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Get Started Free →Global multilingual news event stream with tone scoring via GDELT 2.0 Doc API.
.claude/skills/kansoku-trade-gdelt/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 30% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 16% | 0% |
> Response language: match user input.
> ⚠️ GDELT is a rolling recent-window API, not a historical event archive. > Time windows are anchored to absolute timestamps (UTC) for reproducibility — > the same query asked tomorrow will return different results. > > ⚠️ 5-second throttle between requests (enforced). Plan batches accordingly.
Trigger phrases:
Useful for "what is the world saying about X right now" — i.e. retrieving articles from non-English / non-financial sources that don't surface in Longbridge's curated newsfeed.
domain:bloomberg.com, sourcelang:eng).
artlist — list of articles (default).timelinetone — per-15-min tone time series (-10 = very negative,+10 = very positive).
timelinevol / timelinevolinfo — article volume over time.tonechart — tone histogram.--start/--end (absolute), fall back to--timespan. The script converts relative timespans to absolute timestamps before the call and echoes them in meta.window so the journal can be re-run.
bash# Articles about Nvidia in the last 24h python3 .claude/skills/gdelt/scripts/doc.py "Nvidia" # 7-day window, English + Chinese articles about TSMC python3 .claude/skills/gdelt/scripts/doc.py "TSMC OR \"Taiwan Semiconductor\"" --timespan 7d --lang eng,zho # Tone timeline for Federal Reserve over 30 days python3 .claude/skills/gdelt/scripts/doc.py "Federal Reserve" --mode timelinetone --timespan 30d # Absolute window python3 .claude/skills/gdelt/scripts/doc.py "AI chips" --start 20260501000000 --end 20260528000000
json{ "data": [ { "url": "https://...", "title": "...", "seendate": "20260527T161500Z", "domain": "...", "language": "English", "sourcecountry": "United States", "socialimage": "..." } ], "meta": { "mode": "artlist", "query": "Nvidia", "window": { "start": "20260527071804", "end": "20260528071804" }, "max_records": 75 }, "ok": true }
json{ "ok": true, "data": [ {"date": "20260520T000000Z", "value": 1.42}, {"date": "20260520T001500Z", "value": 1.05}, ... ], "meta": {"mode": "timelinetone", ...} }
| Exit code | Meaning | LLM action | | --------- | --------------------------------------- | ------------------------------------------------------------------------------------ | | 0 | Success | Parse data. | | 1 | Invalid args (e.g. bad timespan / lang) | Read hint. | | 3 | HTTP 4xx / non-JSON response | If body contains "Please limit requests", the throttle was tripped — wait and retry. | | 4 | Network | Suggest retry. |
sequenced.
--max-records cap is 250.ground truth.
longbridge-news for curated equity-specific newsfeed (Chinese-language UX).sec-edgar for primary-source filings as the contrast to media narrative.fred for macro data referenced in the narrative.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 19,572 | 15,494 | -21% | 1 | 1 | 0% | 2,840 | 1,479 | -48% | 0 | 0 | — |
case-02 | fail→fail | 14,336 | 4,835 | -66% | 1 | 1 | 0% | 2,159 | 1,391 | -36% | 0 | 0 | — |
case-03 | fail→fail | 5,977 | 4,864 | -19% | 1 | 1 | 0% | 378 | 1,402 | +271% | 0 | 0 | — |
case-04 | pass→pass | 10,815 | 6,249 | -42% | 1 | 1 | 0% | 1,852 | 2,140 | +16% | 0 | 0 | — |
case-05 | pass→pass | 13,096 | 5,592 | -57% | 1 | 1 | 0% | 2,138 | 2,039 | -5% | 0 | 0 | — |
case-06 | pass→pass | 12,256 | 8,631 | -30% | 1 | 1 | 0% | 2,126 | 2,685 | +26% | 0 | 0 | — |
case-07 | fail→fail | 10,332 | 19,382 | +88% | 1 | 1 | 0% | 1,895 | 1,467 | -23% | 0 | 0 | — |
case-08 | pass→pass | 9,730 | 3,786 | -61% | 1 | 1 | 0% | 1,476 | 1,832 | +24% | 0 | 0 | — |
case-09 | fail→fail | 18,316 | 33,145 | +81% | 1 | 1 | 0% | 2,978 | 1,477 | -50% | 0 | 0 | — |
case-10 | fail→fail | 15,234 | 5,698 | -63% | 1 | 1 | 0% | 2,698 | 1,418 | -47% | 0 | 0 | — |
case-11 | fail→fail | 4,957 | 28,019 | +465% | 1 | 1 | 0% | 238 | 7,521 | +3060% | 0 | 0 | — |
case-12 | fail→fail | 8,566 | 5,305 | -38% | 1 | 1 | 0% | 1,581 | 1,391 | -12% | 0 | 0 | — |
case-13 | pass→pass | 12,025 | 2,848 | -76% | 1 | 1 | 0% | 1,904 | 1,613 | -15% | 0 | 0 | — |
case-14 | pass→pass | 9,229 | 3,454 | -63% | 1 | 1 | 0% | 1,661 | 1,756 | +6% | 0 | 0 | — |
case-15 | fail→pass | 13,731 | 6,432 | -53% | 1 | 1 | 0% | 2,317 | 2,216 | -4% | 0 | 0 | — |
case-16 | fail→pass | 12,241 | 3,055 | -75% | 1 | 1 | 0% | 1,838 | 1,587 | -14% | 0 | 0 | — |
case-17 | fail→pass | 28,338 | 2,474 | -91% | 1 | 1 | 0% | 1,251 | 1,625 | +30% | 0 | 0 | — |
case-18 | pass→pass | 12,238 | 10,311 | -16% | 1 | 1 | 0% | 1,862 | 2,756 | +48% | 0 | 0 | — |
case-19 | fail→fail | 16,655 | 6,832 | -59% | 1 | 1 | 0% | 2,709 | 1,562 | -42% | 0 | 0 | — |
case-20 | fail→pass | 11,637 | 4,488 | -61% | 1 | 1 | 0% | 2,117 | 1,957 | -8% | 0 | 0 | — |
case-21 | fail→fail | 9,093 | 5,035 | -45% | 1 | 1 | 0% | 1,618 | 1,964 | +21% | 0 | 0 | — |
case-22 | pass→pass | 5,376 | 2,893 | -46% | 1 | 1 | 0% | 908 | 1,579 | +74% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 12 counted toward the lift figure. The other 10 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +18 percentage points is the difference between those two pass rates over the 12 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.