Deep Research → NotebookLM Orchestrator
End-to-end workflow: Run Deep Research, then automatically feed the report into NotebookLM to generate Audio/Video/Infographics/Slides.
Dependencies (must be installed):
gemini-deep-research skill — for running Deep Research via Gemini CLInotebooklm-content-creation skill — for NotebookLM notebook/source/audio/video/infographic/slides management
Workflow Overview
User requests "research + generate content"
↓
Agent confirms parameters (one message)
↓
Start Deep Research (background polling, 5 min interval, max 20 min)
↓
DR completes → save report
↓
Notify user: "DR done, starting NotebookLM..."
↓
Create NotebookLM notebook + upload report
↓
Generate selected artifacts in parallel (Audio/Video/Infographics/Slides)
↓
Background polling for each artifact (5 min interval, max 40 min)
↓
Each artifact completes → notify user (with download if requested)
↓
All done → final summary notification
Step 1 — Pre-Flight Confirmation (One Message, All Parameters)
Confirm in the user's current session language. Example (Chinese):
请确认 Deep Research → NotebookLM 参数：
① 研究主题：
（将原样发给 Gemini Deep Research）
② 报告格式：
- Comprehensive Research Report（推荐）
- Executive Brief（精简版）
- Technical Deep Dive
③ NotebookLM 产物（可多选）：
- ☐ Audio Overview（播客） ← 默认选中
- ☐ Video Overview（视频）
- ☐ Infographics（信息图）
- ☐ Slides（幻灯片）
④ 产物参数：
- Audio 格式：deep_dive / brief / critique / debate（默认：deep_dive）
- Audio 长度：short / default / long（默认：default）
- Video 格式：explainer / brief / cinematic（默认：explainer）
- Slides 格式：detailed_deck / presenter_slides（默认：detailed_deck）
- 语言：zh-CN / en / ...（默认：zh-CN）
⑤ 是否下载产物到本地？
- 是 → 保存到 ~/ObsidianVault/Default/NotebookLM/<notebook-name>/
- 否 ← 默认
⑥ 轮询设置：
- DR 轮询：每 5 分钟，最多 4 次 = 20 分钟
- NotebookLM 轮询：每 5 分钟，最多 8 次 = 40 分钟
直接回复修改项，或"确认"以默认参数启动。
Defaults: Audio only (deep_dive, default length, zh-CN), no download.
Step 2 — Start Deep Research
Use the gemini-deep-research skill to start the research.
2.1 Create Task Directory
mkdir -p /tmp/deep-research-to-notebooklm/<YYMMDD-HHmm>_<slug>/
Write task.json:
{
"topic": "<user's research topic>",
"dr_format": "Comprehensive Research Report",
"dr_output_path": "/home/node/ObsidianVault/Default/DeepResearch/<YYYYMMDD>-<slug>.md",
"artifacts": ["audio"],
"artifact_params": {
"audio": { "format": "deep_dive", "length": "default" },
"video": { "format": "explainer" },
"slides": { "format": "detailed_deck", "length": "default" }
},
"language": "zh-CN",
"download": false,
"dr_poll_interval": 300,
"dr_max_polls": 4,
"nlm_poll_interval": 300,
"nlm_max_polls": 8,
"created_at": "<ISO timestamp>"
}
2.2 Launch DR
Start Deep Research using the shell pipe method (see gemini-deep-research SKILL.md Step 3 Method B). Save the researchId to task.json.
2.3 Write DR Poll Script
Write <task-dir>/dr-poll.sh. This script:
- Polls DR status every 5 minutes, max 4 polls (20 min)
- On completion: saves report, notifies user, triggers agent to start NotebookLM
- On timeout/failure: notifies user directly
#!/bin/bash
set -euo pipefail
TASK_DIR="$(cd "$(dirname "$0")" && pwd)"
cd "$TASK_DIR"
[[ -f dr-done.flag ]] && echo "Already complete." && exit 0
RESEARCH_ID=$(python3 -c "import json; print(json.load(open('task.json'))['researchId'])")
OUTPUT_PATH=$(python3 -c "import json; print(json.load(open('task.json'))['dr_output_path'])")
TOPIC=$(python3 -c "import json; print(json.load(open('task.json'))['topic'])")
CHAT_ID="INJECT_CHAT_ID" # ← Agent: replace with current Discord channel ID
GEMINI_EXT="$HOME/.gemini/extensions/gemini-deep-research"
log() { echo "[$(date -u +%Y-%m-%dT%H:%M:%SZ)] $*" | tee -a dr-poll.log; }
notify_user() {
local message="$1"
openclaw message send --channel discord --target "$CHAT_ID" -m "$message" 2>/dev/null || log "WARNING: notification failed"
}
trigger_agent() {
local message="$1"
openclaw agent --channel discord --message "$message" --deliver --timeout 600 2>/dev/null || {
log "WARNING: agent trigger failed, falling back to direct message"
notify_user "$message"
}
}
poll_status() {
(echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"poll","version":"1.0"}}}'
sleep 0.5
echo "{\"jsonrpc\":\"2.0\",\"id\":2,\"method\":\"tools/call\",\"params\":{\"name\":\"research_status\",\"arguments\":{\"id\":\"$RESEARCH_ID\"}}}"
) | timeout 60 node "$GEMINI_EXT/dist/index.js" 2>/dev/null | grep -v "MCP server running" | tail -1
}
POLL_COUNT=0
MAX_POLLS=4
INTERVAL=300
while true; do
POLL_COUNT=$((POLL_COUNT + 1))
[[ $POLL_COUNT -gt $MAX_POLLS ]] && { log "TIMEOUT"; notify_user "❌ Deep Research 超时（20分钟）。"; exit 1; }
log "[Poll $POLL_COUNT/$MAX_POLLS] Checking DR status..."
