feat: RSO observation system, child safety, Discord adapter, Telegram watchdog, email attachments
Core agent improvements: - RSO (Relevance Scoring & Observation) system: interaction_logger, memory_scorer, signal_detector - Memory access logging (memory_access_log table) for relevance scoring; high-signal turn detection - Rich conversation storage for notable turns; compact_conversation truncates long user messages - Task-type classifier (query/action/analysis/creative) for observation tagging - Nested sub-agent visibility: deep delegations now register against the main agent's manager Child safety (Gabriel profile): - child_safety.py: filtering, audit logging, prompt constants for restricted sessions - .kiro/specs/child-safety-profile: requirements, design, tasks specs - GABRIEL_BOT_PROPOSAL.md: initial proposal doc - Reduced context window (10 msgs) and tutor-mode identity for restricted users Telegram adapter: - Polling watchdog: auto-restarts updater if polling drops unexpectedly - get_me() with exponential-backoff retry on NetworkError at startup - Correct stop() ordering: signal watchdog before cancelling tasks Email / Gmail: - send_email: supports file attachments (attachments list param) - get_email: surfaces attachment metadata in response Scheduled tasks / weather: - Remove OpenWeatherMap API calls from morning-weather task; use wttr.in exclusively - New scheduled tasks and scheduler state persistence Discord: - adapters/discord/__init__.py scaffold - discord-plugin: MCP plugin for Claude Code Discord integration (server.ts, skills, config) Infrastructure: - n8n workflow exports (garvis_webhook, content_pipeline variants) - memory_workspace: context, homelab-repo-updates, weekly observation summaries, error logs - UCS C240 migration plan doc - requirements.txt: new deps - .claude/settings.json, fix_hooks.py: hook/permission tuning
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@@ -5,17 +5,19 @@ tasks:
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# Morning briefing - sent to Slack/Telegram
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- name: morning-weather
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prompt: |
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Check the user profile (Jordan.md) for the location (Centennial, CO). Use the get_weather tool with OpenWeatherMap API to fetch the current weather.
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Also use web_fetch to get today's high/low from a weather service:
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Fetch the current weather for Centennial, CO using web_fetch:
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https://wttr.in/Centennial,CO?format=j1
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Parse the JSON response to extract:
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- maxtempF (today's high)
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- mintempF (today's low)
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- current_condition[0].temp_F (current temp)
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- current_condition[0].FeelsLikeF (feels like)
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- weather[0].maxtempF (today's high)
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- weather[0].mintempF (today's low)
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- current_condition[0].weatherDesc[0].value (conditions)
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- current_condition[0].windspeedMiles (wind speed)
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Format the report as:
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🌤️ **Weather Report for Centennial, CO**
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- Current: [current]°F (feels like [feels_like]°F)
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- Today's High: [high]°F
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@@ -23,7 +25,7 @@ tasks:
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- Conditions: [conditions]
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- Wind: [wind speed] mph
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- Recommendation: [brief clothing/activity suggestion]
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Keep it brief and friendly!
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schedule: "daily 06:00"
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enabled: true
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@@ -59,7 +61,7 @@ tasks:
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send_to_platform: "telegram"
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send_to_channel: "8088983654"
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# Daily API cost report
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# Daily API cost report — DISABLED (bot runs on Max subscription, no per-token billing)
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- name: daily-cost-report
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prompt: |
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Generate a daily API usage and cost report:
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@@ -88,6 +90,181 @@ tasks:
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Keep it clear and actionable!
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schedule: "daily 23:00"
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enabled: false
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send_to_platform: "telegram"
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send_to_channel: "8088983654"
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# RSO Phase 2 — Weekly Reflection Agent
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- name: rso-weekly-reflection
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prompt: |
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You are the Weekly Reflection Agent for the RSO (Reflective Self-Optimization) system.
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Your job is to analyze the past 7 days of interaction logs and produce a structured report.
