安装
npx skills add https://github.com/tiangong-ai/skills --skill ai-tech-summary
- 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
- Retrieve evidence context by time window.
- 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
- 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.
- 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:
- Time range scope
- Top themes/trends
- Key developments (grouped)
- Risks/open questions
- 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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