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feat: integrate Spark Intelligence into Timmy swarm system

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Adds a self-evolving cognitive layer inspired by vibeship-spark-intelligence,
adapted for Timmy's agent architecture. Spark captures swarm events, runs
EIDOS prediction-evaluation loops, consolidates memories, and generates
advisory recommendations — all backed by SQLite consistent with existing
patterns.

New modules:
- spark/memory.py — event capture with importance scoring + memory consolidation
- spark/eidos.py — EIDOS cognitive loop (predict → observe → evaluate → learn)
- spark/advisor.py — ranked advisory generation from accumulated intelligence
- spark/engine.py — top-level API wiring all subsystems together

Dashboard:
- /spark/ui — full Spark Intelligence dashboard (3-column: status/advisories,
  predictions/memories, event timeline) with HTMX auto-refresh
- /spark — JSON API for programmatic access
- SPARK link added to navigation header

Integration:
- Coordinator hooks emit Spark events on task post, bid, assign, complete, fail
- EIDOS predictions generated when tasks are posted, evaluated on completion
- Memory consolidation triggers when agents accumulate enough outcomes
- SPARK_ENABLED config toggle (default: true)

Tests: 47 new tests covering all Spark subsystems + dashboard routes.
Full suite: 538 tests passing.

https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c
This commit is contained in:
Claude
2026-02-24 15:51:15 +00:00
parent 4554891674
commit 1ab26d30ad
15 changed files with 2420 additions and 4 deletions
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src/dashboard/templates/spark.html Normal file
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{% extends "base.html" %}
{% block title %}Timmy Time — Spark Intelligence{% endblock %}
{% block content %}
<div class="container-fluid spark-container py-4">
<!-- Header -->
<div class="spark-header mb-4">
<div class="spark-title">SPARK INTELLIGENCE</div>
<div class="spark-subtitle">
Self-evolving cognitive layer &mdash;
<span class="spark-status-val">{{ status.events_captured }}</span> events captured,
<span class="spark-status-val">{{ status.memories_stored }}</span> memories,
<span class="spark-status-val">{{ status.predictions.evaluated }}</span> predictions evaluated
</div>
</div>
<div class="row g-3">
<!-- Left column: Status + Advisories -->
<div class="col-12 col-lg-4 d-flex flex-column gap-3">
<!-- EIDOS Status -->
<div class="card mc-panel">
<div class="card-header mc-panel-header">// EIDOS LOOP</div>
<div class="card-body p-3">
<div class="spark-stat-grid">
<div class="spark-stat">
<span class="spark-stat-label">PREDICTIONS</span>
<span class="spark-stat-value">{{ status.predictions.total_predictions }}</span>
</div>
<div class="spark-stat">
<span class="spark-stat-label">EVALUATED</span>
<span class="spark-stat-value">{{ status.predictions.evaluated }}</span>
</div>
<div class="spark-stat">
<span class="spark-stat-label">PENDING</span>
<span class="spark-stat-value">{{ status.predictions.pending }}</span>
</div>
<div class="spark-stat">
<span class="spark-stat-label">ACCURACY</span>
<span class="spark-stat-value {% if status.predictions.avg_accuracy >= 0.7 %}text-success{% elif status.predictions.avg_accuracy < 0.4 %}text-danger{% else %}text-warning{% endif %}">
{{ "%.0f"|format(status.predictions.avg_accuracy * 100) }}%
</span>
</div>
</div>
</div>
</div>
<!-- Event Counts -->
<div class="card mc-panel">
<div class="card-header mc-panel-header">// EVENT PIPELINE</div>
<div class="card-body p-3">
{% for event_type, count in status.event_types.items() %}
<div class="spark-event-row">
<span class="spark-event-type-badge spark-type-{{ event_type }}">{{ event_type | replace("_", " ") | upper }}</span>
