Generating Lightning App

Name: Generating Lightning App
Author: forcedotcom

forcedotcom/sf-skills

2.4k installs
763 repo stars
Updated July 24, 2026
forcedotcom/sf-skills

generating-lightning-app orchestrates Salesforce Lightning Experience app metadata from natural language across objects, pages, tabs, flows, and permission sets.

About

Generating Lightning App builds deployable Salesforce Lightning Experience applications from natural language descriptions by orchestrating dependent metadata types in correct order. It defines a Lightning Custom Application and invokes specialized skills for custom objects, fields, tabs, FlexiPages, list views, validation rules, flows, and permission sets. The metadata type registry requires loading each specialized skill and calling salesforce-api-context before generation except for Flow which uses the metadata-experts pipeline. Use cases include project management apps, vehicle tracking solutions, and employee onboarding with custom Lightning record pages spanning multiple interconnected components. The skill excludes single-component work, Classic apps, troubleshooting-only tasks, or bare application containers without supporting metadata. Related skills cover generating-custom-object, generating-flexipage, generating-flow, generating-validation-rule, generating-list-view, and generating-permission-set as composable blocks orchestrated in dependency order to produce a deployable LEX solution from scenario descriptions.

End-to-end Lightning Experience app orchestration from natural language.
Metadata type registry with skill and API context requirements.
Dependency-ordered generation across objects, pages, tabs, and security.
Delegates to specialized generating-* metadata skills per type.
Excludes single-component or Classic-only application requests.

Generating Lightning App by the numbers

2,436 all-time installs (skills.sh)
+7 installs in the week ending Jul 28, 2026 (Skillselion tracking)
Ranked #211 of 4,386 Backend & APIs skills by installs in the Skillselion catalog
Security screen: LOW risk (skills.sh audit)
Data as of Jul 28, 2026 (Skillselion catalog sync)

At a glance

generating-lightning-app capabilities & compatibility

Capabilities: natural language to lightning app scope decompos · metadata type registry with skill delegation rul · dependency ordered orchestration across sf metad · salesforce api context integration for most meta · flow metadata experts pipeline routing · exclusion rules for single component and classic
Works with: salesforce
Use cases: api development · project management
Pricing: Free

From the docs

What generating-lightning-app says it does

Invoke specialized metadata skills when available; generate metadata directly when no skill exists.

SKILL.md

Build a complete, deployable Salesforce Lightning Experience application from a natural language description

SKILL.md

npx skills add https://github.com/forcedotcom/sf-skills --skill generating-lightning-app

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Installs	2.4k
repo stars	★ 763
Security audit	3 / 3 scanners passed
Last updated	July 24, 2026
Repository	forcedotcom/sf-skills ↗

How do I turn a business app description into a deployable Lightning app with all dependent metadata in order?

Build complete Salesforce Lightning Experience apps from natural language by orchestrating objects, pages, tabs, flows, and security metadata in dependency order.

Who is it for?

Salesforce developers building multi-component Lightning apps from scenario descriptions.

Skip if: Skip for lone object or page creation, Classic apps, or troubleshooting existing metadata only.

When should I use this skill?

User requests a complete Lightning app, business solution, or management system with multiple Salesforce components.

What you get

Dependency-ordered metadata plan invoking specialized skills and API context for a complete Lightning Custom Application.

Salesforce metadata XML
Lightning app package
flow and FlexiPage definitions

By the numbers

sf-skills generating-lightning-app metadata version 1.0
Orchestrates multiple metadata types including objects, fields, FlexiPages, flows, and tabs

Files

SKILL.mdMarkdownGitHub ↗

data360-query: Data Cloud Retrieve Phase

Use this skill when the user needs query, search, and metadata introspection for Data Cloud: sync SQL, paginated SQL, async query workflows, table describe, vector search, hybrid search, or search index operations.

When This Skill Owns the Task

Use data360-query when the work involves:

sf data360 query *
sf data360 search-index *
sf data360 metadata *
sf data360 profile * or sf data360 insight * inspection
understanding Data Cloud SQL results or query shape

Delegate elsewhere when the user is:

writing standard CRM SOQL only → platform-soql-query
designing segment or calculated insight assets → data360-segment
analyzing STDM/session tracing/parquet telemetry → agentforce-observe

---

Required Context to Gather First

Ask for or infer:

target org alias
whether the user needs quick count, medium result set, large export, schema inspection, or semantic search
table/index name if known
whether the task is read-only SQL or search-index lifecycle management

---

Core Operating Rules

Treat Data Cloud SQL as its own query language, not SOQL.
Run the shared readiness classifier before relying on query/search surfaces: node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --json.
Use describe before guessing columns.
Prefer sqlv2 or async query flows for larger result sets.
Use vector search or hybrid search only when the search index lifecycle is healthy.
Keep STDM/parquet/session-tracing workflows out of this skill family.

