afv-library/skills/data360-orchestrate

data360-orchestrate

Salesforce Data Cloud skill family for sf-skills. This is the cross-phase orchestrator for community-driven Data Cloud workflows built around the external sf data360 CLI runtime.

What this skill is for

Use data360-orchestrate when the task spans multiple Data Cloud phases:

• connection + ingestion + harmonization setup
• troubleshooting a Data Cloud pipeline end to end
• managing data spaces or data kits
• deciding which specialized Data Cloud skill to use next

What this skill is not

• It does not vendor or fork the external Data Cloud CLI plugin.
• It does not use MCP.
• It does not replace phase-specific skills once the problem is localized.
• It does not cover STDM/session tracing/parquet analysis; use agentforce-observe for that.

Data Cloud skill family

Skill	Purpose
data360-orchestrate	Orchestrator, data spaces, data kits, cross-phase workflows
data360-connect	Connections, connectors, source discovery
data360-prepare	Data streams, DLOs, transforms, DocAI
data360-harmonize	DMOs, mappings, identity resolution, data graphs
data360-segment	Segments, calculated insights
data360-activate	Activations, activation targets, data actions
data360-query	SQL, async query, vector search, search indexes

Runtime model

This family assumes:

• Salesforce CLI (sf)
• a Data Cloud-enabled org
• the external community sf data360 plugin linked into sf

See references/plugin-setup.md.

Deterministic helpers included

Path	Purpose
scripts/bootstrap-plugin.sh	Clone/update the community plugin, compile it, and link it into sf
scripts/verify-plugin.sh	Check that the runtime is available before starting Data Cloud work
scripts/diagnose-org.mjs	Classify org readiness by phase before mutating Data Cloud assets
references/feature-readiness.md	Map high-signal errors and feature gates to concrete next steps
assets/definitions/	Generic JSON templates for repeatable Data Cloud definition files

Generic templates

The family includes reusable starting points for:

• data streams
• DMOs
• mappings
• identity resolution rulesets
• segments
• search indexes

These are intentionally generic and should be adapted to the target org.

Quick start

1. Verify the runtime

bash ./scripts/verify-plugin.sh
# or with an org alias
bash ./scripts/verify-plugin.sh myorg

The helper treats sf data360 doctor as advisory and falls back to additional read-only smoke checks when an org is only partially provisioned.

2. Diagnose feature readiness before mutating

node ./scripts/diagnose-org.mjs -o myorg --json
# optional retrieve-plane probe, only when you know the table is real
node ./scripts/diagnose-org.mjs -o myorg --phase retrieve --describe-table MyDMO__dlm --json

Use the diagnose helper to distinguish between:

• feature-gated modules
• empty-but-enabled modules
• query-plane issues
• runtime/auth problems

3. Bootstrap the plugin if needed

python3 ~/.claude/sf-skills-install.py --with-datacloud-runtime
# or run the helper script directly
bash ./scripts/bootstrap-plugin.sh

4. Start with read-only inspection

sf data360 man
sf data360 doctor -o myorg 2>/dev/null
sf data360 dmo list -o myorg 2>/dev/null
sf data360 segment list -o myorg 2>/dev/null
sf data360 activation platforms -o myorg 2>/dev/null

Common examples

"Set up a Customer 360 proof of concept in Data Cloud"
"Troubleshoot why my unified profiles are not increasing"
"I need to figure out whether this issue is in mappings, identity resolution, or segment SQL"
"Show me how to inspect data spaces and data kits for this org"

References

• SKILL.md - Orchestrator guidance
• references/plugin-setup.md - Plugin install and verification
• references/feature-readiness.md - Readiness classification and setup guidance