ai-tech-summary

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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.

6installs

Sourcetiangong-ai/skills

Added on2026-04-19

NPX Install

npx skill4agent add tiangong-ai/skills ai-tech-summary

SKILL.md Content

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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.

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

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:
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
```