Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observabilitygit clone --depth 1 https://github.com/Jaganpro/sf-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jaganpro/sf-skills/sf-ai-agentforce-observability)<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-ai-agentforce-observability"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-ai-agentforce-observability/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jaganpro/sf-skills/sf-ai-agentforce-observability"><img src="https://agentmods.dev/badge/skills/jaganpro/sf-skills/sf-ai-agentforce-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.01688 |
| Opus 5 | $0.00043 | $0.00844 |
| Sonnet 5 | $0.00017 | $0.00338 |
| Haiku 4.5 | $0.00009 | $0.00169 |
Grade A, and why
sf-ai-agentforce-observability scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
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
What ships with it
35 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .gitignore 387 B
- assets/analysis/message-timeline.py 9.1 KB runs code
- assets/analysis/session-summary.py 6.2 KB runs code
- assets/analysis/step-distribution.py 6.4 KB runs code
- assets/queries/interactions.sql 1.7 KB
- assets/queries/messages.sql 1.9 KB
- assets/queries/sessions.sql 1.9 KB
- assets/queries/steps.sql 2.0 KB
- CREDITS.md 3.4 KB
- hooks/scripts/suggest-analysis.py 6.3 KB runs code
- hooks/scripts/validate-extraction.py 7.6 KB runs code
- LICENSE 1.1 KB
- README.md 8.2 KB
- references/agent-execution-lifecycle.md 8.7 KB
- references/analysis-cookbook.md 7.7 KB
- references/analysis-examples.md 7.4 KB
- references/auth-setup.md 6.9 KB
- references/basic-extraction.md 2.9 KB
- references/billing-and-troubleshooting.md 2.6 KB
- references/builder-trace-api.md 18 KB
- references/cli-reference.md 6.4 KB
- references/client-demo-queries.md 4.3 KB
- references/data-model-reference.md 35 KB
- references/debugging-sessions.md 8.2 KB
- references/filtered-extraction.md 4.0 KB
- references/polars-cheatsheet.md 6.2 KB
- references/query-patterns.md 38 KB
- references/troubleshooting.md 9.4 KB
- scripts/__init__.py 598 B runs code
- scripts/analyzer.py 43 KB runs code
- scripts/auth.py 14 KB runs code
- scripts/cli.py 30 KB runs code
- scripts/datacloud_client.py 22 KB runs code
- scripts/extractor.py 31 KB runs code
- scripts/models.py 22 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 211 lines · 86 tokens per session scan A f2098d286b72
sf-ai-agentforce-observability is a skill published in the GitHub repository Jaganpro/sf-skills (423 stars, last pushed 4mo ago), licensed MIT. It adds 86 tokens to every session and 1,688 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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