forecasting

name	forecasting
description	On-demand time-series forecasting. CAPTURE params from project context, call run_forecast, present deterministic engine results.
tags	["forecasting","time-series","prediction"]
version	1.0.0

Forecasting

Workflow: CAPTURE → RUN → PRESENT

CAPTURE

Read project context (context/forecast_guide.md) to determine:

• the source table, date column, and time grain
• the value aggregate and any series filter
• baseline start (regime boundary) and exclusions

Then construct a 2-column SQL (X = time grain, Y = value):

SELECT date_trunc('month', <date_col>) AS x, COUNT(DISTINCT <id_col>) AS y
FROM <schema>.<table>
WHERE <date_col> >= '<baseline>'
GROUP BY 1
ORDER BY 1

Lock: {sql, periods}. If the source/series is ambiguous → call request_clarification.

RUN

Call run_forecast(sql, periods) with the SQL you constructed and the horizon in periods. The tool executes the SQL, validates it is exactly 2 columns with enough rows, infers the frequency, and triggers the engine. Do NOT do forecast arithmetic.

If the tool returns ## Ditolak → present the reason to the user. Do not retry with different params unless the user changes the request.

PRESENT

Format the tool's structured output for the user:

• lead with a short headline summary
• show quality label (BAIK / CUKUP / LEMAH)
• show projected periods with realistic ranges
• hide σ, p10/p90, raw residuals unless the user asks for methodology

Notes

• This skill is domain-neutral. All domain specifics (tables, filters, regime) come from project context — never hardcode them here.
• Bounds widen with horizon (σ·√h principle).
• The tool is only registered in non-interactive environments when agent.forecast.enabled: true is set in seeknal_agent.yml.