Salesforce Observability
jeremylongshore/tons-of-skills-marketplace
Build Salesforce integration observability across application traces, platform status, limits, async jobs, events, logs, and business reconciliation.
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows.
Install Sf Datacloud
$ npx skills add Jaganpro/sf-skills --skill sf-datacloud -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud --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-datacloud .claude/skills/sf-datacloud && 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-datacloud" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud into .claude/skills/sf-datacloud/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud", 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-datacloudType 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-datacloud -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud --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-datacloud .agents/skills/sf-datacloud && 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-datacloud" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud into .agents/skills/sf-datacloud/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud", 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-datacloud -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud --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-datacloud .cursor/skills/sf-datacloud && 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-datacloud" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud into .cursor/skills/sf-datacloud/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud", 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-datacloud--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-datacloud -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud --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-datacloud .gemini/skills/sf-datacloud && 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-datacloud" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud into .gemini/skills/sf-datacloud/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud", 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-datacloudInstalls 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-datacloud -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-datacloud .github/skills/sf-datacloud && 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-datacloud" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud into .github/skills/sf-datacloud/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud", 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-datacloud -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-datacloud --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-datacloud .opencode/skills/sf-datacloud && 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-datacloud" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud into .opencode/skills/sf-datacloud/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud", 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-datacloud- GitHub stars
- 424
- Token cost
- ~2.7k tokens
- SKILL.md length
- 889 words
- Files
- 26 (incl. scripts, references, assets)
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows.
- Works in 6 steps: Verify the runtime and auth → Classify readiness before changing… → Discover existing state with read-only… → …
- : user needs a multi-step Data Cloud pipeline
- SKILL.md covers When This Skill Owns the Task, Required Context to Gather First, Core Operating Rules and Recommended Workflow, plus 4 more sections
- Calls sf, node and bash
What it does
Sf Datacloud is an agent skill from Jaganpro/sf-skills. Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud- skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex…
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 27 other files, including scripts, reference files and assets (for example `CREDITS.md`, `README.md` and `UPSTREAM.md`). Compatibility notes: Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org
It sits in Sales & Support, covering CRM management, Observability and DataFrames. It works with Salesforce. 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 needs a multi-step Data Cloud pipeline
- Troubleshoot Data Cloud across phases
- Manages data spaces
- Wants a cross-phase sf data360 workflow
Example prompts
- “/sf-datacloud”
Requirements
- Compatibility (from SKILL.md): Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org
Workflow steps
6 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/, which the agent can run.
Shell commands in SKILL.md call:
sfnodebash
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 an external community sf data360 CLI plugin and a Data Cloud-enabled org
From compatibility in the SKILL.md frontmatter.
Context cost
Sf Datacloud loads about 2.7k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 140 tokens; SKILL.md has 889 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). 889 words, ~2,709 tokens.
Download SKILL.mdSave it as
.claude/skills/sf-datacloud/SKILL.md (or your agent's skills folder). This skill also uses 25 other files; get the full folder from GitHub.- name
- sf-datacloud
- description
- Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
- compatibility
- Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org
- license
- MIT
- metadata.version
- 1.0.0
- metadata.author
- Gnanasekaran Thoppae
- metadata.runtime
- External sf data360 community CLI plugin
sf-datacloud: Salesforce Data Cloud Orchestrator
Use this skill when the user needs product-level Data Cloud workflow guidance rather than a single isolated command family: pipeline setup, cross-phase troubleshooting, data spaces, data kits, or deciding whether a task belongs in Connect, Prepare, Harmonize, Segment, Act, or Retrieve.
This skill intentionally follows sf-skills house style while using the external sf data360 command surface as the runtime. The plugin is not vendored into this repo.
When This Skill Owns the Task
Use sf-datacloud when the work involves:
- multi-phase Data Cloud setup or remediation
- data spaces (
sf data360 data-space *) - data kits (
sf data360 data-kit *) - health checks (
sf data360 doctor) - CRM-to-unified-profile pipeline design
- deciding how to move from ingestion → harmonization → segmentation → activation
- cross-phase troubleshooting where the root cause is not yet clear
Delegate to a phase-specific skill when the user is focused on one area:
| Phase | Use this skill | Typical scope |
|---|---|---|
| Connect | sf-datacloud-connect | connections, connectors, source discovery |
| Prepare | sf-datacloud-prepare | data streams, DLOs, transforms, DocAI |
| Harmonize | sf-datacloud-harmonize | DMOs, mappings, identity resolution, data graphs |
| Segment | sf-datacloud-segment | segments, calculated insights |
| Act | sf-datacloud-act | activations, activation targets, data actions |
| Retrieve | sf-datacloud-retrieve | SQL, search indexes, vector search, async query |
Delegate outside the family when the user is:
- extracting Session Tracing / STDM telemetry → sf-ai-agentforce-observability
- writing CRM SOQL only → sf-soql
- loading CRM source data → sf-data
- creating missing CRM schema → sf-metadata
- implementing downstream Apex or Flow logic → sf-apex, sf-flow
Required Context to Gather First
Ask for or infer:
- target org alias
- whether the plugin is already installed and linked
- whether the user wants design guidance, read-only inspection, or live mutation
- data sources involved: CRM objects, external databases, file ingestion, knowledge, etc.
