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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
Prerequisites That Must Exist
Before extraction, verify:
- Data 360 is enabled
- Session Tracing is enabled
- the Salesforce Standard Data Model version is sufficient
- Einstein / Agentforce capabilities are enabled in the org
- JWT / ECA auth for Data 360 access is configured
If auth is missing, hand off to:
Deep setup guide:
What This Skill Works With
Core storage / analysis model
- extraction via Data 360 APIs
- Parquet for storage efficiency
- Polars for large-scale lazy analysis
Core STDM entities
At minimum, expect work around:
- session
- interaction / turn
- interaction step
- moment
- message
GenAI Trust Layer / audit records may also be relevant for content-quality and generation debugging.
Full schema:
Required Context to Gather First
Ask for or infer:
- target org alias
- time window or date range
- agent filter, if any
- whether the goal is extraction, summary analysis, or single-session debugging
- output location for extracted data
- whether the user already has Parquet files on disk
Recommended Workflow
1. Verify setup and auth
Confirm Data 360 tracing exists and JWT/ECA auth is working.
2. Choose the extraction mode
| Need | Default approach |
|---|
| recent telemetry snapshot | extract last N days |
| focused investigation | filtered extraction by date and agent |
| one broken conversation | extract or debug a single session tree |
| ongoing usage analytics | incremental extraction |
3. Extract to Parquet
Use the provided scripts under scripts/ rather than reimplementing extraction logic.
4. Analyze with Polars
Common analysis goals:
- session volume and duration
- topic distribution
- action step failures
- latency hotspots
- abandonment / escalation patterns
- session-level timeline reconstruction
5. Convert findings into next actions
Typical outcomes:
- topic mismatch → improve routing or descriptions
- action failure → inspect Flow / Apex implementation
- latency issue → optimize downstream action path
- test gap → add targeted agent tests
High-Signal Operational Rules
- treat STDM as read-only telemetry
- expect ingestion lag; this is not perfect real-time debugging
- use date filters and focused extraction to avoid unnecessary volume / query cost
- prefer Parquet over ad hoc JSON for durable analysis
- use lazy Polars patterns for large datasets
Common pitfalls:
- assuming missing data means no issue, when tracing may simply not be enabled
- running huge broad queries without date or agent filters
- trying to fix the agent inside this skill instead of handing off to authoring / testing skills
Output Format
When finishing, report in this order:
- What data was extracted or analyzed
- Scope (org, dates, agent filter, session IDs)
- Key findings
- Likely root causes
- Recommended next skill / next action
Suggested shape:
Observability task: <extract / analyze / debug-session>
Scope: <org, dates, agents, session ids>
Artifacts: <directories / parquet files>
Findings: <latency, routing, action, quality, abandonment patterns>
Root cause: <best current explanation>
Next step: <testing, agent fix, flow fix, apex fix>
Cross-Skill Integration
Reference Map
Start here
Data model / querying
Analysis / debugging
Auth / troubleshooting
Score Guide
| Score | Meaning |
|---|
| 90+ | strong telemetry-backed diagnosis |
| 75–89 | useful analysis with minor gaps |
| 60–74 | partial visibility only |
| < 60 | insufficient evidence; gather more telemetry |
Weekly Installs
230
Repository
jaganpro/sf-skills
GitHub Stars
223
First Seen
Jan 29, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
codex226
opencode226
cursor226
gemini-cli224
github-copilot224
amp223