<span class="spark-event-count">{{ count }}</span>
</div>
{% endfor %}
</div>
</div>
<!-- Advisories -->
<div class="card mc-panel"
hx-get="/spark/insights"
hx-trigger="load, every 30s"
hx-target="#spark-insights-body"
hx-swap="innerHTML">
<div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
<span>// ADVISORIES</span>
<span class="badge bg-info">{{ advisories | length }}</span>
</div>
<div class="card-body p-3" id="spark-insights-body">
{% if advisories %}
{% for adv in advisories %}
<div class="spark-advisory priority-{{ 'high' if adv.priority >= 0.7 else ('medium' if adv.priority >= 0.4 else 'low') }}">
<div class="spark-advisory-header">
<span class="spark-advisory-cat">{{ adv.category | replace("_", " ") | upper }}</span>
<span class="spark-advisory-priority">{{ "%.0f"|format(adv.priority * 100) }}%</span>
</div>
<div class="spark-advisory-title">{{ adv.title }}</div>
<div class="spark-advisory-detail">{{ adv.detail }}</div>
<div class="spark-advisory-action">{{ adv.suggested_action }}</div>
</div>
{% endfor %}
{% else %}
<div class="text-center text-muted py-3">No advisories yet. Run more tasks to build intelligence.</div>
{% endif %}
</div>
</div>
</div>
<!-- Middle column: Predictions -->
<div class="col-12 col-lg-4 d-flex flex-column gap-3">
<!-- EIDOS Predictions -->
<div class="card mc-panel">
<div class="card-header mc-panel-header">// EIDOS PREDICTIONS</div>
<div class="card-body p-3">
{% if predictions %}
{% for pred in predictions %}
<div class="spark-prediction {% if pred.evaluated_at %}evaluated{% else %}pending{% endif %}">
<div class="spark-pred-header">
<span class="spark-pred-task">{{ pred.task_id[:8] }}...</span>
{% if pred.accuracy is not none %}
<span class="spark-pred-accuracy {% if pred.accuracy >= 0.7 %}text-success{% elif pred.accuracy < 0.4 %}text-danger{% else %}text-warning{% endif %}">
{{ "%.0f"|format(pred.accuracy * 100) }}%
</span>
{% else %}
<span class="spark-pred-pending-badge">PENDING</span>
{% endif %}
</div>
<div class="spark-pred-detail">
{% if pred.predicted %}
<div class="spark-pred-item">
<span class="spark-pred-label">Winner:</span>
{{ (pred.predicted.likely_winner or "?")[:8] }}
</div>
<div class="spark-pred-item">
<span class="spark-pred-label">Success:</span>
{{ "%.0f"|format((pred.predicted.success_probability or 0) * 100) }}%
</div>
<div class="spark-pred-item">
<span class="spark-pred-label">Bid range:</span>
{{ pred.predicted.estimated_bid_range | join("–") }} sats
</div>
{% endif %}
{% if pred.actual %}
<div class="spark-pred-actual">
<span class="spark-pred-label">Actual:</span>
{% if pred.actual.succeeded %}completed{% else %}failed{% endif %}
by {{ (pred.actual.winner or "?")[:8] }}
{% if pred.actual.winning_bid %} at {{ pred.actual.winning_bid }} sats{% endif %}
</div>
{% endif %}
</div>
<div class="spark-pred-time">{{ pred.created_at[:19] }}</div>
</div>
{% endfor %}
{% else %}
<div class="text-center text-muted py-3">No predictions yet. Post tasks to activate the EIDOS loop.</div>
{% endif %}
</div>
</div>
<!-- Consolidated Memories -->
<div class="card mc-panel">
<div class="card-header mc-panel-header">// MEMORIES</div>
<div class="card-body p-3">
{% if memories %}
{% for mem in memories %}
<div class="spark-memory-card mem-{{ mem.memory_type }}">
<div class="spark-mem-header">
<span class="spark-mem-type">{{ mem.memory_type | upper }}</span>
<span class="spark-mem-confidence">{{ "%.0f"|format(mem.confidence * 100) }}% conf</span>
</div>
<div class="spark-mem-content">{{ mem.content }}</div>
<div class="spark-mem-meta">
{{ mem.source_events }} events &bull; {{ mem.created_at[:10] }}
</div>
</div>
{% endfor %}
{% else %}
<div class="text-center text-muted py-3">Memories will form as patterns emerge.</div>
