cost-breakdown — Claude Code skill for cost analysis
This skill reports model and session costs, daily spending trends, and token efficiency. Use it to compare model pricing or plan a budget.
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a claude-codeWhat cost-breakdown does
The skill reads pricing, total costs, session costs, and daily statistics from Agent Monitor. Its Markdown report breaks spending down by model and session, lists the most expensive sessions, and reviews token efficiency. Currency is shown in USD to four decimal places.
Costs include input and output tokens, plus cache reads and writes. The report also examines baseline tokens preserved across compaction; without those baselines, earlier tokens could be lost when a transcript is rewritten. The report relies on Agent Monitor API data, so its figures depend on that data.
What it helps with
- compare model costs by token type
- identify the most expensive sessions
- track daily spending trends
- review cache reads and tokens recovered from baselines
- spot sessions with costly models or frequent compaction
Example prompts for the agent
- Break down this week's costs and compare models.
- Show the most expensive sessions from the last 30 days.
- Check cache reads and tokens preserved through compaction.
How to install cost-breakdown
1Skills CLI (Vercel)
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a claude-code2Manually: put the files in place
~/.claude/skills/cost-breakdown/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/cost-breakdown/
1Skills CLI (Vercel)
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a codex2Manually: put the files in place
.agents/skills/cost-breakdown/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a cursor2Manually: put the files in place
.cursor/skills/cost-breakdown/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install hoangsonww/Claude-Code-Agent-Monitor cost-breakdown2Manually: put the files in place
.github/skills/cost-breakdown/SKILL.md1Skills CLI (Vercel)
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a gemini-cli2Platform CLI
gemini skills install https://github.com/hoangsonww/Claude-Code-Agent-Monitor --path plugins/ccam-analytics/skills/cost-breakdownManually: put the files in place
.devin/skills/cost-breakdown/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about cost-breakdown
How do I install cost-breakdown in Claude Code?
Run npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cost-breakdown -a claude-code. The skill file is at ~/.claude/skills/cost-breakdown/SKILL.md.
What does cost-breakdown do?
It creates a report of spending by model and session, daily trends, and token usage. It also covers baseline token recovery after compaction.
How does cost-breakdown calculate token costs?
It applies separate rates for input, output, cache-read, and cache-write tokens. Effective totals add current counts to baseline counts preserved across compaction.
Source text
SKILL.md · MIT
---
name: cost-breakdown
description: >
Break down Claude Code costs using the Agent Monitor pricing engine.
Shows per-model costs (input, output, cache_read, cache_write at $/Mtok rates),
per-session costs, daily trends, and compaction baseline token recovery.
Use when analyzing spending, comparing model costs, or planning budgets.
---
# Cost Breakdown
Detailed cost analysis from the Agent Monitor's pricing engine.
## Input
The user provides: **$ARGUMENTS**
This may be: "today", "this week", "last 30 days", a session ID, or "budget $50/week".
## Data Sources
| Endpoint | Returns |
|----------|---------|
| `GET /api/pricing` | `{ pricing: [{ model_pattern, display_name, input_per_mtok, output_per_mtok, cache_read_per_mtok, cache_write_per_mtok }] }` |
| `GET /api/pricing/cost` | Total cost: `{ total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] }` |
| `GET /api/pricing/cost/{sessionId}` | Per-session cost with same breakdown shape |
| `GET /api/sessions?limit=200` | Sessions list — each includes inline `cost` field (bulk pricing) |
| `GET /api/analytics` | Token totals (total_input, total_output, total_cache_read, total_cache_write — baselines pre-summed), daily trends |
### How costs are calculated
The pricing engine matches model names against `model_pattern` using SQL LIKE (e.g. `claude-sonnet-4-5%` matches `claude-sonnet-4-5-20250514`). **Longest pattern wins** for specificity. Cost per model:
```
cost = (input_tokens / 1M) × input_per_mtok
+ (output_tokens / 1M) × output_per_mtok
+ (cache_read_tokens / 1M) × cache_read_per_mtok
+ (cache_write_tokens / 1M) × cache_write_per_mtok
```
Token counts are **effective totals** = `current + baseline` (baselines preserve pre-compaction tokens that would otherwise be lost when the transcript JSONL is rewritten).
### Default pricing tiers (seeded on first run)
| Family | Input $/Mtok | Output $/Mtok | Cache Read $/Mtok | Cache Write $/Mtok |
|--------|-------------|--------------|-------------------|-------------------|
| Opus 4.5/4.6 | $5 | $25 | $0.50 | $6.25 |
| Sonnet 4/4.5/4.6 | $3 | $15 | $0.30 | $3.75 |
| Haiku 4.5 | $1 | $5 | $0.10 | $1.25 |
## Report Sections
### 1. Cost by Model
Table from `/api/pricing/cost` breakdown — each model with 4 token counts + cost. Highlight which pricing rule matched.
### 2. Cost by Session (Top 10 Most Expensive)
From sessions list with inline `cost` — sort descending. Show session name, model, duration, cost.
### 3. Daily Cost Trend
Cross-reference `daily_sessions` with per-session costs to compute daily spend. Show 7/30-day trend with direction arrows.
### 4. Token Efficiency Analysis
- **Cache hit rate**: `total_cache_read / (total_cache_read + total_input) × 100` — higher = more efficient
- **Compaction baseline recovery**: Tokens preserved via baseline columns (tokens not lost to compaction)
- **Output/input ratio**: Balanced ratio indicates good prompt efficiency
### 5. Cost Optimization Opportunities
- Sessions where cache_write >> cache_read (poor cache reuse)
- Expensive models used for simple tasks (check subagent_type vs model)
- Sessions with many compactions (context overflow = wasted tokens)
## Output
Structured Markdown with tables. Currency as USD to 4 decimal places. Include total and per-model subtotals.
© hoangsonww · MIT · hoangsonww/Claude-Code-Agent-Monitor