feat: Claude Code Monitor — lanes, pipelines and a merged workspace

Internal SmartGift build of a Claude Code monitoring dashboard.

Lanes: a durable unit of parallel agent work, one per working directory,
tracked across session restarts. Managed lanes are git worktrees the
dashboard provisions and can reset or remove behind a three-check destroy
guard and a counted preflight; adopted lanes are directories you already
own and are never destroyable.

Pipelines: a lane moves through pipeline stages. A stage the agent declares
with evidence renders green; a stage inferred from the tool-event stream
renders dashed amber and never counts as done. Detection is forward-only
within a 30-minute window, and never writes the declared stage.

Workspace: one page at /run with a lane grid, the selected lane's pipeline,
and a full Claude console behind a disclosure.

plugins/ccam-insights/skills/optimization-suggest/SKILL.md

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+---
+description: >
+  Suggest concrete optimizations for Claude Code usage based on historical
+  session data. Covers cost reduction, speed improvement, error prevention,
+  and workflow efficiency. Use for data-driven optimization planning.
+---
+
+# Optimization Suggest
+
+Generate data-driven optimization recommendations for Claude Code usage.
+
+## Input
+
+The user provides: **$ARGUMENTS**
+
+This may be:
+- "all" or empty (default: comprehensive optimization scan)
+- "cost" for cost reduction focus
+- "speed" for performance/speed focus
+- "quality" for error reduction focus
+- "efficiency" for workflow efficiency focus
+
+## Procedure
+
+1. **Gather optimization data** from `http://localhost:4820`:
+   - `GET /api/sessions?limit=200` — session history
+   - `GET /api/analytics` — tool and token analytics
+   - `GET /api/pricing/cost` — cost data
+   - `GET /api/pricing` — pricing rules for model comparison
+   - Sample event streams for behavioral analysis
+
+2. **Analyze optimization opportunities**:
+
+   ### 💰 Cost Optimization
+   - **Model downgrade opportunities**: Tasks completed with expensive models that could use cheaper ones
+     - Compare success rates per model per task type
+     - Calculate savings from model substitution
+   - **Cache optimization**: Sessions with low cache hit rates
+     - Identify sessions that could benefit from better prompt caching
+   - **Early termination**: Sessions that ran longer than needed
+     - Detect sessions where useful work completed well before session end
+   - **Compaction reduction**: Sessions hitting context limits
+     - Suggest breaking large tasks into smaller sessions
+
+   ### ⚡ Speed Optimization
+   - **Tool selection**: Faster alternatives for commonly-used tool patterns
+   - **Subagent parallelization**: Tasks that could run in parallel
+   - **Session planning**: Better upfront context to reduce back-and-forth
+   - **Preemptive context loading**: Frequently needed files/context
+
+   ### 🛡 Quality Optimization
+   - **Error prevention**: Common error patterns with preventive measures
+   - **Tool reliability**: Tools with high failure rates and alternatives
+   - **Validation gaps**: Sessions lacking verification steps
+   - **Recovery strategies**: Better error handling patterns
+
+   ### 🔄 Workflow Optimization
+   - **Session sizing**: Optimal session scope based on historical success
+   - **Task decomposition**: Complex sessions that should be split
+   - **Automation candidates**: Repetitive workflows to automate
+   - **Knowledge reuse**: Patterns where previous session context could help
+
+3. **Quantify each recommendation**:
+   - Estimated impact (cost savings $, time savings %, error reduction %)
+   - Implementation effort (low/medium/high)
+   - Confidence level based on data available
+   - Priority score = Impact × Confidence / Effort
+
+## Output Format
+
+Present as a prioritized optimization plan:
+
+| # | Recommendation | Category | Impact | Effort | Priority |
+|---|---------------|----------|--------|--------|----------|
+| 1 | Specific action | 💰/⚡/🛡/🔄 | High | Low | ★★★★★ |
+| 2 | Specific action | ... | ... | ... | ★★★★☆ |
+
+For the top 5 recommendations, include:
+- Detailed explanation with supporting data
+- Step-by-step implementation guide
+- Expected before/after metrics
+- How to measure success