sf-ai-agentforce-observability
Agentforce session tracing extraction and analysis. TRIGGER when: user extracts STDM data from Data Cloud, analyzes agent session traces, debugs agent conversations via telemetry, or works with .parquet files from Agentforce. DO NOT TRIGGER when: testing agents (use sf-ai-agentforce-testing), Apex debug logs (use sf-debug), or building agents (use sf-ai-agentforce).
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jaganpro/sf-skills --skill sf-ai-agentforce-observability- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sf-ai-agentforce-observability automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
.parquetfiles 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:
- sf-connected-apps
Deep setup guide:
- references/auth-setup.md
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:
- references/data-model-reference.md
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
| Need | Delegate to | Reason |
|---|---|---|
| auth / JWT setup | sf-connected-apps | Data 360 access |
| fix agent routing / behavior | sf-ai-agentscript | authoring corrections |
| formal regression / coverage tests | sf-ai-agentforce-testing | reproducible test loops |
| Flow-backed action debugging | sf-flow | declarative repair |
| Apex-backed action debugging | sf-debug or sf-apex | code / log investigation |
Reference Map
Start here
- README.md
- references/basic-extraction.md
- references/filtered-extraction.md
- references/cli-reference.md
Data model / querying
- references/data-model-reference.md
- references/query-patterns.md
- references/client-demo-queries.md
Analysis / debugging
- references/analysis-cookbook.md
- references/analysis-examples.md
- references/debugging-sessions.md
- references/polars-cheatsheet.md
- references/agent-execution-lifecycle.md
Auth / troubleshooting
- references/auth-setup.md
- references/troubleshooting.md
- references/billing-and-troubleshooting.md
- references/builder-trace-api.md
- scripts/
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 |
FAQ
What does the sf-ai-agentforce-observability skill do?
Agentforce session tracing extraction and analysis. TRIGGER when: user extracts STDM data from Data Cloud, analyzes agent session traces, debugs agent conversations via telemetry, or works with .parquet files from Agentforce. DO NOT TRIGGER when: testing agents (use sf-ai-agentforce-testing), Apex debug logs (use sf-debug), or building agents (use sf-ai-agentforce).
How do I install sf-ai-agentforce-observability?
Run npx skills add https://github.com/jaganpro/sf-skills --skill sf-ai-agentforce-observability in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sf-ai-agentforce-observability/ (personal) or .claude/skills/sf-ai-agentforce-observability/ (project).
Which AI agents can use sf-ai-agentforce-observability?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sf-ai-agentforce-observability safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains sf-ai-agentforce-observability?
It is published by jaganpro in jaganpro/sf-skills. It has 1,266 recorded installs.
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