Rift Backend Effect
Compound-inc/rift
A skill your agent uses when adding, reviewing, or refactoring backend code in Rift's TanStack Start app that should follow apps/start/BACKENDEFFECTPLAYBOOK.md.
Salesforce Data Cloud Segment phase. An agent skill from Jaganpro/sf-skills.
Install Sf Datacloud Segment
$ npx skills add Jaganpro/sf-skills --skill sf-datacloud-segment -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud-segment --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-segment .claude/skills/sf-datacloud-segment && 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-segment" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud-segment into .claude/skills/sf-datacloud-segment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud-segment", 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-datacloud-segmentType 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-segment -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud-segment --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-segment .agents/skills/sf-datacloud-segment && 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-segment" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud-segment into .agents/skills/sf-datacloud-segment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud-segment", 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-segment -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud-segment --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-segment .cursor/skills/sf-datacloud-segment && 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-segment" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud-segment into .cursor/skills/sf-datacloud-segment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud-segment", 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-segment--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-segment -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Jaganpro/sf-skills sf-datacloud-segment --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-segment .gemini/skills/sf-datacloud-segment && 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-segment" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud-segment into .gemini/skills/sf-datacloud-segment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud-segment", 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-datacloud-segmentInstalls 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-segment -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-segment .github/skills/sf-datacloud-segment && 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-segment" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud-segment into .github/skills/sf-datacloud-segment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud-segment", 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-segment -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-segment --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-segment .opencode/skills/sf-datacloud-segment && 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-segment" agent skill from https://github.com/Jaganpro/sf-skills/tree/main/skills/sf-datacloud-segment into .opencode/skills/sf-datacloud-segment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "sf-datacloud-segment", 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-segment- GitHub stars
- 424
- Token cost
- ~1.2k tokens
- SKILL.md length
- 310 words
- Files
- 4
- Skills in repo
- 36
- Repo updated
- First seen
- Licence
- MIT
At a glance
Salesforce Data Cloud Segment phase. An agent skill from Jaganpro/sf-skills.
- Works in 5 steps: Classify readiness for segment work → Inspect current state → Create with reusable JSON definitions → …
- Publishes segments
- SKILL.md covers When This Skill Owns the Task, Required Context to Gather First, Core Operating Rules and Recommended Workflow, plus 3 more sections
- Calls sf and node
What it does
Sf Datacloud Segment is an agent skill from Jaganpro/sf-skills. Salesforce Data Cloud Segment phase. TRIGGER when: user creates or publishes segments, manages calculated insights, inspects segment counts or membership, or troubleshoots audience SQL in Data Cloud. DO NOT TRIGGER when: the task is DMO/mapping/identity-resolution work (use sf-datacloud-harmonize), activation work (use sf-datacloud-act), query/search-index work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `CREDITS.md` and `README.md`). Compatibility notes: Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org
It sits in Sales & Support, covering SQL, CRM management and Observability. It works with SQL and 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
- Publishes segments
- Manages calculated insights
- Inspects segment counts
- Troubleshoots audience SQL in Data Cloud
Example prompts
- “/sf-datacloud-segment”
Requirements
- Compatibility (from SKILL.md): Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org
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
Shell commands in SKILL.md call:
sfnode
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 Segment loads about 1.2k tokens when it runs. Until then it costs about 119 tokens; SKILL.md has 310 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from Jaganpro/sf-skills at commit 53c9956, republished under its MIT licence (© Jaganpro). 310 words, ~1,160 tokens.
Download SKILL.mdSave it as
.claude/skills/sf-datacloud-segment/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- sf-datacloud-segment
- description
- Salesforce Data Cloud Segment phase. TRIGGER when: user creates or publishes segments, manages calculated insights, inspects segment counts or membership, or troubleshoots audience SQL in Data Cloud. DO NOT TRIGGER when: the task is DMO/mapping/identity-resolution work (use sf-datacloud-harmonize), activation work (use sf-datacloud-act), query/search-index work (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
- 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.phase
- Segment
sf-datacloud-segment: Data Cloud Segment Phase
Use this skill when the user needs audience and insight work: segments, calculated insights, publish workflows, member counts, or troubleshooting Data Cloud segment SQL.
When This Skill Owns the Task
Use sf-datacloud-segment when the work involves:
sf data360 segment *sf data360 calculated-insight *- segment publish workflows
- member counts and segment troubleshooting
- calculated insight execution and verification
Delegate elsewhere when the user is:
- still building DMOs, mappings, or identity resolution → sf-datacloud-harmonize
- activating a segment downstream → sf-datacloud-act
- writing read-only SQL or search-index queries → sf-datacloud-retrieve
Required Context to Gather First
Ask for or infer:
- target org alias
- unified DMO or base entity name
- whether the user wants create, publish, inspect, or troubleshoot
- whether the asset is a segment or calculated insight
- expected success metric: member count, aggregate value, or publish status
Core Operating Rules
- Treat Data Cloud segment SQL as distinct from CRM SOQL.
