refactor!: rebrand CCAM to AgentHub

Clean break, no compatibility alias. Renames the product name, the CLI
binary (`ccam` -> `agenthub`), the env var prefix (`CCAM_` ->
`AGENTHUB_`), the Prometheus metric namespace (`ccam_*` ->
`agenthub_*`), the tmux session prefix (`ccam-lane-<id>` ->
`agenthub-lane-<id>`), the per-repo runtime profile directory
(`.ccam/profile` -> `.agenthub/profile`), the lanes state root
(`~/.claude/ccam-lanes` -> `~/.claude/agenthub-lanes`), the bundled
plugins (`plugins/ccam*` -> `plugins/agenthub*`) and their slash
commands (`/ccam-doctor` -> `/agenthub-doctor`, `/ccam:*` ->
`/agenthub:*`).

Because there is no alias, an existing install has to be updated by
hand: rename its `.ccam/profile` directory and any `CCAM_*` variables,
and reinstall the plugin under its new name.

Two other name families are deliberately left alone here, since they are
not just strings: the `Claude-Code-Monitor` repository/marketplace name,
and the `agent-dashboard` npm package plus its `~/.claude/agent-dashboard`
runtime directory, which holds the live database and needs a migration
rather than a rename.

Verified: server 1406/1406, client 459/459, `grep -i ccam` clean across
tracked files, `openapi.yaml` regenerated (95 paths, unchanged count).

plugins/agenthub-insights/commands/anomalies.md

@@ -0,0 +1,23 @@
+---
+description: List current cost and token outlier sessions via z-score
+argument-hint: "[threshold]"
+---
+
+List the current cost/token **outlier** sessions from the Agent Monitor dashboard using a z-score test. **$ARGUMENTS** optionally sets the z-score threshold (default `2.0`; lower = stricter).
+
+1. Fetch the population:
+   - `curl -s "http://localhost:4820/api/sessions?limit=200"` → a session list; each item has `id`, `status`, `model`, `cwd`, `started_at`, `cost`, and `metadata`.
+
+2. Compute the baseline over all returned sessions:
+   - Mean and standard deviation of `cost`.
+   - For sessions where you need token totals, pull `curl -s http://localhost:4820/api/pricing/cost/<id>` and sum `input_tokens + output_tokens + cache_read_tokens + cache_write_tokens`; compute mean and stddev of total tokens too.
+
+3. Flag outliers: any session whose `z = (value − mean) / stddev` exceeds the threshold (default 2.0) on cost (primary) or tokens (secondary). Skip the calc gracefully if stddev is 0.
+
+4. Print the flagged sessions, sorted by descending cost z-score:
+   - Session id (short), model, started_at.
+   - Cost (USD, 4 decimals) and its z-score.
+   - Total tokens and its z-score (when fetched).
+   - A flag tag: 🔴 if z > 3, 🟡 if z > 2.
+
+Output rules: a Markdown table of flagged sessions only; currency in USD to 4 decimals; z-scores to 2 decimals; if nothing exceeds the threshold, say "No cost/token outliers above z=<threshold>" and report the top session by cost for context. Cite only API values — never fabricate. If the dashboard is unreachable at `http://localhost:4820`, tell the user to start it with `npm start` from the repo root.

plugins/agenthub-insights/commands/compare.md

@@ -0,0 +1,26 @@
+---
+description: Compare two sessions side-by-side with cost and workflow deltas
+argument-hint: "[sessionA] [sessionB]"
+---
+
+Compare the two sessions in **$ARGUMENTS** (first id = Session A, second id = Session B) side-by-side using the Agent Monitor dashboard. If fewer than two ids are given, ask for both.
+
+1. Fetch cost for each, in parallel:
+   - `curl -s http://localhost:4820/api/pricing/cost/<sessionA>`
+   - `curl -s http://localhost:4820/api/pricing/cost/<sessionB>`
+   Each returns `{ total_cost, breakdown:[{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] }`.
+
+2. Fetch workflow intelligence for each:
+   - `curl -s http://localhost:4820/api/workflows/<sessionA>`
+   - `curl -s http://localhost:4820/api/workflows/<sessionB>`
+   Use `stats` (tool/event/agent counts), `complexity` (score), `effectiveness` (subagent success), `compaction` (impact), and `errorPropagation`.
+
+3. Print a side-by-side comparison table with a delta column (B − A):
+   - Total cost (USD, 4 decimals) and Δ% .
+   - Tokens: input, output, cache_read, cache_write (sum the breakdown per session).
+   - Cache hit rate = `cache_read / (cache_read + input)`.
+   - Tool count, event count, agent count (from `stats`).
+   - Complexity score (from `complexity`).
+   - Subagent success rate (from `effectiveness`) and compaction count (from `compaction`).
+
+Output rules: one row per metric with columns Session A | Session B | Δ; use ▲ when B is higher and ▼ when lower; currency in USD to 4 decimals; rates as percentages to 2 decimals. End with a one-line verdict on which session was cheaper/leaner and the main driver. Cite only fields the API returned — never fabricate. If a session id is unknown or the dashboard is unreachable at `http://localhost:4820`, say so and tell the user to start it with `npm start` from the repo root.

plugins/agenthub-insights/commands/insights.md

@@ -0,0 +1,22 @@
+---
+description: Surface the top 3 data-backed insights about your Claude Code usage right now
+---
+
+Produce the **top 3 insights** about Claude Code usage right now, each backed by real numbers from the Agent Monitor dashboard.
+
+1. Fetch high-level state:
+   - `curl -s http://localhost:4820/api/stats` → total_sessions, active_sessions, active_agents, total_events, events_today, agents_by_status, sessions_by_status.
+   - `curl -s http://localhost:4820/api/analytics` → tokens (total_input/total_output/total_cache_read/total_cache_write), tool_usage (top 20), daily_events (365d), daily_sessions (365d), event_types, avg_events_per_session, total_subagents.
+
+2. Derive signal, citing exact field values:
+   - Cache hit rate = `total_cache_read / (total_cache_read + total_input)`.
+   - Activity trend: compare the last 7 days of `daily_sessions`/`daily_events` against the prior 7.
+   - Concentration: the single most-used tool and most-frequent `event_type`, with its share of the total.
+   - Error pressure: `APIError` share of events; subagent fan-out via `total_subagents` and `avg_events_per_session`.
+
+3. Pick the **3 most decision-relevant** findings (biggest cost lever, sharpest trend, or clearest anomaly). For each print:
+   - A one-line headline with the supporting number.
+   - Why it matters in one sentence.
+   - One concrete action.
+
+Output rules: rank by impact (most important first); currency in USD to 4 decimals; rates as percentages to 2 decimals; use ▲/▼ for trend direction; cite only fields the API returned — never fabricate. If `curl` cannot reach `http://localhost:4820`, tell the user to start the dashboard with `npm start` from the repo root.