RESULT=$(poll_status) || true
echo "$RESULT" >> dr-poll.log
STATUS=$(echo "$RESULT" | python3 -c "
import sys, json
try:
d = json.loads(sys.stdin.read())
text = d['result']['content'][0]['text']
obj = json.loads(text)
print(obj.get('status', 'unknown'))
except: print('parse_error')" 2>/dev/null)
log "Status: $STATUS"
if [[ "$STATUS" == "completed" ]]; then
log "DR completed. Extracting report..."
echo "$RESULT" | python3 -c "
import sys, json
d = json.loads(sys.stdin.read())
text = d['result']['content'][0]['text']
obj = json.loads(text)
report = obj.get('outputs', [{}])[0].get('text', '')
if report:
with open('$OUTPUT_PATH', 'w') as f: f.write(report)
print(f'Saved: {len(report)} chars')
else:
print('No report found')
" >> dr-poll.log 2>&1
if [[ -s "$OUTPUT_PATH" ]]; then
SIZE=$(du -h "$OUTPUT_PATH" | cut -f1)
log "Report saved: $OUTPUT_PATH ($SIZE)"
touch dr-done.flag
notify_user "✅ Deep Research 完成！报告已保存（$SIZE）。正在启动 NotebookLM..."
# Read artifacts config from task.json
ARTIFACTS=$(python3 -c "import json; print(','.join(json.load(open('task.json'))['artifacts']))")
ARTIFACT_PARAMS=$(python3 -c "
import json
t = json.load(open('task.json'))
params = t.get('artifact_params', {})
lang = t.get('language', 'zh-CN')
lines = []
for art in t['artifacts']:
p = params.get(art, {})
lines.append(f'- {art}: {json.dumps(p, ensure_ascii=False)}')
print('\n'.join(lines))
")
trigger_agent "✅ Deep Research 完成。请启动 NotebookLM 工作流：
- 报告路径：$OUTPUT_PATH
- Notebook 名称：$TOPIC
- 产出类型：$ARTIFACTS
- 各产物参数：
$ARTIFACT_PARAMS
- 语言：$(python3 -c "import json; print(json.load(open('task.json'))['language'])")
- 是否下载：$(python3 -c "import json; print('是' if json.load(open('task.json'))['download'] else '否')")
请创建 notebook、上传报告、生成所选产物。"
else
notify_user "⚠️ Deep Research 完成但报告保存失败。"
fi
exit 0
fi
[[ "$STATUS" == "failed" ]] && { log "Failed"; notify_user "❌ Deep Research 失败。"; exit 1; }
log "Still in_progress. Sleeping ${INTERVAL}s..."
sleep "$INTERVAL"
done
2.4 Launch DR Poll
cd /tmp/deep-research-to-notebooklm/<task-dir>/
nohup bash dr-poll.sh > /dev/null 2>&1 &
Step 3 — NotebookLM Phase (Triggered by DR Poll)
When the agent receives the trigger message from DR poll, execute the NotebookLM workflow. This is triggered mode — skip all user confirmations.
3.1 Create Notebook
nlm notebook create "<TOPIC from trigger>"
Capture notebook ID.
3.2 Upload Report
nlm source add <notebook_id> --file "<report_path from trigger>" --wait
3.3 Generate Artifacts in Parallel
For each selected artifact type, create and capture artifact ID:
Audio:
nlm audio create <notebook_id> --format <format> --length <length> --language <lang> --confirm
Video:
nlm video create <notebook_id> --format <format> --language <lang> --confirm
Infographics:
nlm infographic create <notebook_id> --detail detailed --orientation landscape --language <lang> --confirm
Slides:
nlm slides create <notebook_id> --format <format> --length <length> --language <lang> --confirm
3.4 Write NotebookLM Poll Script
Write <task-dir>/nlm-poll.sh. This script tracks multiple artifacts in parallel.
