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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsf-ai-agentforce-observabilityExecute the skills CLI command in your project's root directory to begin installation:
Fetches sf-ai-agentforce-observability from jaganpro/sf-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sf-ai-agentforce-observability. Access via /sf-ai-agentforce-observability in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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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.
Use sf-ai-agentforce-observability when the work involves:
.parquet files from Agentforce telemetryDelegate elsewhere when the user is:
Before extraction, verify:
If auth is missing, hand off to:
Deep setup guide:
At minimum, expect work around:
GenAI Trust Layer / audit records may also be relevant for content-quality and generation debugging.
Full schema:
Ask for or infer:
Confirm Data 360 tracing exists and JWT/ECA auth is working.
| 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 |
Use the provided scripts under scripts/ rather than reimplementing extraction logic.
Common analysis goals:
Typical outcomes:
Common pitfalls:
When finishing, report in this order:
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>
| 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 |
| 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 |
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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sf-ai-agentforce-observability reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend sf-ai-agentforce-observability for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
sf-ai-agentforce-observability is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added sf-ai-agentforce-observability from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in sf-ai-agentforce-observability — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in sf-ai-agentforce-observability — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for sf-ai-agentforce-observability matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: sf-ai-agentforce-observability is the kind of skill you can hand to a new teammate without a long onboarding doc.
sf-ai-agentforce-observability has been reliable in day-to-day use. Documentation quality is above average for community skills.
sf-ai-agentforce-observability has been reliable in day-to-day use. Documentation quality is above average for community skills.
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