| name | docyrus-dsql-query-design |
| description | Write, discover, and run DSQL (Docyrus Structured Query Language) queries against logical Docyrus data sources. Use when the user wants to query, report on, or aggregate data from Docyrus — list records, count tasks by status, join contacts with users, build time-series breakdowns, answer "show me all projects with more than 5 tasks", or any read-only data question. Also covers using the AI-powered `docyrus dsql ask` command (natural language → DSQL → run → results). Triggers on "query data", "show me all X", "count by Y", "join Z with W", "DSQL query", "docyrus dsql", "run a report", "SQL over data sources", "list records from", "aggregate", "how many", "docyrus dsql ask", "docyrus dsql query", "docyrus dsql schema", or any data retrieval / reporting task against Docyrus logical data sources. |
Docyrus DSQL Query Design
DSQL (Docyrus Structured Query Language) is a read-only, PostgreSQL-compatible SQL dialect that queries logical data sources — not physical tables. Tables are named appSlug.dataSourceSlug. This skill covers the full workflow: schema discovery → query authoring → execution.
For the complete language reference (all supported functions, tenant pseudo-functions, row limits, rejection rules), see references/dsql-language-reference.md. For common query patterns and examples, see references/query-patterns.md.
Workflow
Follow in order.
-
Confirm auth.
docyrus auth who --json
No session → docyrus auth login first.
-
Identify the data sources. If app/source slugs are already known, skip to step 3. Otherwise:
docyrus apps list --json
docyrus dsql schema app <appSlug> --json
The schema response includes a compact CREATE TABLE appSlug.dataSourceSlug (...) DDL per source — this lists all queryable field slugs and -- references join hints.
-
Fetch the schema for the exact sources you'll query. Three options — pick the smallest:
| Situation | Command |
|---|
| Know appSlug + dataSourceSlug | docyrus dsql schema data-source <appSlug> <dsSlug> |
| Know one or more data source IDs | docyrus dsql schema data-sources --ids <id1,id2> |
| Need all sources in an app | docyrus dsql schema app <appSlug> |
Always read the schema before writing joins — field slugs and reference hints are only visible there.
-
Write the DSQL query. Follow the rules in references/dsql-language-reference.md. Key constraints:
- Tables are
appSlug.dataSourceSlug (e.g. base.task, crm.contact)
- Always alias every table; qualify all columns in multi-table queries
- Bare
* only when exactly one source is in scope
- Default limit 100 applies if omitted; max 1000
-
Run it.
docyrus dsql query "select t.id, t.subject from base.task t limit 10"
docyrus dsql query --from-file ./my-query.sql
-
Quick path — AI generate + run in one shot. For natural-language questions where you don't need to handwrite SQL:
docyrus dsql ask "how many open tasks per project this month?"
ask calls the DSQL Generator agent (discover schema → write DSQL → execute → return results). Use generate if you only want the SQL without running it:
docyrus dsql generate "list contacts created in the last 30 days with their owner names"
-
Explain the result. Summarize what the data shows; note any relevant limit truncation or empty-result reasons.
Command cheat-sheet
Schema discovery
docyrus dsql schema app crm --json
docyrus dsql schema data-source crm contact --json
docyrus dsql schema data-sources --ids "019c48d0-...,019c48e0-..." --json
Execute a query
docyrus dsql query "select p.id, p.name, p.status from base.project p order by p.created_on desc limit 25"
docyrus dsql query --from-file ./report.sql
docyrus dsql query "select count(*) as n from base.task t" --format json
AI-assisted generation
docyrus dsql generate "total time logged per project last week"
docyrus dsql ask "how many tasks are overdue by assignee?"
docyrus dsql ask "..." --agentId <custom-agent-id>
Critical rules
- Table notation is
appSlug.dataSourceSlug, never a physical Postgres table. Bare table names (task, contact) are rejected unless they are CTE aliases.
- Always alias every table (
from base.task t). Qualify every column in joins — DSQL rejects ambiguous unqualified column references.
- Bare
* is only valid when exactly one logical source is in scope. select * from base.task t join base.project p ... is rejected — use t.* or list columns.
- Read-only only.
INSERT, UPDATE, DELETE, CREATE, DROP, and any DDL are rejected outright.
- No physical tables, no schema-qualified functions.
select * from public.tenant_user or select pg_catalog.now() — both rejected. Use tenant.user for users and the approved function list.
- Tenant pseudo-functions (
tenant.current_user_id(), tenant.user_in_current_teams(userId), etc.) are the only way to filter by current session identity securely. Don't substitute hard-coded UUIDs for them.
tenant.user and tenant.enum are system sources for resolving user names/emails and enum labels/colors. Join on u.id and e.id respectively.
- Inherited base fields:
base.task and base.event expose base.activity fields (e.g. record_owner, created_on) — query them directly on the child source.
- Row limit: default 100 if no
LIMIT clause; hard cap 1000. A user-written smaller limit is preserved. LIMIT ALL does not bypass the cap.
- Schema first: never guess field slugs — fetch the schema and read the DDL. Wrong field slugs return a DSQL error.
docyrus dsql ask is the one-liner path: natural language → AI-generate → run → results. Use it when handwriting SQL is unnecessary.
- CLI-only — do not use in frontend pages. The underlying endpoint is not documented in Swagger and is not intended for use in implemented React/frontend application code. All DSQL queries must go through the
docyrus dsql CLI commands.
References
- references/dsql-language-reference.md — Full DSQL language spec: supported clauses, functions, tenant pseudo-functions, table resolution rules, rejection list, row limits.
- references/query-patterns.md — Common query patterns: aggregates, joins, CTEs, tenant filters, enum lookups, date ranges, and error reference.