---

Recommended Workflow

1. Classify readiness for retrieve work

node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --json
# optional query-plane probe, only with a real table name
node ../data360-orchestrate/scripts/diagnose-org.mjs -o <org> --phase retrieve --describe-table MyDMO__dlm --json

2. Choose the smallest correct query shape

sf data360 query sql -o <org> --sql 'SELECT COUNT(*) FROM "ssot__Individual__dlm"' 2>/dev/null
sf data360 query sqlv2 -o <org> --sql 'SELECT * FROM "ssot__Individual__dlm"' 2>/dev/null
sf data360 query async-create -o <org> --sql 'SELECT * FROM "ssot__Individual__dlm"' 2>/dev/null

3. Use describe before guessing fields

sf data360 query describe -o <org> --table ssot__Individual__dlm 2>/dev/null

4. Use vector or hybrid search only when an index exists

sf data360 search-index list -o <org> 2>/dev/null
sf data360 query vector -o <org> --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null
sf data360 query hybrid -o <org> --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null
sf data360 query hybrid -o <org> --index Insurance_Index --query "weather damage coverage" --prefilter "Type_of_Insurance__c='Home'" --limit 10 2>/dev/null

5. Reuse curated search-index examples when creating indexes

Use the phase-owned examples instead of inventing JSON from scratch:

examples/search-indexes/vector-knowledge.json
examples/search-indexes/hybrid-structured.json

---

High-Signal Gotchas

Data Cloud SQL is not SOQL.
Table names should be double-quoted in SQL.
sqlv2 is better than ad hoc OFFSET paging for medium result sets.
async query is preferable for large results.
search-index operations and vector/hybrid queries depend on the index lifecycle being healthy.
Hybrid search can use --prefilter, but only on fields configured as prefilter-capable when the search index was created.
HNSW index parameters are typically read-only on create; leave userValues: [] unless the platform explicitly documents otherwise.
query describe is not a universal tenant probe; only run it with a known DMO or DLO table after broader readiness has been confirmed.

---

Output Format

Retrieve task: <sql / sqlv2 / async / describe / vector / search-index>
Target org: <alias>
Target object: <table or index>
Commands: <key commands run>
Verification: <query rows / schema / status>
Next step: <segment / harmonize / follow-up>

---

References

README.md
examples/search-indexes/vector-knowledge.json
examples/search-indexes/hybrid-structured.json
../data360-orchestrate/assets/definitions/search-index.template.json
../data360-orchestrate/references/plugin-setup.md
../data360-orchestrate/references/feature-readiness.md

{
  "label": "<INDEX_NAME>",
  "developerName": "<INDEX_NAME>",
  "description": "Hybrid search index on a structured Data Cloud DMO",
  "sourceDmoDeveloperName": "<SOURCE_DMO>__dlm",
  "chunkDmoName": "<INDEX_NAME> chunk",
  "chunkDmoDeveloperName": "<INDEX_NAME>_chunk",
  "vectorDmoName": "<INDEX_NAME> index",
  "vectorDmoDeveloperName": "<INDEX_NAME>_index",
  "searchType": "HYBRID",
  "vectorEmbedding": {
    "vectorEmbeddingRelatedFields": []
  },
  "rankingConfigurations": [],
  "chunkingConfiguration": {
    "fieldLevelConfigurations": [
      {
        "sourceDmoDeveloperName": "<SOURCE_DMO>__dlm",
        "sourceDmoFieldDeveloperName": "<TEXT_FIELD>__c",
        "config": {
          "id": "passage_extraction",
          "userValues": [
            { "id": "max_tokens", "value": "512" },
            { "id": "strip_html", "value": "true" }
          ]
        }
      }
    ]
  },
  "vectorEmbeddingConfiguration": {
    "embeddingModel": {
      "id": "e5_large_v2",
      "userValues": [
        { "id": "dimension", "value": "1024" },
        { "id": "max_token_limit", "value": "512" }
      ]
    },
    "index": {
      "id": "HNSW",
      "userValues": []
    },
    "similarityMetric": "COSINE"
  }
}

