ai-tech-summary
Retrieve time-windowed RSS evidence from SQLite and let the agent produce final summaries using RAG over selected records and fields. Use when generating daily, weekly, monthly, or custom-range AI tech digests directly in agent responses instead of fixed template reports.
What this skill does
# AI Tech Summary ## Core Goal - Pull the right records and fields for a requested time range. - Package evidence into a compact JSON context for RAG. - Let the agent synthesize final summary text from retrieved evidence. - Support daily, weekly, monthly, and custom time windows. ## Triggering Conditions - Receive requests for daily, weekly, or monthly digests. - Receive requests for arbitrary date-range summaries. - Need evidence-grounded summary output from RSS entries/fulltext. - Need agent-generated summary style rather than rigid scripted report format. ## Input Requirements - Required tables in SQLite: `feeds`, `entries` (from `ai-tech-rss-fetch`). - Optional table: `entry_content` (from `ai-tech-fulltext-fetch`). - Shared DB path should be the same across all RSS skills. - In multi-agent runtimes, set `AI_RSS_DB_PATH` to one absolute DB path for this agent. ## RAG Workflow 1. Retrieve evidence context by time window. ```bash export AI_RSS_DB_PATH="/absolute/path/to/workspace-rss-bot/ai_rss.db" python3 scripts/time_report.py \ --db "$AI_RSS_DB_PATH" \ --period weekly \ --date 2026-02-10 \ --max-records 120 \ --max-per-feed 20 \ --summary-chars 8192 \ --fulltext-chars 8192 \ --pretty \ --output /tmp/ai-tech-weekly-context.json ``` 2. Load retrieval output and generate final summary in agent response. - Read `query`, `dataset`, `aggregates`, `records`. - Prioritize `records` as evidence source. - Mention key trends, major events, and notable changes grounded in records. 3. Include evidence anchors in summary. - Reference `entry_id`, feed, and URL for key claims. - If retrieval is truncated, state that summary is based on sampled top records. ## Time Window Modes - `--period daily --date YYYY-MM-DD` - `--period weekly --date YYYY-MM-DD` - `--period monthly --date YYYY-MM-DD` - `--period custom --start ... --end ...` - Time filtering is always based on `entries.first_seen_at` (UTC). Custom boundaries support both `YYYY-MM-DD` and ISO datetime. ## Field Selection for RAG - Use `--fields` to control token budget and relevance. - Default fields are tuned for summarization: - `entry_id,timestamp_utc,timestamp_source,feed_title,feed_url,title,url,summary,fulltext_status,fulltext_length,fulltext_excerpt` - Common minimal field set for tight context: - `entry_id,timestamp_utc,feed_title,title,url,summary` ## Recommended Agent Output Pattern - Use this order in final response: 1. Time range scope 2. Top themes/trends 3. Key developments (grouped) 4. Risks/open questions 5. Evidence list (entry ids + URLs) ## Configurable Parameters - `--db` - `AI_RSS_DB_PATH` (recommended absolute path in multi-agent runtime) - `--period` - `--date` - `--start` - `--end` - `--max-records` - `--max-per-feed` - `--summary-chars` - `--fulltext-chars` - `--top-feeds` - `--top-keywords` - `--fields` - `--output` - `--pretty` - `--fail-on-empty` ## Error Handling - Missing `feeds`/`entries`: fail fast with setup guidance. - Invalid date/time/field list: return parse errors. - Missing `entry_content`: continue in metadata-only mode. - Empty retrieval set: return empty context; optionally fail with `--fail-on-empty`. ## References - `references/time-window-rules.md` - `references/report-format.md` ## Assets - `assets/config.example.json` ## Scripts - `scripts/time_report.py`
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