Sf Ai Agentforce Observability

When your Agentforce agent misbehaves in production, this pulls the telemetry to figure out why. It extracts Session Tracing Data Model records from Data 360, dumps them to Parquet files, and gives you Polars-based analysis patterns to reconstruct what happened during actual user conversations. You get session timelines, topic routing paths, action failures, and latency breakdowns. It's strictly observability, not testing or building the agent itself. The skill assumes you already have Data 360 and session tracing turned on, plus JWT auth wired up. If you find a root cause here, you hand off to the authoring or testing skills to actually fix it. Think of it as the black box recorder for agent conversations.

skills.sh installs: 1.3K
GitHub stars: 422
last updated: Jul 14, 2026

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Install

$ npx -y skills add jaganpro/sf-skills --skill sf-ai-agentforce-observability --agent claude-code

Installs into .claude/skills of the current project.

Files

sf-ai-agentforce-observability: Agentforce Session Tracing Extraction & Analysis

Use this skill when the user needs trace-based observability, not just testing: extract Session Tracing Data Model (STDM) records, work with Parquet datasets, reconstruct session timelines, analyze topic/action latency, or debug agent behavior from Data 360 telemetry.

When This Skill Owns the Task

Use sf-ai-agentforce-observability when the work involves:

Data 360 / Session Tracing extraction
.parquet files from Agentforce telemetry
session timeline reconstruction
trace-driven debugging of topic routing, action failures, or latency
Polars / PyArrow-based analysis of large telemetry datasets

Delegate elsewhere when the user is:

formally testing agents → sf-ai-agentforce-testing
debugging Apex logs → sf-debug
authoring or reconfiguring the agent itself → sf-ai-agentforce or sf-ai-agentscript