: Produces a structured audit that labels each capability as Present, Partial, or Absent and explains what breaks when something is missing.
  • Actionable Remediation Planning: Identifies critical vs. partial gaps and provides a readiness verdict plus a single highest-impact next action.

Quick Start

Use this Skill to audit whether your agent harness and logging capture structured, replayable reasoning and tool-call traces by asking it to run the “Trace Infrastructure Audit” workflow for your current agent deployment.

Frequently Asked Questions about karpathy-trace-infrastructure

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit agent observability to check readiness for auto-improvement?▼

Agent trace infrastructure requires structured logging of reasoning traces, tool calls, decision points, and per-step cost or latency metrics to enable session replay and failure classification for meta-agent auto-improvement.

What is needed in trace logging for reliable session replay?▼

Session replay requires trace infrastructure that captures complete reasoning chains, structured tool-call granularity, decision points, and harness version control linkage to ensure full session reproducibility.

How do I assess if my agent harness supports evaluation and failure classification?▼

Assess your harness by verifying trace completeness, structured logging, failure classification, and per-step cost and latency instrumentation to determine if evaluation support is sufficient for meta-agent readiness.

What happens if reasoning traces or tool-call logging are missing from my agent deployment?▼

When reasoning traces or tool-call logging are missing, session reproducibility and failure diagnosis break, which prevents a meta-agent from safely making targeted auto-improvements to the deployment.

What is the best way to identify critical gaps in agent trace infrastructure?▼

Identify critical gaps by running a trace infrastructure audit that evaluates trace completeness, labels capabilities as Present, Partial, or Absent, and outputs a readiness verdict with the highest-impact next action.

Do I need structured logging and decision-point capture for meta-agent readiness?▼

Yes, structured logging and decision-point capture are required for meta-agent readiness because they provide the reasoning-chain visibility and session reproducibility needed to diagnose failures and replay sessions safely.

Related Skills

Explore More Software Engineering Skills→
jamesogunsanjamesogunsan

python-observability

Guide Python services to implement structured logging, metrics, and traces.

Community
Intermediate
FlexNetOSFlexNetOS

harness-evolution

Evaluate harness run artifacts, classify lessons, and apply low-risk upgrades.

Official
Advanced
MicrockMicrock
322

Convex Agents Usage Tracking

Track LLM token usage and calculate costs for billing reports.

Community
Advanced
gke-labsgke-labs
42

kube-agents-observability

Audit logs, metrics, and traces for Platform Agent on GKE.

Official
Advanced