PaperclipOrg
Claude Agent Skill · by Jaganpro

Sf Ai Agentforce Observability

Install Sf Ai Agentforce Observability skill for Claude Code from jaganpro/sf-skills.

Install
Terminal · npx
$npx skills add https://github.com/nextlevelbuilder/ui-ux-pro-max-skill --skill ui-ux-pro-max
Works with Paperclip

How Sf Ai Agentforce Observability fits into a Paperclip company.

Sf Ai Agentforce Observability drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md210 lines
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---name: sf-ai-agentforce-observabilitydescription: >  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).license: MITcompatibility: "Requires Data 360 enabled org with Agentforce Session Tracing"metadata:  version: "1.0.0"  author: "Jag Valaiyapathy"  data_model: "Session Tracing Data Model (STDM)"  storage_format: "Parquet (via PyArrow)"  analysis_library: "Polars"--- # 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](../sf-ai-agentforce-testing/SKILL.md)- debugging Apex logs → [sf-debug](../sf-debug/SKILL.md)- authoring or reconfiguring the agent itself → [sf-ai-agentforce](../sf-ai-agentforce/SKILL.md) or [sf-ai-agentscript](../sf-ai-agentscript/SKILL.md) --- ## 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](../sf-connected-apps/SKILL.md) Deep setup guide:- [references/auth-setup.md](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 entitiesAt 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](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 authConfirm 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 ParquetUse the provided scripts under `scripts/` rather than reimplementing extraction logic. ### 4. Analyze with PolarsCommon 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 actionsTypical 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:1. **What data was extracted or analyzed**2. **Scope** (org, dates, agent filter, session IDs)3. **Key findings**4. **Likely root causes**5. **Recommended next skill / next action** Suggested shape: ```textObservability 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](../sf-connected-apps/SKILL.md) | Data 360 access || fix agent routing / behavior | [sf-ai-agentscript](../sf-ai-agentscript/SKILL.md) | authoring corrections || formal regression / coverage tests | [sf-ai-agentforce-testing](../sf-ai-agentforce-testing/SKILL.md) | reproducible test loops || Flow-backed action debugging | [sf-flow](../sf-flow/SKILL.md) | declarative repair || Apex-backed action debugging | [sf-debug](../sf-debug/SKILL.md) or [sf-apex](../sf-apex/SKILL.md) | code / log investigation | --- ## Reference Map ### Start here- [README.md](README.md)- [references/basic-extraction.md](references/basic-extraction.md)- [references/filtered-extraction.md](references/filtered-extraction.md)- [references/cli-reference.md](references/cli-reference.md) ### Data model / querying- [references/data-model-reference.md](references/data-model-reference.md)- [references/query-patterns.md](references/query-patterns.md)- [references/client-demo-queries.md](references/client-demo-queries.md) ### Analysis / debugging- [references/analysis-cookbook.md](references/analysis-cookbook.md)- [references/analysis-examples.md](references/analysis-examples.md)- [references/debugging-sessions.md](references/debugging-sessions.md)- [references/polars-cheatsheet.md](references/polars-cheatsheet.md)- [references/agent-execution-lifecycle.md](references/agent-execution-lifecycle.md) ### Auth / troubleshooting- [references/auth-setup.md](references/auth-setup.md)- [references/troubleshooting.md](references/troubleshooting.md)- [references/billing-and-troubleshooting.md](references/billing-and-troubleshooting.md)- [references/builder-trace-api.md](references/builder-trace-api.md)- [scripts/](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 |