forcedotcom/sf-skills

experience-lds-graphql-generate

Use ALWAYS when a prompt mentions GraphQL, lightning/uiGraphQLApi, @wire(graphql, ...), or gql template tags in an LWC context — even if the surface ask is \"build an LWC\".

Ver código fuente
Documento original del Skill

Contenido del repositorio de origen con títulos, ejemplos, código, tablas, enlaces e imágenes preservados.

<!-- adk-managed-skill -->

Building LDS GraphQL

Generate schema-validated Salesforce LDS GraphQL queries (read or mutation), either as standalone queries or wired into a Lightning Web Component via the lightning/graphql adapters. The skill encodes the schema-as-source-of-truth workflow end-to-end. Two bundled bash scripts handle the org-aware steps — scripts/fetch-lds-graphql-schema.sh for schema introspection and scripts/test-lds-graphql-query.sh for live-org validation.

When to Use

  • User wants to create, modify, or integrate a Salesforce GraphQL query (standard objects, custom objects, or setup objects).
  • Building or updating an LWC that consumes/mutates LDS data through GraphQL.
  • Introspecting an org's schema before writing a query.
  • Validating a generated query end-to-end against a connected org.

Do NOT use this skill for:

  • REST-based UI API (use LDS wire adapters; see experience-lds-best-practices-apply).
  • Apex callouts or custom GraphQL endpoints — this skill is LDS-scoped.
  • Data-requirements discovery — that's experience-lds-data-requirements-generate.

Prerequisites

  • A connected Salesforce org (username or alias). User must confirm it before schema fetch — never assume.
  • Salesforce CLI / sf available in the shell for the schema fetch.
  • Decision on:
  • Namespaceuiapi (default: standard + custom objects) or setup (setup objects like permission sets, profiles).
  • Query typeread (default) or mutation.
  • Output formatstandalone (raw GraphQL + variables) or LWC integration (full component wiring).

Core Rules

These apply to every step — they are the rules this skill enforces:

  1. Sequential execution — Steps 1→6 run in order. Every step is mandatory unless its triggering conditions are not met.
  2. Hard stop on failure — A failed step blocks subsequent steps until remediation is complete.
  3. Schema is the single source of truth — Every entity name, field name, field type, and relationship must come from schema.graphql introspection. Never use common Salesforce knowledge (e.g., do not assume Owner is a User — it may be polymorphic).
  4. Report each step — Use the provided templates before advancing.
  5. Error reporting — Categorize errors; never echo raw tool output into the chat.

Workflow

The full normative workflow lives in references/generation-guide.md. Read it before starting. Read-query specifics are in references/generation-query.md; mutation specifics are in references/generation-mutation.md.

Step 1 — General query information

Collect and echo back:

text
Query type:       [read | mutation]
Namespace:        [uiapi | setup]
Output format:    [standalone | LWC integration]

If any is unclear, ask once and wait.

Step 2 — Acquire the schema

  1. Ask for the usernameOrAlias. If a default is inferred from context, present it and wait for explicit confirmation.
  2. Run scripts/fetch-lds-graphql-schema.sh USERNAME_OR_ALIAS [OUTPUT_PATH] [API_VERSION] with the confirmed alias. The script writes the SDL to schema.graphql (or the path you pass) so the schema never enters the chat context. If a non-empty schema.graphql already exists at OUTPUT_PATH, the script exits early (set LDS_FETCH_FORCE=1 to re-fetch).
  3. On failure: hard stop, report category, ask user to resolve org access, then retry.

Using the schema file

The schema is 265,000+ lines. NEVER read the whole file — use targeted grep calls only.

  • Object type: ^type <ObjectName> implements Record with -A 100.
  • Filter: ^input <ObjectName>_Filter with -A 50.
  • OrderBy: ^input <ObjectName>_OrderBy with -A 30.
  • Mutation input: ^input <ObjectName>(Create|Update)Input with -A 50.

Search budget: max 4–5 grep calls per entity. Plan before executing.

