Ak Dev New Tracing Provider
yaalalabs/agent-kernel
Step-by-step guide for adding a new observability/tracing provider to Agent Kernel.
Agentforce session tracing extraction and analysis. An agent skill from Jaganpro/sf-skills.
Install Sf AI Agentforce Observability
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-observability --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sf-ai-agentforce-observability .claude/skills/sf-ai-agentforce-observability && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "sf-ai-agentforce-observability" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observability into .claude/skills/sf-ai-agentforce-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-ai-agentforce-observability", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observabilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-observability --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/sf-ai-agentforce-observability .agents/skills/sf-ai-agentforce-observability && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "sf-ai-agentforce-observability" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observability into .agents/skills/sf-ai-agentforce-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-ai-agentforce-observability", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-observability --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/sf-ai-agentforce-observability .cursor/skills/sf-ai-agentforce-observability && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "sf-ai-agentforce-observability" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observability into .cursor/skills/sf-ai-agentforce-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-ai-agentforce-observability", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Jaganpro/sf-skills.git --path skills/sf-ai-agentforce-observability--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-observability --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/sf-ai-agentforce-observability .gemini/skills/sf-ai-agentforce-observability && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "sf-ai-agentforce-observability" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observability into .gemini/skills/sf-ai-agentforce-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-ai-agentforce-observability", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-observabilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/sf-ai-agentforce-observability .github/skills/sf-ai-agentforce-observability && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "sf-ai-agentforce-observability" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observability into .github/skills/sf-ai-agentforce-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-ai-agentforce-observability", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Jaganpro/sf-skills sf-ai-agentforce-observability --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Jaganpro/sf-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/sf-ai-agentforce-observability .opencode/skills/sf-ai-agentforce-observability && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "sf-ai-agentforce-observability" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-ai-agentforce-observability into .opencode/skills/sf-ai-agentforce-observability/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-ai-agentforce-observability", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
sf-ai-agentforce-observability- GitHub stars
- 424
- Token cost
- ~1.8k tokens
- SKILL.md length
- 603 words
- Files
- 37 (incl. scripts, references, assets)
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
Agentforce session tracing extraction and analysis. An agent skill from Jaganpro/sf-skills.
- Works in 5 steps: Verify setup and auth → Choose the extraction mode → Extract to Parquet → …
- : user extracts STDM data from Data Cloud
- SKILL.md covers When This Skill Owns the Task, Prerequisites That Must Exist, What This Skill Works With and Required Context to Gather First, plus 6 more sections
- Runs Python scripts from its folder
What it does
Sf AI Agentforce Observability is an agent skill from Jaganpro/sf-skills. 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).
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 42 other files, including scripts, reference files and assets (for example `CREDITS.md`, `README.md` and `assets/analysis/message-timeline.py`). Compatibility notes: Requires Data 360 enabled org with Agentforce Session Tracing
It sits in DevOps & Cloud, covering DataFrames, Observability and Building AI agents. It works with Polars. The repository describes itself as: [ARCHIVED — migrated to forcedotcom/afv-library] Salesforce Skills for Agentic Coding Tools — Apex, Flow, LWC, SOQL, Agentforce, Data Cloud, OmniStudio. Read-only archive; active… The licence is MIT.
When your agent uses it
- : user extracts STDM data from Data Cloud
- Analyzes agent session traces
- Debugs agent conversations via telemetry
- Works with .parquet files from Agentforce
Example prompts
- “/sf-ai-agentforce-observability”
Requirements
- Python 3
- Compatibility (from SKILL.md): Requires Data 360 enabled org with Agentforce Session Tracing
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 53c9956. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Requires Data 360 enabled org with Agentforce Session Tracing
From compatibility in the SKILL.md frontmatter.
Context cost
Sf AI Agentforce Observability loads about 1.8k tokens when it runs, and up to ~44k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 603 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from Jaganpro/sf-skills at commit 53c9956, republished under its MIT licence (© Jaganpro). 603 words, ~1,815 tokens.
Download SKILL.mdSave it as
.claude/skills/sf-ai-agentforce-observability/SKILL.md (or your agent's skills folder). This skill also uses 36 other files; get the full folder from GitHub.- name
- sf-ai-agentforce-observability
- description
- 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).