- Run the shared readiness classifier before mutating audience assets:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase segment --json. - Prefer reusable JSON definitions for repeatable segment and CI creation.
- Use
--api-version 64.0when segment creation behavior is unstable on newer defaults. - Verify with counts or SQL after publish/run steps instead of assuming success.
- Use SQL joins rather than
segment memberswhen readable member details are needed.
Recommended Workflow
1. Classify readiness for segment work
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase segment --json2. Inspect current state
sf data360 segment list -o <org> 2>/dev/null
sf data360 calculated-insight list -o <org> 2>/dev/null3. Create with reusable JSON definitions
sf data360 segment create -o <org> -f segment.json --api-version 64.0 2>/dev/null
sf data360 calculated-insight create -o <org> -f ci.json 2>/dev/null4. Publish or run explicitly
sf data360 segment publish -o <org> --name My_Segment 2>/dev/null
sf data360 calculated-insight run -o <org> --name Lifetime_Value 2>/dev/null5. Verify with counts or SQL
sf data360 segment count -o <org> --name My_Segment 2>/dev/null
sf data360 query sql -o <org> --sql 'SELECT COUNT(*) FROM "UnifiedssotIndividualMain__dlm"' 2>/dev/nullHigh-Signal Gotchas
- Segment creation can require
--api-version 64.0. segment membersreturns opaque IDs; use SQL joins when human-readable member details are needed.- Segment SQL is not SOQL.
- Calculated insight assets and segment SQL have different limitations.
- Publish/run steps may kick off asynchronous work even when the command returns quickly.
- An empty segment or calculated-insight list usually means the module is reachable but unconfigured, not unavailable.
Output Format
Segment task: <segment / calculated-insight>
Action: <create / publish / inspect / troubleshoot>
Target org: <alias>
Artifacts: <definition files / commands>
Verification: <member count / query result / publish state>
Next step: <act / retrieve / follow-up>References
© 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 3 other files in skills/sf-datacloud-segment of Jaganpro/sf-skills.
- SKILL.md
- CREDITS.md
- LICENSE
- README.md
Open the folder on GitHubat commit 53c9956
Compare with similar skills
Sf Datacloud Segment 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 Segment this skillJaganpro/sf-skills | 424 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Rift Backend EffectCompound-inc/rift | 124 | — | ~1.8k | Automated safety check: Pass | Custom licence | |
| Salesforce Observabilityjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.1k | Automated safety check: Pass | MIT | |
| RevopsAvdLee/RocketSimApp | 803 | 6 repos | ~3.7k | Automated safety check: Pass | Custom licence | |
| Mz Query TracingMaterializeInc/materialize | 6.4k | — | ~1.8k | Automated safety check: Pass | Custom licence | |
| Suede RevopsJasonColapietro/suede-creator-skills | 127 | — | ~3.8k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Sf Datacloud Segment
What does Sf Datacloud Segment do?
Salesforce Data Cloud Segment phase. An agent skill from Jaganpro/sf-skills. Sf Datacloud Segment is an agent skill from Jaganpro/sf-skills. Salesforce Data Cloud Segment phase.
When should I use Sf Datacloud Segment?
Sf Datacloud Segment fits situations like: publishes segments; manages calculated insights; inspects segment counts; troubleshoots audience SQL in Data Cloud.
How do I install Sf Datacloud Segment in Claude Code?
Run `npx skills add Jaganpro/sf-skills --skill sf-datacloud-segment -a claude-code`. Or copy the skill folder (skills/sf-datacloud-segment in Jaganpro/sf-skills) into .claude/skills/sf-datacloud-segment in your project. Claude Code loads it when a task matches its description.
How do I install Sf Datacloud Segment in Codex?
Run `npx skills add Jaganpro/sf-skills --skill sf-datacloud-segment -a codex`. Or copy the skill folder (skills/sf-datacloud-segment in Jaganpro/sf-skills) into .agents/skills/sf-datacloud-segment in your project. Codex loads it when a task matches its description.
Can I use Sf Datacloud Segment 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-segment -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-segment, .gemini/skills/sf-datacloud-segment, .github/skills/sf-datacloud-segment and .opencode/skills/sf-datacloud-segment in your project.
What does Sf Datacloud Segment need to run?
Going by SKILL.md and its folder, Sf Datacloud Segment needs the command-line tools its instructions call (sf and node). Compatibility (from SKILL.md): Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org.
Does Sf Datacloud Segment 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 Segment 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. Review the folder before installing.
What licence does Sf Datacloud Segment use?
Sf Datacloud Segment 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 Segment use?
About 1.2k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Sf Datacloud Segment?
Skills that share tags, products or a category with Sf Datacloud Segment: Rift Backend Effect (Compound-inc/rift, 124 stars), Salesforce Observability (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Revops (AvdLee/RocketSimApp, 803 stars) and Mz Query Tracing (MaterializeInc/materialize, 6.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Sf Datacloud Segment?
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.