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.
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
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_IDenv 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 |
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
agent-ops-spec
Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
agent-ops-testing
Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.
agent-ops-state
Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.
Didn't find tool you were looking for?