dashboard

LCARS Dashboard

Display current plugin state: rolling stats, drift patterns, correction effectiveness, and active thresholds.

Instructions

When user runs /lcars:dashboard:

1. Read the following files from ~/.claude/lcars/:

   • scores.jsonl — score ledger
   • thresholds.json — active thresholds
   • memory/correction-outcomes.jsonl — correction outcomes
   • memory/patterns.json — consolidated patterns
   • memory/session-summaries.jsonl — session summaries

2. Compute and display:

Rolling Stats (7 days)

Run: python3 ${CLAUDE_PLUGIN_ROOT}/lib/score.py is available for ad-hoc scoring, but for stats, read scores.jsonl directly and compute:

• Total responses scored
• Drift rate (responses with filler > 0 or preamble > 0) / total
• Average info density
• Average word count
• Most common query types

Correction Fitness

Run: python3 -c "import sys; sys.path.insert(0, '${CLAUDE_PLUGIN_ROOT}/lib'); from fitness import fitness_rate; import json; r = fitness_rate(); print(json.dumps(r, indent=2) if r else 'No correction data yet.')"

Display the rate with interpretation:

• = 0.70: "Corrections working well"

• 0.50-0.70: "Some strategies may be ineffective"
• < 0.50: "Corrections may be adding noise"
• No data: "No corrections evaluated yet"

Active Thresholds

Read thresholds.json and display the global defaults plus any query-type overrides in a table.

Validated Patterns

Read memory/patterns.json and list any validated or stale patterns with their session counts and date ranges.

Tool Registry

Read ~/.claude/lcars/memory/tool-registry.json and display tracked tools by tier:

python3 -c "
import sys, json
sys.path.insert(0, '${CLAUDE_PLUGIN_ROOT}/lib')
import registry
reg = registry.load()
tools = reg.get('tools', [])
by_tier = {}
for t in tools:
    tier = t.get('tier', 'candidate')
    by_tier.setdefault(tier, []).append(t)
for tier in ['promoted', 'standard', 'candidate']:
    group = by_tier.get(tier, [])
    if group:
        print(f'\n{tier.upper()} ({len(group)}):')
        for t in group:
            inv = t.get('lifetime_invocations', 0)
            rate = t.get('lifetime_successes', 0) / inv if inv > 0 else None
            rate_str = f'{rate:.0%}' if rate is not None else 'n/a'
            print(f'  {t[\"name\"]:15s}  inv={inv:3d}  fitness={rate_str:>4s}  {t.get(\"status\", \"\")}')
archived = len(by_tier.get('archived', []) + [t for t in tools if t.get('status') == 'archived'])
if archived:
    print(f'\nARCHIVED: {archived}')
"

Show promoted tools (injected into context), standard tools (tracked), and candidate count. Include fitness rate for tools with usage.

Recent Drift Events

Read the last 10 entries from scores.jsonl that have padding_count > 0 or answer_position > 0, showing timestamp, query type, and which dimensions drifted.

Output Format

Present as a compact summary. No preambles. Data first.