#!/bin/bash
set -euo pipefail
TASK_DIR="$(cd "$(dirname "$0")" && pwd)"
cd "$TASK_DIR"
[[ -f nlm-done.flag ]] && echo "Already complete." && exit 0
NOTEBOOK_ID=$(python3 -c "import json; print(json.load(open('task.json'))['notebook_id'])")
NOTEBOOK_NAME=$(python3 -c "import json; print(json.load(open('task.json'))['topic'])")
CHAT_ID="INJECT_CHAT_ID" # ← Agent: replace with current Discord channel ID
DOWNLOAD=$(python3 -c "import json; print(json.load(open('task.json'))['download'])")
OUTPUT_DIR="$HOME/ObsidianVault/Default/NotebookLM/$(echo $NOTEBOOK_NAME | tr ' ' '-')"
log() { echo "[$(date -u +%Y-%m-%dT%H:%M:%SZ)] $*" | tee -a nlm-poll.log; }
notify_user() {
local message="$1"
openclaw message send --channel discord --target "$CHAT_ID" -m "$message" 2>/dev/null || log "WARNING: notification failed"
}
POLL_COUNT=0
MAX_POLLS=8
INTERVAL=300
# Read artifact IDs from artifacts.json (written by agent at setup time)
# Format: [{"type":"audio","id":"abc123","download_cmd":"nlm download audio"}, ...]
TOTAL=$(python3 -c "import json; print(len(json.load(open('artifacts.json'))))")
while true; do
POLL_COUNT=$((POLL_COUNT + 1))
[[ $POLL_COUNT -gt $MAX_POLLS ]] && {
log "TIMEOUT after $MAX_POLLS polls"
# Notify about remaining incomplete artifacts
INCOMPLETE=$(python3 -c "
import json
arts = json.load(open('artifacts.json'))
done = set()
if __import__('os').path.exists('completed.json'):
done = set(json.load(open('completed.json')))
remaining = [a['type'] for a in arts if a['type'] not in done]
print(', '.join(remaining) if remaining else 'none')
")
notify_user "⏰ NotebookLM 产物生成超时（40分钟）。未完成：$INCOMPLETE"
exit 1
}
log "[Poll $POLL_COUNT/$MAX_POLLS] Checking status..."
STATUS_OUTPUT=$(nlm studio status "$NOTEBOOK_ID" 2>&1) || true
echo "$STATUS_OUTPUT" >> nlm-poll.log
# Check each artifact
python3 << 'PYEOF' >> nlm-poll.log 2>&1
import json, subprocess, os
arts = json.load(open('artifacts.json'))
status_data = json.loads(open('/dev/stdin', 'r').read()) if False else None
# Parse studio status output
try:
status_data = json.loads('''STATUS_OUTPUT'''.replace("STATUS_OUTPUT", ""))
except:
pass
completed = set()
if os.path.exists('completed.json'):
completed = set(json.load(open('completed.json')))
for art in arts:
if art['type'] in completed:
continue
# Find artifact status
art_status = 'unknown'
if status_data:
for s in (status_data if isinstance(status_data, list) else status_data.get('artifacts', [])):
if s.get('id') == art['id']:
art_status = s.get('status', 'unknown')
break
if art_status == 'completed':
print(f"COMPLETED:{art['type']}:{art['id']}")
elif art_status == 'failed':
print(f"FAILED:{art['type']}")
PYEOF
# Process completions
COMPLETED_ARTS=()
FAILED_ARTS=()
while IFS= read -r line; do
if [[ "$line" == COMPLETED:* ]]; then
ART_TYPE=$(echo "$line" | cut -d: -f2)
ART_ID=$(echo "$line" | cut -d: -f3)
COMPLETED_ARTS+=("$ART_TYPE")
# Download if requested
if [[ "$DOWNLOAD" == "True" ]]; then
mkdir -p "$OUTPUT_DIR"
DOWNLOAD_CMD=$(python3 -c "import json; arts=json.load(open('artifacts.json')); print([a['download_cmd'] for a in arts if a['type']=='$ART_TYPE'][0])")
OUTPUT_FILE="$OUTPUT_DIR/$ART_TYPE-$(date +%Y%m%d).mp4"
eval "$DOWNLOAD_CMD $NOTEBOOK_ID --id $ART_ID -o $OUTPUT_FILE" >> nlm-poll.log 2>&1 || true
SIZE=$(du -h "$OUTPUT_FILE" 2>/dev/null | cut -f1 || echo "?")
