Three-tier cascade taxonomy quality monitor. Assesses whether Heart/Mind taxonomy tags and collection_tags on memory documents are CORRECT (not just present). Uses T0 heuristic → T1.5 classifier/GPT → T2 Brandon teacher cascade. Accumulates training labels for autonomous quality assessment. Heart/Mind field conventions: - `mind` (8 SPARTA tactical tags: Detect/Evade/Exploit/Harden/Isolate/Model/Persist/Restore) lives on `sparta_qra` documents. - `heart` (6 emotional tags: anger/fear/joy/neut...
Scanned 9/11/2026
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---
name: monitor-taxonomy
description: >
Three-tier cascade taxonomy quality monitor. Assesses whether Heart/Mind
taxonomy tags and collection_tags on memory documents are CORRECT (not just
present). Uses T0 heuristic → T1.5 classifier/GPT → T2 Brandon teacher cascade.
Accumulates training labels for autonomous quality assessment.
Heart/Mind field conventions:
- `mind` (8 SPARTA tactical tags: Detect/Evade/Exploit/Harden/Isolate/Model/Persist/Restore)
lives on `sparta_qra` documents.
- `heart` (6 emotional tags: anger/fear/joy/neutral/sadness/trust)
lives on `lessons` documents.
triggers:
- monitor taxonomy
- taxonomy quality
- check taxonomy quality
- taxonomy cascade
- mind tag quality
- heart tag quality
- run taxonomy monitor
allowed-tools: Bash
metadata:
short-description: Three-tier cascade taxonomy quality monitor (Heart/Mind)
provides:
- monitor-taxonomy
composes:
- task-monitor
- agentic-evals
disciplines:
- observability-operations
- memory-knowledge
---
# Monitor Taxonomy
Taxonomy quality monitor that assesses correctness of Heart/Mind tags and
collection_tags on memory documents via a three-tier cascade.
**Key distinction from monitor-memory**: monitor-memory checks coverage/method/drift.
monitor-taxonomy checks **correctness** via cascade validation.
## Heart/Mind Fields
| Field | Collection | Valid vocabulary (13 total) |
|---------|--------------|----------------------------------------------------------------------|
| `mind` | `sparta_qra` | Detect, Evade, Exploit, Harden, Isolate, Model, Persist, Restore |
| `heart` | `lessons` | anger, fear, joy, sadness, trust |
The cascade reads whichever field (`mind` or `heart`) is present in the input
document. The legacy key `bridge_attributes` is accepted as a fallback but
should not appear on new documents.
## 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.
## Architecture
```
Document with `mind` (QRA) or `heart` (lessons) tags
│
┌────▼────┐
│ Tier 0 │ Heuristic: vocabulary validation, null check,
│ (instant) │ text-tag coherence (keyword overlap score)
└────┬────┘
│ confidence < 0.80
┌────▼─────┐
│ Tier 1.5 │ Trained classifier (after 50+ labels)
│ (~200ms) │ OR small GPT (after /create-gpt training)
└────┬─────┘
│ confidence < 0.85 ("maybe" zone)
┌────▼────┐
│ Tier 2 │ Brandon (scillm persona) — authoritative teacher
│ (~3s) │ Full semantic assessment → training_labels.jsonl
└────┬────┘
│
Grade: CORRECT / MISTAGGED / MISSING / HALLUCINATED
Action: keep / re-extract / remove / flag
```
## Commands
```bash
# Run all probes
./run.sh check --json
# Run a specific tier
./run.sh check --tier 0 --autofix --json
# Run a single probe
./run.sh check --probe null-bridge-gc --json
# Dashboard
./run.sh dashboard
# Status (labels, shadow, classifier)
./run.sh status
# Register nightly schedule
./run.sh register-nightly
# Help
./run.sh help
```
## Probes
### Tier 0 — Heuristic Quality (instant, deterministic)
| ID | Probe | Collections checked | Auto-Fix |
|-----|------------------------------|---------------------------|----------|
| P01 | null-tag-gc | sparta_qra (mind), lessons (heart) | Yes |
| P02 | vocabulary-violation | sparta_qra (mind), lessons (heart) | Yes |
| P03 | text-tag-coherence | sparta_qra (mind), lessons (heart) | No |
| P04 | collection-tag-violation | lessons | Yes |
| P05 | stale-taxonomy | sparta_qra, lessons | No |
### Tier 1.5 — Classifier/GPT Quality (after training)
| ID | Probe | Auto-Fix |
|-----|---------------------------|----------|
| P10 | classifier-quality-check | No |
| P11 | shadow-agreement | No |
| P12 | confidence-distribution | No |
### Tier 2 — Brandon Teacher + Training
| ID | Probe | Auto-Fix |
|-----|--------------------|----------|
| P20 | teacher-validate | No |
| P21 | label-accumulation | No |
| P22 | shadow-tracking | No |
| P23 | retrain-trigger | Yes |
## Nightly Schedule
```
03:00 monitor-taxonomy T0: Heuristic quality (P01-P05) + auto-fix
03:15 monitor-taxonomy T1.5: Classifier quality (P10-P12) — skips if no model
03:30 monitor-taxonomy T2: Brandon teacher (P20-P23) — validates flagged docs
```
Runs BEFORE monitor-memory at 05:00. Taxonomy fixes applied before coverage measured.
## Environment Variables
| Variable | Default | Description |
|-------------------------------|--------------------------------|--------------------------|
| `ARANGO_URL` | `http://127.0.0.1:8529` | ArangoDB endpoint |
| `ARANGO_DB` | `memory` | Database name |
| `MONITOR_TAXONOMY_STATE_DIR` | `~/.pi/monitor-taxonomy` | State directory |
| `TAXONOMY_RETRAIN_THRESHOLD` | `50` | Labels to trigger retrain|
| `TAXONOMY_SAMPLE_SIZE` | `100` | Nightly sample size |
| `TAXONOMY_COHERENCE_THRESHOLD` | `0.20` | Min keyword overlap |
| `TAXONOMY_STALE_DAYS` | `90` | Days before stale |
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