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claude-usage
Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagen
概览
Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.
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Claude Usage
Ground-truth token usage and cost reporting for Claude Code sessions. Parses JSONL files directly—parent sessions and subagent sidechains—to produce accurate numbers.
Why This Exists
ccusage (v18+, the standard community tool with 10.6k stars) undercounts output tokens by 77-94% for heavy users:
| Source | Output Tokens | Notes |
|---|---|---|
| Raw JSONL (all files) | 1,076,691 | Ground truth |
| Raw JSONL (date-filtered) | 412,987 | Properly filtered |
| ccusage --timezone UTC | 93,960 | 77% undercount |
| ccusage default | 62,375 | 94% undercount |
Three compounding bugs:
- Ignores subagent files at
{session-uuid}/subagents/{agent-id}.jsonl—132 files missed on a typical day with Agent Teams - Timezone confusion between UTC entry timestamps and local time filtering
- Drops entries in parent files—even at UTC, reports only 23% of actual tokens
This script reads every JSONL file to produce correct totals.
Usage
# Today's usage (default)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py
# Last 7 days, broken down by day
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 7d
# Weekly breakdown for the past month
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by week --since 30d
# Per-model breakdown
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by model --since 7d
# Per-session with human-readable summaries
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by session --since 1d
# Per-project breakdown
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by project --since 7d
# Filter to one project
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --project Thera --since 30d
# Custom date range
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 2026-02-01 --until 2026-02-28
# JSON output (pipe to jq)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 7d --json | jq '.grand_total'
# CSV output
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 7d --csv
# Compare against ccusage (shows delta)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --compare
# PDF report (dark editorial, CTO-ready)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage_report.py --since 30d
# PDF with custom output path
python3 ~/.claude/skills/claude-usage/scripts/claude_usage_report.py --since 30d -o ~/Desktop/feb-usage.pdf
# HTML only (no Playwright needed)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage_report.py --since 7d --html
Output Columns
| Column | Description |
|---|---|
| Input | Prompt tokens (charged at input rate) |
| Output | Response tokens (most expensive category) |
| CacheW | Cache write tokens (cache_creation_input_tokens) |
| CacheR | Cache read tokens (significantly cheaper than input) |
| Total | Sum of all four token types |
| Cost | Estimated USD from the hardcoded pricing table |
Aggregation Modes (--by)
| Mode | Groups by | Example key |
|---|---|---|
day (default) | Calendar date in local timezone | 2026-02-20 |
week | ISO week number | 2026-W08 |
month | Month | 2026-02 |
session | Session UUID (resolved to summary from sessions-index.json) | 8a6c801a Working on Claudicle... |
model | Model name | claude-opus-4-6 |
project | Project directory (decoded to path) | ~/Desktop/Programming |
Pricing Table
Prices are hardcoded in the PRICING dict at the top of claude_usage.py. Update when Anthropic changes rates. Keys are model name substrings matched most-specific-first.
Current defaults (USD per million tokens, verified 2026-02-21):
| Model | Input | Output | Cache Write (1h) | Cache Read |
|---|---|---|---|---|
| Opus 4.5 / 4.6 | $5.00 | $25.00 | $10.00 | $0.50 |
| Opus 4.0 / 4.1 | $15.00 | $75.00 | $30.00 | $1.50 |
| Sonnet 4.x | $3.00 | $15.00 | $6.00 | $0.30 |
| Haiku 4.5 | $1.00 | $5.00 | $2.00 | $0.10 |
| Sonnet 3.7 / 3.5 | $3.00 | $15.00 | $6.00 | $0.30 |
| Haiku 3.5 | $0.80 | $4.00 | $1.60 | $0.08 |
| Opus 3 | $15.00 | $75.00 | $30.00 | $1.50 |
| Haiku 3 | $0.25 | $1.25 | $0.50 | $0.03 |
Cache write column shows the 1-hour rate (2x base input). Claude Code uses 1-hour prompt caching exclusively. The 5-minute rate (1.25x base input) is also supported when the JSONL cache_creation sub-object provides a TTL split.
