Agent skill

monitor-episodic-archiver

Monitor episodic archiver health and run nightly analysis pipeline. Health dashboard, failure patterns, session aging alerts. Nightly: archive + high-fidelity taxonomy + user profiling + lessons.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/monitor-episodic-archiver

Metadata

Additional technical details for this skill

short description
Health monitoring + nightly pipeline for episodic archiver

SKILL.md

Monitor Episodic Archiver

Track session health, run nightly analysis with user profiling, and catch aging unresolved sessions.

Continuous Operation (Non-Negotiable)

This skill is always-on. It:

  • Runs on its configured schedule indefinitely — it NEVER stops unless explicitly halted by the user
  • The agent MUST NOT stop and wait for the human to ask for status or remember to check
  • If a cycle fails, diagnose the failure, attempt auto-repair, and continue
  • Only escalate to the human if genuinely blocked after exhausting /dogpile research
  • Gracefully handles restarts and maintains state across cycles
  • Is designed for multi-day/week/month autonomous operation

Anti-pattern: Reporting status and waiting for the human to ask "what next?" is UNACCEPTABLE. The agent must proactively fix issues and continue the monitoring loop.

Quick Start

bash
cd .pi/skills/monitor-episodic-archiver

# Health dashboard
./run.sh dashboard

# Quick health check (JSON for automation)
./run.sh check --json

# List unresolved sessions with aging flags
./run.sh list-unresolved

# Analyze failure patterns
./run.sh analyze-patterns

# Run nightly pipeline (archive + analyze + profile)
./run.sh nightly --hours 24

# Dry run (see what would be processed)
./run.sh nightly --dry-run --json

# Register for daily scheduler
./run.sh register-nightly

Nightly Pipeline

3:00 AM daily (via scheduler)
    |
    v
Archive recent sessions (--no-analyze, fast)
    |
    v
Re-analyze with high-fidelity taxonomy (LLM)
    |
    v
Extract user behavioral profiles
    |
    v
Merge into user_priors (RGMem incremental)
    |
    v
Store lessons to /memory with bridge tags
    |
    v
Health check + report
    |
    v
~/.pi/monitor-episodic-archiver/nightly_report.json

Model Selection

  • Default: deepseek-ai/DeepSeek-V3.1-TEE (0.60s latency, 6 instances)
  • Override: ./run.sh nightly --model <model-id>
  • Set via CHUTES_MODEL_ID env var

Commands

dashboard - Rich health overview

check - Automated health check (exit 0/1/2)

list-unresolved - Sessions needing attention

analyze-patterns - Failure pattern analysis

nightly - Full nightly pipeline

register-nightly - Register with scheduler

Health Criteria

Metric Healthy Warning Critical
Unresolved rate <20% 20-40% >40%
Oldest unresolved <14 days 14-30 days >30 days
Success rate >75% 50-75% <50%

Integration

Skill How
episodic-archiver Queries collections, runs archive/analyze
task-monitor Reports pipeline progress via registry.json
scheduler Registers nightly job at 3am
memory Stores lessons from resolved sessions
taxonomy High-fidelity bridge classification
scillm LLM completions for analysis + profiling

State Files

~/.pi/monitor-episodic-archiver/
  health_report.json       # Latest health check
  nightly_report.json      # Latest nightly pipeline report
  nightly_state.json       # Nightly run history
  task_state_nightly.json  # Task-monitor progress tracking
  pattern_cache.json       # Cached failure patterns
  alert_history.jsonl      # Alert log

Environment Variables

Variable Default Description
ARANGO_URL http://localhost:8529 ArangoDB connection
ARANGO_DB memory Database name
CHUTES_MODEL_ID deepseek-ai/DeepSeek-V3.1-TEE LLM model for nightly
ALERT_AGE_DAYS 14 Days before session flagged
CRITICAL_AGE_DAYS 30 Days before critical alert

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