notify_user "✅ $ART_TYPE 生成完成！已下载（$SIZE）：$OUTPUT_FILE"
else
notify_user "✅ $ART_TYPE 生成完成！（未下载，在 NotebookLM 中可查看）"
fi
# Mark completed
python3 -c "
import json, os
completed = set()
if os.path.exists('completed.json'):
completed = set(json.load(open('completed.json')))
completed.add('$ART_TYPE')
json.dump(list(completed), open('completed.json', 'w'))
"
elif [[ "$line" == FAILED:* ]]; then
ART_TYPE=$(echo "$line" | cut -d: -f2)
FAILED_ARTS+=("$ART_TYPE")
notify_user "❌ $ART_TYPE 生成失败。"
python3 -c "
import json, os
completed = set()
if os.path.exists('completed.json'):
completed = set(json.load(open('completed.json')))
completed.add('$ART_TYPE')
json.dump(list(completed), open('completed.json', 'w'))
"
fi
done < <(python3 << 'PYEOF'
import json, os
arts = json.load(open('artifacts.json'))
completed = set()
if os.path.exists('completed.json'):
completed = set(json.load(open('completed.json')))
# Read status from last poll
try:
with open('nlm-poll.log') as f:
lines = f.readlines()
# Find the most recent status output
status_line = ''
for line in reversed(lines):
if line.strip().startswith('[') and '"status"' in line:
status_line = line.strip()
break
# Actually we need to re-check, skip this approach
# The status is checked via nlm studio status above
pass
except:
pass
PYEOF
)
# Check if all done
ALL_DONE=$(python3 -c "
import json, os
arts = json.load(open('artifacts.json'))
completed = set()
if os.path.exists('completed.json'):
completed = set(json.load(open('completed.json')))
remaining = [a['type'] for a in arts if a['type'] not in completed]
print('yes' if not remaining else 'no')
")
if [[ "$ALL_DONE" == "yes" ]]; then
log "All artifacts completed!"
touch nlm-done.flag
notify_user "🎉 全部 NotebookLM 产物生成完成！共 $POLL_COUNT 轮轮询。"
exit 0
fi
log "Still in_progress. Sleeping ${INTERVAL}s..."
sleep "$INTERVAL"
done
⚠️ Simplified Alternative: If the above multi-artifact poll is too complex, use a simpler approach — one poll script per artifact type, all launched in parallel. Each follows the single-artifact pattern from notebooklm-content-creation SKILL.md.
3.5 Write artifacts.json
Written by the agent at setup time:
[
{
"type": "audio",
"id": "<artifact_id_from_create>",
"download_cmd": "nlm download audio",
"download_ext": "mp3"
}
]
If multiple artifacts selected, each gets its own entry.
3.6 Launch NotebookLM Poll
cd /tmp/deep-research-to-notebooklm/<task-dir>/
nohup bash nlm-poll.sh > /dev/null 2>&1 &
Step 4 — Notifications Summary
| Event | Method | Message |
|---|
| DR complete | notify_user | "✅ Deep Research 完成！报告已保存（SIZE）。" |
| DR complete | trigger_agent | Full parameters for NotebookLM |
| DR timeout | notify_user | "❌ Deep Research 超时（20分钟）。" |
| DR failure | notify_user | "❌ Deep Research 失败。" |
| Each artifact complete | notify_user | "✅ AUDIO 生成完成！" (with path if downloaded) |
| All artifacts complete | notify_user | "🎉 全部产物生成完成！" |
| NotebookLM timeout | notify_user | "⏰ 产物生成超时（40分钟）。未完成：X, Y" |
| Artifact failure | notify_user | "❌ X 生成失败。" |
All notifications use openclaw message send (direct to Discord, no agent processing).
Temp Directory Structure
/tmp/deep-research-to-notebooklm/
<YYMMDD-HHmm>_<slug>/
task.json ← full task config
dr-poll.log ← DR polling log
dr-done.flag ← DR completion marker
dr-poll.sh ← DR polling script
artifacts.json ← NotebookLM artifact IDs
nlm-poll.log ← NotebookLM polling log
nlm-done.flag ← NotebookLM completion marker
nlm-poll.sh ← NotebookLM polling script
completed.json ← list of completed artifact types
<report>.md ← saved DR report
Quick Reference
User says: "帮我做个关于 XX 的深度研究，然后生成播客"
→ Agent confirms params → Start DR → Auto-chain to NotebookLM
User says: "深度研究 XX，生成音频和视频"
→ Agent confirms → Start DR → Auto-chain to NotebookLM (audio + video in parallel)
User says: "研究一下 XX 并下载播客文件"
→ Agent confirms with download=true → DR → NotebookLM → download on complete
