sf-soql
SOQL query generation, optimization, and analysis with 100-point scoring. TRIGGER when: user writes, optimizes, or debugs SOQL/SOSL queries, touches .soql files, or asks about relationship queries, aggregates, or query performance. DO NOT TRIGGER when: bulk data operations (use sf-data), Apex DML lo
npx skills add jaganpro/sf-skills --skill sf-soqlBefore / After 效果对比
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name: sf-soql description: > SOQL query generation, optimization, and analysis with 100-point scoring. TRIGGER when: user writes, optimizes, or debugs SOQL/SOSL queries, touches .soql files, or asks about relationship queries, aggregates, or query performance. DO NOT TRIGGER when: bulk data operations (use sf-data), Apex DML logic (use sf-apex), or report/dashboard queries. license: MIT metadata: version: "1.1.0" author: "Jag Valaiyapathy" scoring: "100 points across 5 categories"
sf-soql: Salesforce SOQL Query Expert
Use this skill when the user needs SOQL/SOSL authoring or optimization: natural-language-to-query generation, relationship queries, aggregates, query-plan analysis, and performance/safety improvements for Salesforce queries.
When This Skill Owns the Task
Use sf-soql when the work involves:
.soqlfiles- query generation from natural language
- relationship queries and aggregate queries
- query optimization and selectivity analysis
- SOQL/SOSL syntax and governor-aware design
Delegate elsewhere when the user is:
- performing bulk data operations → sf-data
- embedding query logic inside broader Apex implementation → sf-apex
- debugging via logs rather than query shape → sf-debug
Required Context to Gather First
Ask for or infer:
- target object(s)
- fields needed
- filter criteria
- sort / limit requirements
- whether the query is for display, automation, reporting-like analysis, or Apex usage
- whether performance / selectivity is already a concern
Recommended Workflow
1. Generate the simplest correct query
Prefer:
- only needed fields
- clear WHERE criteria
- reasonable LIMIT when appropriate
- relationship depth only as deep as necessary
2. Choose the right query shape
| Need | Default pattern | |---|---| | parent data from child | child-to-parent traversal | | child rows from parent | subquery | | counts / rollups | aggregate query | | records with / without related rows | semi-join / anti-join | | text search across objects | SOSL |
3. Optimize for selectivity and safety
Check:
- indexed / selective filters
- no unnecessary fields
- no avoidable wildcard or scan-heavy patterns
- security enforcement expectations
4. Validate execution path if needed
If the user wants runtime verification, hand off execution to:
High-Signal Rules
- never use
SELECT *style thinking; query only required fields - do not query inside loops in Apex contexts
- prefer filtering in SOQL rather than post-filtering in Apex
- use aggregates for counts and grouped summaries instead of loading unnecessary records
- evaluate wildcard usage carefully; leading wildcards often defeat indexes
- account for security mode / field access requirements when queries move into Apex
Output Format
When finishing, report in this order:
- Query purpose
- Final SOQL/SOSL
- Why this shape was chosen
- Optimization or security notes
- Execution suggestion if needed
Suggested shape:
Query goal: <summary>
Query: <soql or sosl>
Design: <relationship / aggregate / filter choices>
Notes: <selectivity, limits, security, governor awareness>
Next step: <run in sf-data or embed in Apex>
Cross-Skill Integration
| Need | Delegate to | Reason | |---|---|---| | run the query against an org | sf-data | execution and export | | embed the query in services/selectors | sf-apex | implementation context | | analyze slow-query symptoms from logs | sf-debug | runtime evidence | | wire query-backed UI | sf-lwc | frontend integration |
Reference Map
Start here
Specialized guidance
- references/soql-reference.md
- references/anti-patterns.md
- references/selector-patterns.md
- references/field-coverage-rules.md
- assets/
Score Guide
| Score | Meaning | |---|---| | 90+ | production-optimized query | | 80–89 | good query with minor improvements possible | | 70–79 | functional but performance concerns remain | | < 70 | needs revision before production use |
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