- compatibility
- Requires Data 360 enabled org with Agentforce Session Tracing
- license
- MIT
- metadata.version
- 1.0.0
- metadata.author
- Jag Valaiyapathy
- metadata.data_model
- Session Tracing Data Model (STDM)
- metadata.storage_format
- Parquet (via PyArrow)
- metadata.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
.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
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
Show full SKILL.md (250 more words)Show less
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
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 |
© Jaganpro, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 36 other files (scripts, references, assets) in skills/sf-ai-agentforce-observability of Jaganpro/sf-skills.
- SKILL.md
- .gitignore
- CREDITS.md
- LICENSE
- README.md
- assets/analysis/message-timeline.py
- assets/analysis/session-summary.py
- assets/analysis/step-distribution.py
- assets/queries/interactions.sql
- assets/queries/messages.sql
- assets/queries/sessions.sql
- assets/queries/steps.sql
- hooks/scripts/suggest-analysis.py
- hooks/scripts/validate-extraction.py
- references/agent-execution-lifecycle.md
- … and 22 more
Open the folder on GitHubat commit 53c9956
Compare with similar skills
Sf AI Agentforce Observability next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Sf AI Agentforce Observability this skillJaganpro/sf-skills | 424 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Ak Dev New Tracing Provideryaalalabs/agent-kernel | 192 | — | ~3.5k | Automated safety check: Pass | Apache-2.0 | |
| Ag2 Telemetryag2ai/build-with-ag2 | 252 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| AWS Strands Agents Agentcoresammcj/agentic-coding | 162 | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Langchain Otel Observabilityjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Failproof AI SDK IntegrationFailproofAI/failproofai | 5.3k | — | ~6k | Automated safety check: Pass | Custom licence |
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Related
Works with
Categories
Questions about Sf AI Agentforce Observability
What does Sf AI Agentforce Observability do?
Agentforce session tracing extraction and analysis. An agent skill from Jaganpro/sf-skills. Sf AI Agentforce Observability is an agent skill from Jaganpro/sf-skills. Agentforce session tracing extraction and analysis.
When should I use Sf AI Agentforce Observability?
Sf AI Agentforce Observability fits situations like: : user extracts STDM data from Data Cloud; analyzes agent session traces; debugs agent conversations via telemetry; works with .parquet files from Agentforce.
How do I install Sf AI Agentforce Observability in Claude Code?
Run `npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a claude-code`. Or copy the skill folder (skills/sf-ai-agentforce-observability in Jaganpro/sf-skills) into .claude/skills/sf-ai-agentforce-observability in your project. Claude Code loads it when a task matches its description.
How do I install Sf AI Agentforce Observability in Codex?
Run `npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a codex`. Or copy the skill folder (skills/sf-ai-agentforce-observability in Jaganpro/sf-skills) into .agents/skills/sf-ai-agentforce-observability in your project. Codex loads it when a task matches its description.
Can I use Sf AI Agentforce Observability in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Jaganpro/sf-skills --skill sf-ai-agentforce-observability -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sf-ai-agentforce-observability, .gemini/skills/sf-ai-agentforce-observability, .github/skills/sf-ai-agentforce-observability and .opencode/skills/sf-ai-agentforce-observability in your project.
What does Sf AI Agentforce Observability need to run?
Going by SKILL.md and its folder, Sf AI Agentforce Observability needs Python for the scripts in its folder. Our summary lists: Python 3. Compatibility (from SKILL.md): Requires Data 360 enabled org with Agentforce Session Tracing.
Does Sf AI Agentforce Observability access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Sf AI Agentforce Observability safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Sf AI Agentforce Observability use?
Sf AI Agentforce Observability is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Sf AI Agentforce Observability use?
About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 42k tokens, read only when the agent opens those files.
What are the alternatives to Sf AI Agentforce Observability?
Skills that share tags, products or a category with Sf AI Agentforce Observability: Ak Dev New Tracing Provider (yaalalabs/agent-kernel, 192 stars), Ag2 Telemetry (ag2ai/build-with-ag2, 252 stars), AWS Strands Agents Agentcore (sammcj/agentic-coding, 162 stars) and Langchain Otel Observability (jeremylongshore/tons-of-skills-marketplace, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sf AI Agentforce Observability?
Jaganpro (a GitHub user) maintains it in Jaganpro/sf-skills, which has 424 GitHub stars. The repository holds 36 skills in this directory. The repository was last updated on April 27, 2026.
Source: Jaganpro/sf-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.