Synthesize customer feedback from multiple sources into ranked pain clusters.

Collects feedback from GitHub Issues, Hacker News, and/or App Store Reviews, then runs a multi-pass LLM pipeline to extract and rank pain clusters with evidence. Returns up to 10 ranked pain clusters with impact scores, evidence links, and suggested actions. Takes 10-60 seconds depending on volume.

Args: sources: List of source specs. Each has 'type' (github_issues/hackernews/appstore) and 'target' (owner/repo, search query, or app bundle ID). Example: [{"type": "github_issues", "target": "owner/repo"}, {"type": "hackernews", "target": "MyProduct"}] max_items_per_source: Max feedback items to collect per source (default 200) since: ISO 8601 datetime to filter items (e.g. '2026-01-01T00:00:00Z') focus: Analysis focus — 'pain_points' (default) or 'feature_requests'

Quickly extract top pain points from a single feedback source.

Faster and cheaper than synthesize_feedback — single LLM pass, one source. Returns the top N pain points with frequency counts and sample evidence URLs.

Args: source: Source spec with 'type' (github_issues/hackernews/appstore) and 'target'. Example: {"type": "github_issues", "target": "owner/repo", "labels": ["bug"]} max_items: Max items to collect (default 100) top_n: Number of top pain points to return (default 5)

Search raw feedback items across cached sources using full-text search.

Useful for drilling into a specific topic after synthesis. Searches previously collected feedback without triggering new LLM processing. Fast and cheap.

Args: query: Search terms (e.g. 'authentication mobile' or 'pricing too expensive') sources: Filter by source types (e.g. ['github_issues', 'appstore']) target: Filter by target repo/app (e.g. 'owner/repo') since: ISO 8601 datetime filter (e.g. '2026-01-01T00:00:00Z') limit: Max results to return (default 20)

Get time-series sentiment analysis across feedback sources.

Shows how sentiment shifts over time — useful for tracking the impact of releases, bug fixes, or feature launches. Returns weekly/monthly sentiment scores with notable shifts and likely causes.

Args: sources: List of source specs (same format as synthesize_feedback) since: Start date for trend analysis (ISO 8601, default 6 months ago) granularity: Time bucket size — 'weekly' (default) or 'monthly'