Timezone Handling
JSONL timestamps are UTC. The script converts to the system's local timezone (or --tz America/New_York) before applying --since/--until filters. This prevents the midnight-boundary confusion that causes ccusage to drop entries when the UTC date differs from the local date.
Data Sources
JSONL files at ~/.claude/projects/{encoded-path}/:
- Parent sessions:
{session-uuid}.jsonl - Subagent sidechains:
{session-uuid}/subagents/{agent-id}.jsonl - Session metadata:
sessions-index.json(summaries for--by session)
The script uses streaming line-by-line reads (O(1) memory) to handle files exceeding 300MB.
PDF Reports
claude_usage_report.py generates a dark editorial PDF with Crimson Pro + JetBrains Mono typography, gold Minoan accents, bar charts, model distribution with color-coded stacked bars, token composition breakdown, and detailed tables. Designed for executive sharing.
Requires Playwright (uv pip install --system playwright && playwright install chromium). Uses Chromium for pixel-perfect CSS rendering—same engine as the Aldea Slide Deck skill. Falls back to HTML output with --html flag.
Caveats
- Estimated accuracy: ~95%. The remaining gap comes from data residency multipliers (1.1x for US-only inference, not tracked) and potential edge cases in streaming chunk boundaries.
- Streaming chunks are deduplicated by message ID using last-wins strategy—
output_tokensmonotonically increases across chunks, so only the final chunk per message ID has the correct value. Without dedup, costs were overcounted by 2-5x. - Cost is estimated from the pricing table. Anthropic billing may differ due to batch discounts or promotional pricing.
- Cache write pricing uses the 1-hour rate by default (Claude Code's caching mode). When JSONL provides
cache_creation.ephemeral_5m_input_tokens/ephemeral_1h_input_tokens, rates are split accordingly. - Synthetic entries (
model: "<synthetic>") and billing error entries (all-zero token counts) are automatically filtered. - Malformed JSON lines and duplicate streaming chunks are counted and reported in the output footer.
- Requires Python 3.9+ for
zoneinfomodule. - PDF reports require Playwright with Chromium (
playwright install chromium). Includes a Pricing Methodology section with full rate table, formula, and confidence badge.
文件元数据
name: claude-usage description: Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.
查看原始文本
---
name: claude-usage
description: Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.
---
# Claude Usage
Ground-truth token usage and cost reporting for Claude Code sessions. Parses JSONL files directly—parent sessions and subagent sidechains—to produce accurate numbers.
## Why This Exists
ccusage (v18+, the standard community tool with 10.6k stars) **undercounts output tokens by 77-94%** for heavy users:
| Source | Output Tokens | Notes |
|--------|-------------|-------|
| Raw JSONL (all files) | 1,076,691 | Ground truth |
| Raw JSONL (date-filtered) | 412,987 | Properly filtered |
| ccusage --timezone UTC | 93,960 | 77% undercount |
| ccusage default | 62,375 | 94% undercount |
Three compounding bugs:
1. **Ignores subagent files** at `{session-uuid}/subagents/{agent-id}.jsonl`—132 files missed on a typical day with Agent Teams
2. **Timezone confusion** between UTC entry timestamps and local time filtering
3. **Drops entries** in parent files—even at UTC, reports only 23% of actual tokens
This script reads every JSONL file to produce correct totals.