{
  "label": "My_kav",
  "developerName": "My_kav",
  "sourceDmoDeveloperName": "ssot__KnowledgeArticleVersion__dlm",
  "chunkDmoName": "My_kav chunk",
  "chunkDmoDeveloperName": "My_kav_chunk",
  "vectorDmoName": "My_kav index",
  "vectorDmoDeveloperName": "My_kav_index",
  "searchType": "VECTOR",
  "vectorEmbedding": {
    "vectorEmbeddingRelatedFields": []
  },
  "chunkingConfiguration": {
    "fieldLevelConfigurations": [
      {
        "sourceDmoDeveloperName": "ssot__KnowledgeArticleVersion__dlm",
        "sourceDmoFieldDeveloperName": "ssot__Name__c",
        "config": {
          "id": "passage_extraction",
          "userValues": [
            { "id": "strip_html", "value": "true" },
            { "id": "max_tokens", "value": "512" }
          ]
        }
      }
    ]
  },
  "vectorEmbeddingConfiguration": {
    "embeddingModel": {
      "id": "e5_large_v2",
      "userValues": [
        { "id": "dimension", "value": "1024" },
        { "id": "max_token_limit", "value": "512" }
      ]
    },
    "index": {
      "id": "HNSW",
      "userValues": []
    },
    "similarityMetric": "COSINE"
  },
  "rankingConfigurations": []
}

data360-query

Query and search workflows for Salesforce Data Cloud.

Use this skill for

quick SQL counts
paginated SQL (sqlv2)
async query lifecycles
table describe
vector search
hybrid search with optional prefilter
search index inspection and lifecycle work

Example requests

"Run a Data Cloud SQL query against unified profiles"
"Describe this Data Cloud table before I write SQL"
"Help me troubleshoot vector search in Data Cloud"
"Run a hybrid search with a prefilter in Data Cloud"
"Create and inspect a search index"

Common commands

sf data360 query sql -o myorg --sql 'SELECT COUNT(*) FROM "ssot__Individual__dlm"' 2>/dev/null
sf data360 query describe -o myorg --table ssot__Individual__dlm 2>/dev/null
sf data360 search-index list -o myorg 2>/dev/null
sf data360 query vector -o myorg --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null
sf data360 query hybrid -o myorg --index Knowledge_Index --query "reset password" --limit 5 2>/dev/null

Example payloads

examples/search-indexes/vector-knowledge.json
examples/search-indexes/hybrid-structured.json

References

SKILL.md
../data360-orchestrate/assets/definitions/search-index.template.json
CREDITS.md

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Forks & variants (1)

Generating Lightning App has 1 known copy in the catalog totaling 1.5k installs. They canonicalize to this original listing.

forcedotcom - 1.5k installs

How it compares

Pick generating-lightning-app for full multi-component Lightning apps; use individual sf-skills like generating-custom-object for single metadata types.

FAQ

Single object or full app?

Use this skill for complete apps with CustomApplication plus supporting metadata; use specific skills for lone components.

Must I load other skills?

Yes. When a metadata type has a generating-* skill, you must load it instead of generating directly.

Does Flow need salesforce-api-context?

No. Flow uses the metadata-experts pipeline and is exempt from salesforce-api-context.

Is Generating Lightning App safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

Backend & APIsbackendintegrations

About

Generating Lightning App by the numbers

generating-lightning-app capabilities & compatibility

What generating-lightning-app says it does

Add your badge

How do I turn a business app description into a deployable Lightning app with all dependent metadata in order?

Who is it for?

When should I use this skill?

What you get

By the numbers

Files

data360-query: Data Cloud Retrieve Phase

When This Skill Owns the Task

Required Context to Gather First

Core Operating Rules

Recommended Workflow

1. Classify readiness for retrieve work

2. Choose the smallest correct query shape

3. Use describe before guessing fields

4. Use vector or hybrid search only when an index exists

5. Reuse curated search-index examples when creating indexes

High-Signal Gotchas

Output Format

References

Credits & Acknowledgments

data360-query

Use this skill for

Example requests

Common commands

Example payloads

References

Related skills

Forks & variants (1)

How it compares

FAQ

Single object or full app?

Must I load other skills?

Does Flow need salesforce-api-context?

Is Generating Lightning App safe to install?

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