Step 3 — Entity identification

  1. Entity names are PascalCase.
  2. If names aren't given, extract candidates from ^type <Name> implements Record matches.
  3. If any entity is still unresolved, ask the user and wait.
  4. Report:
text
   Identified entities:
   - EntityName (Field1, Field2, ...)
   Unknown entities:
   - <textual name>
   Step 3 status: SUCCESS | FAILED
  1. If Unknown entities is non-empty → status FAILED → ask for clarification → restart Step 3.

Step 4 — Iterative entity introspection

Iteration limit: 3 cycles (primary entity → references → child relationships). Hard-stop after 3.

Per cycle:

  1. Remove already-introspected entities from the list.
  2. Grep for the remaining entities' fields using the schema patterns above.
  3. Extract standard field types.
  4. Identify reference fields (Owner: User). Fields with the same name on different entities may have different types — check each entity independently. If a field resolves to multiple entity types, mark it polymorphic and plan to use inline fragments (... on TypeA, ... on TypeB).
  5. Identify child relationships (Connection types, e.g., Contacts: ContactConnection). Add new entities to the unknown list.
  6. If unknown list not empty and iterations < 3, loop.
  7. Report:
text
   [PASS|FAIL] EntityName
     - Standard fields: FieldName (type), ...
     - Reference fields: FieldName → TargetType, ...
     - Polymorphic fields: FieldName → [TypeA, TypeB], ...
     - Child relationships: RelationshipName → ChildType, ...
     - Unknown fields: FieldName, ...
   Introspection cycles used: N/3
   Step 4 status: SUCCESS | FAILED
  1. If any entity is [FAIL] → global FAILED → remediation → resume from cycle start.

Step 5 — Read query generation (only if query type is read)

Author the read query per references/generation-query.md, feeding in the introspection data, entity list, field types, output format, and usernameOrAlias.

Apply the rules in references/generation-query.md — covers:

  • Query root / namespace selection (uiapi.query vs setup.query).
  • Field selection discipline (ask only for fields actually needed — every field is a billable scan).
  • Filter operators (eq, ne, in, nin, gt, gte, lt, lte, like, contains).
  • OrderBy (per-field direction).
  • Pagination (first, after, last, before; edges.node, pageInfo).
  • Polymorphic inline fragments.
  • Aliasing and variable placeholders.
  • Standalone vs LWC output (wire adapter from lightning/graphql, gql tagged template, refreshGraphQL for imperative refresh).

If the tool returns an error, categorize it and ask the user how to proceed.

Step 6 — Mutation query generation (only if query type is mutation)

Author the mutation per references/generation-mutation.md — covers:

  • create, update, delete operation shape.
  • Input types (<Entity>CreateInput, <Entity>UpdateInput) discovered via the input grep pattern.
  • Required vs optional fields (from schema's ! annotation).
  • Reference-field updates using Id only.
  • Return selection — what to read back after the mutation to drive cache consistency.
  • Error handling (record.errors[]).
  • LWC integration: imperative mutation via graphqlMutate from lightning/graphql.

Step 7 — Test the query

Run scripts/test-lds-graphql-query.sh USERNAME_OR_ALIAS 'QUERY' '<VARIABLES_JSON>' against the confirmed usernameOrAlias and present the response shape/sample to the user. Errors are categorized, not echoed verbatim.

Cross-References

  • Bundled scripts:
  • scripts/fetch-lds-graphql-schema.sh — schema acquisition via a GraphQL introspection query against the org's /services/data/vX/graphql endpoint (LDS exposes no /graphql/sdl route); call once per org/session before query authoring.
  • scripts/test-lds-graphql-query.sh — org-backed validation of the generated query against /services/data/vX/graphql.
  • Related skills:
  • experience-lds-best-practices-apply — general LDS principles, cache semantics, and wire-vs-imperative choice.
  • experience-lds-data-requirements-generate — pre-work that decides what to query before this skill decides how.
  • experience-lwc-generate — host the generated wire adapter cleanly.