- desired outcome: unified profiles, segments, activations, vector search, analytics, or troubleshooting
- whether the user is working in the default data space or a custom one
- whether the org has already been classified with
scripts/diagnose-org.mjs - which command family is failing today, if any
If plugin availability or org readiness is uncertain, start with:
- references/plugin-setup.md
- references/feature-readiness.md
scripts/verify-plugin.shscripts/diagnose-org.mjsscripts/bootstrap-plugin.sh
Core Operating Rules
- Use the external
sf data360plugin runtime; do not reimplement or vendor the command layer. - Prefer the smallest phase-specific skill once the task is localized.
- Run readiness classification before mutation-heavy work. Prefer
scripts/diagnose-org.mjsover guessing from one failing command. - For
sf data360commands, suppress linked-plugin warning noise with2>/dev/nullunless the stderr output is needed for debugging. - Distinguish Data Cloud SQL from CRM SOQL.
- Do not treat
sf data360 doctoras a full-product readiness check; the current upstream command only checks the search-index surface. - Do not treat
query describeas a universal tenant probe; only use it with a known DMO/DLO table after broader readiness is confirmed. - Preserve Data Cloud-specific API-version workarounds when they matter.
- Prefer generic, reusable JSON definition files over org-specific workshop payloads.
Recommended Workflow
1. Verify the runtime and auth
Confirm:
sfis installed- the community Data Cloud plugin is linked
- the target org is authenticated
Recommended checks:
sf data360 man
sf org display -o <alias>
bash ~/.claude/skills/sf-datacloud/scripts/verify-plugin.sh <alias>Treat sf data360 doctor as a broad health signal, not the sole gate. On partially provisioned orgs it can fail even when read-only command families like connectors, DMOs, or segments still work.
2. Classify readiness before changing anything
Run the shared classifier first:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --jsonOnly use a query-plane probe after you know the table name is real:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase retrieve --describe-table MyDMO__dlm --jsonUse the classifier to distinguish:
- empty-but-enabled modules
- feature-gated modules
- query-plane issues
- runtime/auth failures
Show full SKILL.md (342 more words)Show less
3. Discover existing state with read-only commands
Use targeted inspection after classification:
sf data360 doctor -o <org> 2>/dev/null
sf data360 data-space list -o <org> 2>/dev/null
sf data360 data-stream list -o <org> 2>/dev/null
sf data360 dmo list -o <org> 2>/dev/null
sf data360 identity-resolution list -o <org> 2>/dev/null
sf data360 segment list -o <org> 2>/dev/null
sf data360 activation platforms -o <org> 2>/dev/null4. Localize the phase
Route the task:
- source/connector issue → Connect
- ingestion/DLO/stream issue → Prepare
- mapping/IR/unified profile issue → Harmonize
- audience or insight issue → Segment
- downstream push issue → Act
- SQL/search/index issue → Retrieve
5. Choose deterministic artifacts when possible
Prefer JSON definition files and repeatable scripts over one-off manual steps. Generic templates live in:
assets/definitions/data-stream.template.jsonassets/definitions/dmo.template.jsonassets/definitions/mapping.template.jsonassets/definitions/relationship.template.jsonassets/definitions/identity-resolution.template.jsonassets/definitions/data-graph.template.jsonassets/definitions/calculated-insight.template.jsonassets/definitions/segment.template.jsonassets/definitions/activation-target.template.jsonassets/definitions/activation.template.jsonassets/definitions/data-action-target.template.jsonassets/definitions/data-action.template.jsonassets/definitions/search-index.template.json
6. Verify after each phase
Typical verification:
- stream/DLO exists
- DMO/mapping exists
- identity resolution run completed
- unified records or segment counts look correct
- activation/search index status is healthy
High-Signal Gotchas
connection listrequires--connector-type.dmo list --allis useful when you need the full catalog, but first-pagedmo listis often enough for readiness checks and much faster.- Segment creation may need
--api-version 64.0. segment membersreturns opaque IDs; use SQL joins for human-readable details.sf data360 doctorcan fail on partially provisioned orgs even when some read-only commands still work; fall back to targeted smoke checks.query describeerrors such asCouldn't find CDP tenant IDorDataModelEntity ... not foundare query-plane clues, not automatic proof that the whole product is disabled.- Many long-running jobs are asynchronous in practice even when the command returns quickly.
- Some Data Cloud operations still require UI setup outside the CLI runtime.
