SQL query generator from a plain-English request
Converts a natural-language data requirement plus a table schema into a syntactically correct SQL query for a named database, with its assumptions stated up front.
Replace ambiguous dashboard metrics with a shared registry of formulas, units, scope, sources, owners, and unresolved measurement decisions.
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Version 1 · 4 variables · 4 required
Fill in the values below — the customized prompt updates as you type. marks a required value.
One to three goals the metrics should inform. Default: Reduce support backlog while maintaining reliable resolution quality.
Current names, formulas if known, and disputed definitions. Default: Tickets closed per week; average resolution time; backlog. Teams disagree about whether reopened tickets count as new work and whether weekends count toward resolution time.
Source systems and known fields, event history, calendars, and limitations. Default: Ticket records: ticket_id, created_at, first_resolved_at, latest_resolved_at, status, priority, team. Status history includes reopened events. Business calendar and metric owners are not yet documented.
Audience, review frequency, and expected data freshness. Default: Weekly support operations review; dashboard refreshed each morning.
Create a shared KPI dictionary for Reduce support backlog while maintaining reliable resolution quality..
Current metric names and definitions:
Tickets closed per week; average resolution time; backlog. Teams disagree about whether reopened tickets count as new work and whether weekends count toward resolution time.
Available source fields and operational context:
Ticket records: ticket_id, created_at, first_resolved_at, latest_resolved_at, status, priority, team. Status history includes reopened events. Business calendar and metric owners are not yet documented.
Audience and review cadence:
Weekly support operations review; dashboard refreshed each morning.
Select at most six metrics that directly inform the goals, retaining existing metrics when useful and explaining any proposed replacement. Distinguish outcome measures from possible leading indicators; label an unvalidated leading indicator as a hypothesis.
Produce a registry table with:
- Metric name and plain-language purpose.
- Formula, numerator and denominator where applicable, unit, and whether higher or lower is desirable.
- Inclusion and exclusion rules, event or entity grain, aggregation window, timezone, and handling of missing, duplicate, or late records.
- Required source fields, refresh cadence, responsible owner or "owner needed", and known measurement limitations.
- Target or alert threshold only when supplied or justified by a stated decision rule; otherwise "not agreed".
Identify two definitions that could produce different numbers under the same metric name, and resolve the ambiguity or record the decision needed. Give a small validation checklist for reconciling the dictionary against source records before adding metrics to a dashboard.
Do not invent data availability, benchmarks, targets, business owners, or measured relationships. Mark proposed definitions as proposed. End with the decisions that the relevant stakeholders must settle.No public examples yet for version 1
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Curated prompt only. No example output or model test is attached; model compatibility has not been verified. Default inputs are illustrative practice material; replace them with your own facts and context.
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Converts a natural-language data requirement plus a table schema into a syntactically correct SQL query for a named database, with its assumptions stated up front.
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