nntrivi2001
57dc91585d
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.
67 lines
3.3 KiB
Markdown
67 lines
3.3 KiB
Markdown
---
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description: >
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Benchmark one session (or a small recent set) against the rolling average using
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Agent Monitor data — cost, total tokens, tool count, and workflow complexity
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score — and report where each metric lands as a percentile of the population.
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Tells you whether a session was normal, cheap, or an outlier. Use when judging
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whether a session was typical or out of band.
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---
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# Benchmark
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Score a session against the rolling population average and report its percentile on
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cost, tokens, tool count, and complexity using Agent Monitor data.
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## Input
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The user provides: **$ARGUMENTS**
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This may be:
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- A single session ID — benchmark that session
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- "latest" — benchmark the most recent session
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- "latest N" — benchmark the N most recent sessions, each vs the average
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- empty — benchmark the most recent session (default)
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## Data Sources
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| Endpoint | Returns |
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|----------|---------|
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| `GET /api/sessions?limit=N` | Population of sessions with `cost`, `model`, `started_at`, `metadata` (turn_count, total_turn_duration_ms) — builds the rolling baseline |
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| `GET /api/pricing/cost/{sessionId}` | `{ total_cost, breakdown:[{ input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost }] }` — the target session's cost and tokens |
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| `GET /api/workflows/{sessionId}` | `complexity` (score), `stats` (tool/event counts), `toolFlow` (distinct tools used) — the target session's tool count and complexity |
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| `GET /api/analytics` | `avg_events_per_session`, `tool_usage`, `daily_sessions` — corroborates population-level averages |
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## Report Sections
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### 1. Build the Baseline
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Fetch the population with `GET /api/sessions?limit=200` (the rolling set). For each
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session gather cost (`GET /api/pricing/cost/{id}` or the list `cost` field), total
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tokens (sum of the 4 token types from the pricing breakdown), tool count and
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complexity (`GET /api/workflows/{id}`). Compute mean, median, and standard
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deviation for each metric across the population.
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### 2. Measure the Target
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For the requested session, pull the same four metrics:
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- **Cost** — `total_cost` from `GET /api/pricing/cost/{id}`.
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- **Total tokens** — `input + output + cache_read + cache_write` summed from the breakdown.
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- **Tool count** — distinct/total tools from `GET /api/workflows/{id}` `stats`/`toolFlow`.
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- **Complexity score** — `complexity.score` from `GET /api/workflows/{id}`.
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### 3. Percentile and Deviation
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For each metric report the target's percentile within the population (share of
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sessions at or below it) and its z-score `(value − mean) / stddev`. Label each:
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below average / typical / above average / outlier (|z| > 2).
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### 4. Verdict
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State whether the session was normal overall. If it is an outlier, name which
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metric drove it (e.g., complexity p96, cost p91 → an unusually heavy session).
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## Output
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- A Markdown table: metric | session value | population mean | percentile | z-score | label.
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- Currency in USD to 4 decimals; tokens and tool counts as integers; complexity to 2 decimals.
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- Use ▲ for above-average and ▼ for below-average vs the mean.
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- One-line verdict: "Normal session" or "Outlier — driven by <metric> (pNN)".
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- When benchmarking multiple sessions, one row block per session plus a summary line.
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- Read-only: percentiles come only from the fetched population; never fabricate the baseline.
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