Output Format
When finishing, report in this order:
- Task classification
- Runtime status
- Readiness classification
- Phase(s) involved
- Commands or artifacts used
- Verification result
- Next recommended step
Suggested shape:
Data Cloud task: <setup / inspect / troubleshoot / migrate>
Runtime: <plugin ready / missing / partially verified>
Readiness: <ready / ready_empty / partial / feature_gated / blocked>
Phases: <connect / prepare / harmonize / segment / act / retrieve>
Artifacts: <json files, commands, scripts>
Verification: <passed / partial / blocked>
Next step: <next phase, setup guidance, or cross-skill handoff>Cross-Skill Integration
| Need | Delegate to | Reason |
|---|---|---|
| load or clean CRM source data | sf-data | seed or fix source records before ingestion |
| create missing CRM schema | sf-metadata | Data Cloud expects existing objects/fields |
| deploy permissions or bundles | sf-deploy | environment preparation |
| write Apex against Data Cloud outputs | sf-apex | code implementation |
| Flow automation after segmentation/activation | sf-flow | declarative orchestration |
| session tracing / STDM / parquet analysis | sf-ai-agentforce-observability | different Data Cloud use case |
Reference Map
Start here
Phase skills
- sf-datacloud-connect
- sf-datacloud-prepare
- sf-datacloud-harmonize
- sf-datacloud-segment
- sf-datacloud-act
- sf-datacloud-retrieve
Deterministic helpers
© 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 25 other files (scripts, references, assets) in skills/sf-datacloud of Jaganpro/sf-skills.
- SKILL.md
- CREDITS.md
- LICENSE
- README.md
- UPSTREAM.md
- assets/definitions/activation-target.template.json
- assets/definitions/activation.template.json
- assets/definitions/calculated-insight.template.json
- assets/definitions/data-action-target.template.json
- assets/definitions/data-action.template.json
- assets/definitions/data-graph.template.json
- assets/definitions/data-stream.template.json
- assets/definitions/dmo.template.json
- assets/definitions/identity-resolution.template.json
- assets/definitions/mapping.template.json
- assets/definitions/relationship.template.json
- assets/definitions/search-index.template.json
- assets/definitions/segment.template.json
- references
- … and 7 more
Open the folder on GitHubat commit 53c9956
Compare with similar skills
Sf Datacloud 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 Datacloud this skillJaganpro/sf-skills | 424 | — | ~2.7k | Automated safety check: Pass | MIT | |
| Salesforce Observabilityjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Soql Lib Query Builderbeyond-the-cloud-dev/soql-lib | 154 | — | ~4.3k | Automated safety check: Pass | MIT | |
| Soql Lib Selectorbeyond-the-cloud-dev/soql-lib | 154 | — | ~2k | Automated safety check: Pass | MIT | |
| Dev SetupPortwood-Global-Solutions/Portwood | 125 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Automation Sandbox Post Copy Configureforcedotcom/sf-skills | 1.1k | — | ~5.3k | Automated safety check: Notes | Apache-2.0 |
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Portwood-Global-Solutions/Portwood
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Categories
Questions about Sf Datacloud
What does Sf Datacloud do?
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Sf Datacloud is an agent skill from Jaganpro/sf-skills. Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows.
When should I use Sf Datacloud?
Sf Datacloud fits situations like: : user needs a multi-step Data Cloud pipeline; troubleshoot Data Cloud across phases; manages data spaces; wants a cross-phase sf data360 workflow.
How do I install Sf Datacloud in Claude Code?
Run `npx skills add Jaganpro/sf-skills --skill sf-datacloud -a claude-code`. Or copy the skill folder (skills/sf-datacloud in Jaganpro/sf-skills) into .claude/skills/sf-datacloud in your project. Claude Code loads it when a task matches its description.
How do I install Sf Datacloud in Codex?
Run `npx skills add Jaganpro/sf-skills --skill sf-datacloud -a codex`. Or copy the skill folder (skills/sf-datacloud in Jaganpro/sf-skills) into .agents/skills/sf-datacloud in your project. Codex loads it when a task matches its description.
Can I use Sf Datacloud 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-datacloud -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-datacloud, .gemini/skills/sf-datacloud, .github/skills/sf-datacloud and .opencode/skills/sf-datacloud in your project.
What does Sf Datacloud need to run?
Going by SKILL.md and its folder, Sf Datacloud needs the command-line tools its instructions call (sf, node and bash). Compatibility (from SKILL.md): Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org.
Does Sf Datacloud 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 Datacloud 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 Datacloud use?
Sf Datacloud 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 Datacloud use?
About 2.7k tokens (SKILL.md is roughly 11k 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 3.1k tokens, read only when the agent opens those files.
What are the alternatives to Sf Datacloud?
Skills that share tags, products or a category with Sf Datacloud: Salesforce Observability (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Soql Lib Query Builder (beyond-the-cloud-dev/soql-lib, 154 stars), Soql Lib Selector (beyond-the-cloud-dev/soql-lib, 154 stars) and Dev Setup (Portwood-Global-Solutions/Portwood, 125 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sf Datacloud?
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.