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## Step 1 — Collect the data
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Run this command to gather and parse the last 7 days of logs:
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```
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python3 -c "
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import json, os, glob
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from datetime import datetime, timedelta
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from collections import Counter
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log_dir = 'memory_workspace/observation/logs'
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error_dir = 'memory_workspace/observation/errors'
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cutoff = datetime.now() - timedelta(days=7)
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interactions, signals, errors = [], [], []
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for path in sorted(glob.glob(f'{log_dir}/*.jsonl')):
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date_str = os.path.basename(path).replace('.jsonl','')
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try:
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file_date = datetime.strptime(date_str, '%Y-%m-%d')
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if file_date < cutoff:
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continue
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except ValueError:
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continue
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with open(path) as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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r = json.loads(line)
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if r.get('record_type') == 'interaction':
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interactions.append(r)
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elif r.get('record_type') == 'signal_patch':
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signals.append(r)
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for path in sorted(glob.glob(f'{error_dir}/*.jsonl')):
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date_str = os.path.basename(path).replace('.jsonl','')
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try:
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file_date = datetime.strptime(date_str, '%Y-%m-%d')
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if file_date < cutoff:
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continue
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except ValueError:
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continue
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with open(path) as f:
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for line in f:
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line = line.strip()
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if not line:
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continue
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errors.append(json.loads(line))
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durations = [r['response']['duration_ms'] for r in interactions]
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task_types = Counter(r['request']['task_type'] for r in interactions)
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complexity = Counter(r['request']['complexity'] for r in interactions)
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all_tools = []
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for r in interactions:
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all_tools.extend(t['tool'] for t in r['response'].get('tool_calls', []))
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tool_counts = Counter(all_tools)
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sig_pos = sum(1 for r in signals if isinstance(r.get('signal'), dict) and r['signal'].get('explicit_positive'))
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sig_neg = sum(1 for r in signals if isinstance(r.get('signal'), dict) and r['signal'].get('explicit_negative'))
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sig_correction = sum(1 for r in signals if isinstance(r.get('signal'), dict) and r['signal'].get('correction_followed'))
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follow_types = Counter(r['signal']['follow_up_type'] for r in signals if isinstance(r.get('signal'), dict))
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slow = [r for r in interactions if r['response']['duration_ms'] > 60000]
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timeouts = [e for e in errors if 'timed out' in e.get('message','').lower()]
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print('=== STATS ===')
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print(f'Total interactions: {len(interactions)}')
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print(f'Total signals: {len(signals)}')
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print(f'Total errors: {len(errors)}, Timeouts: {len(timeouts)}')
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print(f'Avg duration: {sum(durations)/len(durations)/1000:.1f}s' if durations else 'No data')
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print(f'Max duration: {max(durations)/1000:.1f}s' if durations else '')
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print(f'Slow (>60s): {len(slow)} ({len(slow)*100//len(interactions) if interactions else 0}%)')
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print(f'Task types: {dict(task_types)}')
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print(f'Complexity: {dict(complexity)}')
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print(f'Signals — pos:{sig_pos} neg:{sig_neg} correction:{sig_correction} follow_types:{dict(follow_types)}')
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print(f'Top 12 tools:')
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for tool, count in tool_counts.most_common(12):
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print(f' {tool}: {count}')
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print(f'Slow interactions:')
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for r in sorted(slow, key=lambda x: x['response']['duration_ms'], reverse=True)[:10]:
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print(f' {r[\"response\"][\"duration_ms\"]//1000}s — {r[\"request\"][\"message_preview\"][:70]}')
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print(f'Errors:')
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for e in errors:
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print(f' [{e.get(\"timestamp\",\"\")[:10]}] {e.get(\"error_type\",\"?\")} — {e.get(\"message\",\"\")[:120]}')
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"
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```
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## Step 2 — Run the memory relevance scorer
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Run this to score all indexed memory files:
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```
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python -c "
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import sys
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sys.path.insert(0, '.')
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from observation.memory_scorer import MemoryRelevanceScorer
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scorer = MemoryRelevanceScorer('./memory_workspace')
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report = scorer.score_all(lookback_days=30)
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s = report['summary']
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print('=== MEMORY SCORES ===')
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print(f'Files scored: {report[\"files_scored\"]} cold_start: {report[\"cold_start\"]}')
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print(f'Core: {s[\"core_memory\"]} Active: {s[\"active_memory\"]} Archive: {s[\"archive_candidates\"]} Stale: {s[\"stale_candidates\"]}')
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for e in report['archive_recommendations'][:10]:
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flags = ','.join(e['staleness_flags']) or 'none'
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print(f' ARCHIVE {e[\"path\"]} score={e[\"score\"]:.1f} age={e[\"age_days\"]:.0f}d [{flags}]')
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scorer.write_report(lookback_days=30)
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"
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```
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## Step 3 — Write the analysis report
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Using the stats above, write a report answering these five questions. Be specific — use the actual numbers.
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**Q1: What went well?**
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- Interactions with positive signals and fast completions
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- Tools and task types that completed efficiently
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**Q2: What went wrong?**
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- Timeouts, errors, corrections from the user
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- Any recurring failure patterns
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**Q3: What patterns emerged?**
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- Most common task types and tools
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- Any repeated tool chains worth noting
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**Q4: What is being wasted?**
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- Slow interactions that could be faster
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- Redundant tool usage patterns
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- Any scheduled tasks producing no value
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- Include memory archive candidates from Step 2 (files with score <3 and age >=30d)
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**Q5: Recommendations (3–5 max, data-backed)**
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- Each one tagged: prompt | tool_usage | memory | code | config
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- Include the supporting number ("X of Y interactions showed...")
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- If there are memory archive candidates, include a `memory` recommendation
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## Step 4 — Save the report
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Determine the ISO week number:
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```
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python3 -c "from datetime import datetime; d=datetime.now(); print(f'week-{d.year}-{d.isocalendar()[1]:02d}')"
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```
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Save the report to:
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- `memory_workspace/observation/summaries/week-YYYY-WW.md` (use write_file)
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- Obsidian: use permanent_note to save it under `Projects/RSO/Reflections/week-YYYY-WW`
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## Step 5 — Send Telegram summary
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Send a brief summary (not the full report) to Jordan:
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Weekly Reflection Report -- Week WW
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[X] interactions | [Y]% positive signals | [Z] timeouts
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Memory: [A] archive candidates, [B] stale files
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Top findings:
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- [Finding 1]
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- [Finding 2]
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- [Finding 3]
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Full report saved to Obsidian -> Projects/RSO/Reflections/week-WW
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schedule: "weekly sun 20:00"
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enabled: true
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send_to_platform: "telegram"
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send_to_channel: "8088983654"
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4
config/scheduler_state.json
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4
config/scheduler_state.json
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@@ -0,0 +1,4 @@
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{
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"morning-weather": "2026-04-23T06:00:31.656886",
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"zettelkasten-daily-review": "2026-04-22T20:00:25.320736"
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}
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