القائمة
On-demand time-series forecasting. CAPTURE params from project context, call run_forecast, present deterministic engine results.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when helping initialize, configure, or prepare a Seeknal project like a coding agent
Translate business questions into metrics, SQL evidence, and actionable recommendations
Run multi-step SQL plus Python/statistics/ML analysis while keeping tools thin and evidence grounded
Answer business questions from read-only connected databases using deterministic schema discovery and SQL evidence
Run Python code in an isolated subprocess for statistical/ML/visualization work beyond what SQL can express
End-to-end workflow for adding a new pipeline node to a seeknal project — scaffold, validate, apply, and (optionally) run via the 5 thin pipeline-build tools
| 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 |
Read project context (context/forecast_guide.md) to determine:
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.
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.
Format the tool's structured output for the user:
agent.forecast.enabled: true is set in seeknal_agent.yml.