{% endif %}
</div>
</div>
</div>
<!-- Right column: Event Timeline -->
<div class="col-12 col-lg-4 d-flex flex-column gap-3">
<div class="card mc-panel"
hx-get="/spark/timeline"
hx-trigger="load, every 15s"
hx-target="#spark-timeline-body"
hx-swap="innerHTML">
<div class="card-header mc-panel-header d-flex justify-content-between align-items-center">
<span>// EVENT TIMELINE</span>
<span class="badge bg-secondary">{{ status.events_captured }} total</span>
</div>
<div class="card-body p-3 spark-timeline-scroll" id="spark-timeline-body">
{% if timeline %}
{% for ev in timeline %}
<div class="spark-event spark-type-{{ ev.event_type }}">
<div class="spark-event-header">
<span class="spark-event-type-badge">{{ ev.event_type | replace("_", " ") | upper }}</span>
<span class="spark-event-importance" title="Importance: {{ ev.importance }}">
{% if ev.importance >= 0.8 %}&#9679;&#9679;&#9679;{% elif ev.importance >= 0.5 %}&#9679;&#9679;{% else %}&#9679;{% endif %}
</span>
</div>
<div class="spark-event-desc">{{ ev.description }}</div>
{% if ev.task_id %}
<div class="spark-event-meta">task: {{ ev.task_id[:8] }}{% if ev.agent_id %} &bull; agent: {{ ev.agent_id[:8] }}{% endif %}</div>
{% endif %}
<div class="spark-event-time">{{ ev.created_at[:19] }}</div>
</div>
{% endfor %}
{% else %}
<div class="text-center text-muted py-3">No events captured yet.</div>
{% endif %}
</div>
</div>
</div>
</div>
</div>
<style>
/* ------------------------------------------------------------------ */
/* Spark Intelligence — Mission Control theme */
/* ------------------------------------------------------------------ */
.spark-container {
max-width: 1400px;
margin: 0 auto;
}
.spark-header {
border-left: 3px solid #00d4ff;
padding-left: 1rem;
}
.spark-title {
font-size: 1.6rem;
font-weight: 700;
color: #00d4ff;
letter-spacing: 0.08em;
font-family: 'JetBrains Mono', monospace;
}
.spark-subtitle {
font-size: 0.75rem;
color: #6c757d;
margin-top: 0.25rem;
}
.spark-status-val {
color: #00d4ff;
font-weight: 600;
}
/* Stat grid */
.spark-stat-grid {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 0.75rem;
}
.spark-stat {
display: flex;
flex-direction: column;
align-items: center;
padding: 0.5rem;
border: 1px solid #1a2a3a;
border-radius: 4px;
background: #0a1520;
}
.spark-stat-label {
font-size: 0.65rem;
color: #6c757d;
letter-spacing: 0.1em;
text-transform: uppercase;
}
.spark-stat-value {
font-size: 1.3rem;
font-weight: 700;
color: #f8f9fa;
font-family: 'JetBrains Mono', monospace;
}
/* Event pipeline rows */
.spark-event-row {
display: flex;
justify-content: space-between;
align-items: center;
padding: 0.4rem 0;
border-bottom: 1px solid #1a2a3a;
}
.spark-event-row:last-child {
border-bottom: none;
}
.spark-event-count {
font-weight: 600;
color: #adb5bd;
font-family: 'JetBrains Mono', monospace;
}
/* Event type badges */
.spark-event-type-badge {
font-size: 0.65rem;
padding: 0.15em 0.5em;
border-radius: 3px;
letter-spacing: 0.05em;
font-weight: 600;
}
.spark-type-task_posted .spark-event-type-badge,
.spark-event-type-badge.spark-type-task_posted { background: #1a3a5a; color: #5baaff; }
.spark-type-bid_submitted .spark-event-type-badge,
.spark-event-type-badge.spark-type-bid_submitted { background: #3a2a1a; color: #ffaa5b; }
.spark-type-task_assigned .spark-event-type-badge,
.spark-event-type-badge.spark-type-task_assigned { background: #1a3a2a; color: #5bffaa; }
.spark-type-task_completed .spark-event-type-badge,
.spark-event-type-badge.spark-type-task_completed { background: #1a3a1a; color: #5bff5b; }
.spark-type-task_failed .spark-event-type-badge,
.spark-event-type-badge.spark-type-task_failed { background: #3a1a1a; color: #ff5b5b; }
.spark-type-agent_joined .spark-event-type-badge,
