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cost-session

Per-message cost breakdown within a single session. The drill-down companion to cost-anomaly — when an outlier session is flagged, this surfaces the specific expensive messages so operators can see whether the cost came from output tokens, cache writes, or model escalations.

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Repositório
ruvnet/ruflo
Última atividade na origem
16 de junho de 2026 às 16:10
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SKILL.md
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name
cost-session
description
Per-message cost breakdown within a single session. The drill-down companion to cost-anomaly — when an outlier session is flagged, this surfaces the specific expensive messages so operators can see whether the cost came from output tokens, cache writes, or model escalations.
argument-hint
[--session-id <id>] [--top 20] [--since <iso-ts>] [--format table|json]
allowed-tools
Bash
When cost-anomaly flags a session as a >3.5σ outlier, the next question is "which MESSAGES were expensive?". cost-session answers that. | Question | Skill | |---|---| | "Which sessions cost the most?" | `cost-conversation` | | "Which sessions are outliers?" | `cost-anomaly` | | **"Which messages in THIS session were expensive?"** | **`cost-session`** ← this | ## Algorithm Implementation: [`scripts/session.mjs`](../../scripts/session.mjs). 1. Resolve session jsonl: `--session-id <id>` (scans `~/.claude/projects/*/`) or `--latest` (default; picks most-recently-modified jsonl). 2. Parse all assistant messages with `usage` blocks. 3. Cost each message via shared PRICING (`_prices.mjs`). 4. Sort descending by `cost_usd`, surface top-N (default 20). 5. Compute p50/p90/p99 of message costs for in-session percentile context. 6. Flag the top message if it's >2× the p99 — that's an in-session outlier. ## Why this matters: cache writes are the silent cost Example real session, top message: ``` | # | Model | In | Out | Cache W | Cache R | Cost | | 1 | opus-4-7 | 6 | 569 | 881898 | 0 | $16.58 | ``` Without the **Cache W** column it looks like "569 output tokens cost $16" — that's wrong by 380×. The actual cost is ephemeral 1h cache write at opus pricing: 881,898 tokens × $18.75/1M = $16.54. Operators reading the table see immediately: "the model wrote 881K tokens to ephemeral cache". From there the question becomes "why did we cache 881K tokens of context for a 6-input request?" — that's a real engineering signal. ## Drill-down workflow ```bash # Step 1: find outliers across all sessions cost anomaly --alert-on-outliers 1 || cost anomaly # see which session-ids # Step 2: drill into the flagged session cost session --session-id <flagged-id> --top 10 # Step 3: open that jsonl at the timestamp the top message reports, # inspect the prompt + tool calls ``` ## Percentile context Top of output: ``` | p50 (median) message | $0.85 | | p90 message | $1.45 | | p99 message | $1.74 | ``` Lets operators ask "is this top message a 2× outlier or a 380× one?" without having to compute it themselves. The "top is >2× p99" footer fires when the answer is "yes, this is an in-session outlier worth investigating". ## --since filter Useful for drilling into a specific time range within a long session: ```bash cost session --since 2026-06-16T13:00:00Z --top 5 ``` Only messages with `timestamp >= --since` are considered. ## Edge cases - No costed assistant messages → "_No costed assistant messages_" + exit 0. - `--session-id` not found in any project's jsonls → exit 2 with error. - `--top` must be a positive integer → exit 2.
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