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.
This commit is contained in:
2026-07-29 17:07:45 +07:00
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---
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. **$ARGUMENTS** optionally sets the z-score threshold (default `2.0`; lower = stricter).
1. Fetch the population:
- `curl -s "http://localhost:4820/api/sessions?limit=200"` → a session list; each item has `id`, `status`, `model`, `cwd`, `started_at`, `cost`, and `metadata`.
2. Compute the baseline over all returned sessions:
- Mean and standard deviation of `cost`.
- For sessions where you need token totals, pull `curl -s http://localhost:4820/api/pricing/cost/<id>` and sum `input_tokens + output_tokens + cache_read_tokens + cache_write_tokens`; compute mean and stddev of total tokens too.
3. Flag outliers: any session whose `z = (value mean) / stddev` exceeds the threshold (default 2.0) on cost (primary) or tokens (secondary). Skip the calc gracefully if stddev is 0.
4. Print the flagged sessions, sorted by descending cost z-score:
- Session id (short), model, started_at.
- Cost (USD, 4 decimals) and its z-score.
- Total tokens and its z-score (when fetched).
- A flag tag: 🔴 if z > 3, 🟡 if z > 2.
Output rules: a Markdown table of flagged sessions only; currency in USD to 4 decimals; z-scores to 2 decimals; if nothing exceeds the threshold, say "No cost/token outliers above z=<threshold>" and report the top session by cost for context. Cite only API values — never fabricate. If the dashboard is unreachable at `http://localhost:4820`, tell the user to start it with `npm start` from the repo root.
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---
description: Compare two sessions side-by-side with cost and workflow deltas
argument-hint: "[sessionA] [sessionB]"
---
Compare the two sessions in **$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.
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---
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.