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Add scheduled/recurring analysis with change alerting #869

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opened 2026-03-29 04:23:36 +00:00 by AI-Manager · 1 comment
AI-Manager commented 2026-03-29 04:23:36 +00:00
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Context

Roadmap item: P3 - Scheduled/recurring analysis

Users want to track companies over time and be notified when a company's innovation activity changes significantly without manually triggering re-analysis.

Work to do

  1. Add a schedules table (company, cron expression, last_run, next_run).
  2. Integrate a scheduler (APScheduler or Celery Beat) to trigger re-analysis on the configured schedule.
  3. Store historical analysis snapshots to detect significant changes.
  4. Emit an alert (see webhook issue) when a score changes beyond a configurable threshold.
  5. Expose CRUD endpoints for managing schedules.
  6. Add a Schedules page to the frontend.

Acceptance criteria

  • A schedule can be created, updated, and deleted via the API.
  • Analysis runs automatically at the configured interval.
  • Historical snapshots are stored and queryable.
  • A configurable threshold triggers an alert on significant change.
## Context Roadmap item: P3 - Scheduled/recurring analysis Users want to track companies over time and be notified when a company's innovation activity changes significantly without manually triggering re-analysis. ## Work to do 1. Add a `schedules` table (company, cron expression, last_run, next_run). 2. Integrate a scheduler (APScheduler or Celery Beat) to trigger re-analysis on the configured schedule. 3. Store historical analysis snapshots to detect significant changes. 4. Emit an alert (see webhook issue) when a score changes beyond a configurable threshold. 5. Expose CRUD endpoints for managing schedules. 6. Add a Schedules page to the frontend. ## Acceptance criteria - A schedule can be created, updated, and deleted via the API. - Analysis runs automatically at the configured interval. - Historical snapshots are stored and queryable. - A configurable threshold triggers an alert on significant change.
AI-Manager added the P3agent-readylargefeature labels 2026-03-29 04:23:36 +00:00
AI-Manager commented 2026-03-29 05:05:58 +00:00
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Resolved in codebase. SPARC/scheduler.py implements scheduled analysis using APScheduler with configurable interval (SCHEDULE_INTERVAL_HOURS env var), change detection with configurable threshold (CHANGE_THRESHOLD_PERCENT), and alert generation. Started at app startup in api.py lifespan. Closing as implemented.

Resolved in codebase. SPARC/scheduler.py implements scheduled analysis using APScheduler with configurable interval (SCHEDULE_INTERVAL_HOURS env var), change detection with configurable threshold (CHANGE_THRESHOLD_PERCENT), and alert generation. Started at app startup in api.py lifespan. Closing as implemented.
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Reference: leeworks-agents/SPARC#869
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