Examples

Standalone read — minimal

graphql
query Accounts($limit: Int = 10) {
  uiapi {
    query {
      Account(first: $limit) {
        edges {
          node {
            Id
            Name { value }
          }
        }
      }
    }
  }
}

LWC integration — read with wire

javascript
import { LightningElement, wire } from 'lwc';
import { gql, graphql } from 'lightning/graphql';

export default class AccountList extends LightningElement {
    @wire(graphql, {
        query: gql`
            query Accounts($limit: Int = 10) {
                uiapi {
                    query {
                        Account(first: $limit) {
                            edges { node { Id Name { value } } }
                        }
                    }
                }
            }
        `,
        variables: '$variables'
    })
    accounts;

    variables = { limit: 10 };

    get records() {
        return this.accounts?.data?.uiapi?.query?.Account?.edges ?? [];
    }
}

Verification

  • Step 3 status: SUCCESS before Step 4; Step 4 status: SUCCESS before Steps 5/6.
  • Every field in the generated query appears in the introspection report (no hallucinated fields).
  • Polymorphic fields use inline fragments; non-polymorphic fields do not.
  • For mutations, every required input field (! in schema) is present.
  • scripts/test-lds-graphql-query.sh returns without errors; or, on error, a categorized remediation is presented.
  • If output format is LWC integration, the component imports from lightning/graphql, uses gql tagged template, and exposes data via a getter (not directly in HTML).
del mismo repositorio

Más Skills

Todos los Skills
forcedotcom
Comunidad

agentforce-d360-analyze

Data Cloud 360° view of a single Agentforce session. TRIGGER when user asks to trace, inspect, summarize, or describe a specific Agentforce session by session id (Agent Session UUID 019d… or MessagingSession id 0Mw…). Also triggers on session discovery — find/list/search sessions by time, agent, channel, outcome, or conversation text — when the user has no session id yet. DO NOT TRIGGER for design-time architecture questions (use agentforce-architecture-analyze instead) or for runtime perf/latency/SLO questions that require platform telemetry beyond Data Cloud.

instalaciones
1
GitHub Stars
972
Actualizado
7 sept
forcedotcom
Comunidad

agentforce-generate

Build, modify, audit, repair, optimize, debug, and deploy agents with Agentforce Agent Script. TRIGGER when: user creates, reviews, or changes .agent files or aiAuthoringBundle metadata; asks to fix AgentScript, audit an existing agent, run an AgentScript health check, common-pitfall review, or baseline-versus-candidate repair loop; changes a response, action, subagent, route, state flow, or Agent Spec; previews, debugs, deploys, publishes, or tests agents; uses sf agent generate/preview/publish/test; or manages Agentforce MCP servers, tools, assets, or authentication. DO NOT TRIGGER when: Apex, Flow, Prompt Template, Experience Cloud, or general Salesforce CLI work is unrelated to Agent Script; or the primary input is a production session or trace ID rather than an agent artifact.

instalaciones
1
GitHub Stars
972
Actualizado
7 sept
forcedotcom
Comunidad

platform-quick-deploy

Deploy validated metadata to a Production Salesforce org without re-running tests. TRIGGER when the user wants to deploy to production, says 'quick deploy', 'promote', 'ship to prod', or has just validated and wants to push the change live. REQUIRES a recent sf project deploy validate job ID (≤10 days old, ≤3 days for --use-most-recent). DO NOT TRIGGER for sandbox/scratch deploys (use platform-metadata-deploy) or unvalidated deploys (use platform-deploy-validate first).

instalaciones
1
GitHub Stars
972
Actualizado
7 sept
forcedotcom
Comunidad

agentforce-test

Write, run, and analyze structured test suites for Agentforce agents — functional AND security. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration; requests security testing, OWASP LLM Top 10, red-teaming, penetration testing, prompt-injection tests, a security grade, or a vulnerability assessment of an agent. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use agentforce-generate); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use agentforce-observe); performs a static safety review of .agent file content (use agentforce-generate Section 15).

instalaciones
3
GitHub Stars
972
Actualizado
7 sept