.spark-event-type-badge.spark-type-agent_joined { background: #2a1a3a; color: #aa5bff; }
.spark-type-prediction_result .spark-event-type-badge,
.spark-event-type-badge.spark-type-prediction_result { background: #1a2a3a; color: #00d4ff; }
/* Advisories */
.spark-advisory {
border: 1px solid #2a3a4a;
border-radius: 6px;
padding: 0.75rem;
margin-bottom: 0.75rem;
background: #0d1b2a;
}
.spark-advisory.priority-high {
border-left: 3px solid #dc3545;
}
.spark-advisory.priority-medium {
border-left: 3px solid #fd7e14;
}
.spark-advisory.priority-low {
border-left: 3px solid #198754;
}
.spark-advisory-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.25rem;
}
.spark-advisory-cat {
font-size: 0.6rem;
color: #6c757d;
letter-spacing: 0.08em;
}
.spark-advisory-priority {
font-size: 0.65rem;
color: #adb5bd;
font-family: 'JetBrains Mono', monospace;
}
.spark-advisory-title {
font-weight: 600;
font-size: 0.9rem;
color: #f8f9fa;
margin-bottom: 0.25rem;
}
.spark-advisory-detail {
font-size: 0.8rem;
color: #adb5bd;
margin-bottom: 0.4rem;
line-height: 1.4;
}
.spark-advisory-action {
font-size: 0.75rem;
color: #00d4ff;
font-style: italic;
border-left: 2px solid #00d4ff;
padding-left: 0.5rem;
}
/* Predictions */
.spark-prediction {
border: 1px solid #1a2a3a;
border-radius: 6px;
padding: 0.6rem;
margin-bottom: 0.6rem;
background: #0a1520;
}
.spark-prediction.evaluated {
border-left: 3px solid #198754;
}
.spark-prediction.pending {
border-left: 3px solid #fd7e14;
}
.spark-pred-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.3rem;
}
.spark-pred-task {
font-size: 0.75rem;
color: #adb5bd;
font-family: 'JetBrains Mono', monospace;
}
.spark-pred-accuracy {
font-weight: 700;
font-size: 0.85rem;
font-family: 'JetBrains Mono', monospace;
}
.spark-pred-pending-badge {
font-size: 0.6rem;
background: #fd7e14;
color: #fff;
padding: 0.1em 0.4em;
border-radius: 3px;
font-weight: 600;
}
.spark-pred-detail {
font-size: 0.75rem;
color: #adb5bd;
}
.spark-pred-item {
padding: 0.1rem 0;
}
.spark-pred-label {
color: #6c757d;
font-weight: 600;
}
.spark-pred-actual {
margin-top: 0.3rem;
padding-top: 0.3rem;
border-top: 1px dashed #1a2a3a;
color: #dee2e6;
}
.spark-pred-time {
font-size: 0.6rem;
color: #495057;
margin-top: 0.3rem;
font-family: 'JetBrains Mono', monospace;
}
/* Memories */
.spark-memory-card {
border: 1px solid #1a2a3a;
border-radius: 6px;
padding: 0.6rem;
margin-bottom: 0.6rem;
background: #0a1520;
}
.spark-memory-card.mem-pattern {
border-left: 3px solid #198754;
}
.spark-memory-card.mem-anomaly {
border-left: 3px solid #dc3545;
}
.spark-memory-card.mem-insight {
border-left: 3px solid #00d4ff;
}
.spark-mem-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.25rem;
}
.spark-mem-type {
font-size: 0.6rem;
letter-spacing: 0.08em;
color: #6c757d;
font-weight: 600;
}
.spark-mem-confidence {
font-size: 0.65rem;
color: #adb5bd;
font-family: 'JetBrains Mono', monospace;
}
.spark-mem-content {
font-size: 0.8rem;
color: #dee2e6;
line-height: 1.4;
}
.spark-mem-meta {
font-size: 0.6rem;
color: #495057;
margin-top: 0.3rem;
}
/* Timeline */
.spark-timeline-scroll {
max-height: 70vh;
overflow-y: auto;
}
.spark-event {
border: 1px solid #1a2a3a;
border-radius: 4px;
padding: 0.5rem;
margin-bottom: 0.5rem;
background: #0a1520;
}
.spark-event-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 0.2rem;
}
.spark-event-importance {
font-size: 0.5rem;
color: #00d4ff;
}
.spark-event-desc {
font-size: 0.8rem;
color: #dee2e6;
}
.spark-event-meta {
font-size: 0.65rem;
color: #6c757d;
font-family: 'JetBrains Mono', monospace;
margin-top: 0.15rem;
}
.spark-event-time {
font-size: 0.6rem;
color: #495057;
font-family: 'JetBrains Mono', monospace;
}
/* Responsive */
@media (max-width: 992px) {
.spark-title { font-size: 1.2rem; }
.spark-stat-value { font-size: 1.1rem; }
}
</style>
{% endblock %}