---
## Usage
```bash
# Today's usage (default)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py
# Last 7 days, broken down by day
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 7d
# Weekly breakdown for the past month
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by week --since 30d
# Per-model breakdown
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by model --since 7d
# Per-session with human-readable summaries
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by session --since 1d
# Per-project breakdown
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --by project --since 7d
# Filter to one project
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --project Thera --since 30d
# Custom date range
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 2026-02-01 --until 2026-02-28
# JSON output (pipe to jq)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 7d --json | jq '.grand_total'
# CSV output
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --since 7d --csv
# Compare against ccusage (shows delta)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage.py --compare
# PDF report (dark editorial, CTO-ready)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage_report.py --since 30d
# PDF with custom output path
python3 ~/.claude/skills/claude-usage/scripts/claude_usage_report.py --since 30d -o ~/Desktop/feb-usage.pdf
# HTML only (no Playwright needed)
python3 ~/.claude/skills/claude-usage/scripts/claude_usage_report.py --since 7d --html
```
---
## Output Columns
| Column | Description |
|--------|-------------|
| Input | Prompt tokens (charged at input rate) |
| Output | Response tokens (most expensive category) |
| CacheW | Cache write tokens (`cache_creation_input_tokens`) |
| CacheR | Cache read tokens (significantly cheaper than input) |
| Total | Sum of all four token types |
| Cost | Estimated USD from the hardcoded pricing table |
---
## Aggregation Modes (`--by`)
| Mode | Groups by | Example key |
|------|-----------|-------------|
| `day` (default) | Calendar date in local timezone | `2026-02-20` |
| `week` | ISO week number | `2026-W08` |
| `month` | Month | `2026-02` |
| `session` | Session UUID (resolved to summary from sessions-index.json) | `8a6c801a Working on Claudicle...` |
| `model` | Model name | `claude-opus-4-6` |
| `project` | Project directory (decoded to path) | `~/Desktop/Programming` |
---
## Pricing Table
Prices are hardcoded in the `PRICING` dict at the top of `claude_usage.py`. Update when Anthropic changes rates. Keys are model name substrings matched most-specific-first.
Current defaults (USD per million tokens, verified 2026-02-21):
| Model | Input | Output | Cache Write (1h) | Cache Read |
|-------|-------|--------|-------------------|------------|
| Opus 4.5 / 4.6 | $5.00 | $25.00 | $10.00 | $0.50 |
| Opus 4.0 / 4.1 | $15.00 | $75.00 | $30.00 | $1.50 |
| Sonnet 4.x | $3.00 | $15.00 | $6.00 | $0.30 |
| Haiku 4.5 | $1.00 | $5.00 | $2.00 | $0.10 |
| Sonnet 3.7 / 3.5 | $3.00 | $15.00 | $6.00 | $0.30 |
| Haiku 3.5 | $0.80 | $4.00 | $1.60 | $0.08 |
| Opus 3 | $15.00 | $75.00 | $30.00 | $1.50 |
| Haiku 3 | $0.25 | $1.25 | $0.50 | $0.03 |
Cache write column shows the 1-hour rate (2x base input). Claude Code uses 1-hour prompt caching exclusively. The 5-minute rate (1.25x base input) is also supported when the JSONL `cache_creation` sub-object provides a TTL split.
---
## Timezone Handling
JSONL timestamps are UTC. The script converts to the system's local timezone (or `--tz America/New_York`) before applying `--since`/`--until` filters. This prevents the midnight-boundary confusion that causes ccusage to drop entries when the UTC date differs from the local date.
---
## Data Sources
JSONL files at `~/.claude/projects/{encoded-path}/`:
- **Parent sessions**: `{session-uuid}.jsonl`
- **Subagent sidechains**: `{session-uuid}/subagents/{agent-id}.jsonl`
- **Session metadata**: `sessions-index.json` (summaries for `--by session`)
The script uses streaming line-by-line reads (O(1) memory) to handle files exceeding 300MB.
---
## PDF Reports
`claude_usage_report.py` generates a dark editorial PDF with Crimson Pro + JetBrains Mono typography, gold Minoan accents, bar charts, model distribution with color-coded stacked bars, token composition breakdown, and detailed tables. Designed for executive sharing.
Requires Playwright (`uv pip install --system playwright && playwright install chromium`). Uses Chromium for pixel-perfect CSS rendering—same engine as the Aldea Slide Deck skill. Falls back to HTML output with `--html` flag.
---
## Caveats
- **Estimated accuracy: ~95%.** The remaining gap comes from data residency multipliers (1.1x for US-only inference, not tracked) and potential edge cases in streaming chunk boundaries.
- Streaming chunks are deduplicated by message ID using last-wins strategy—`output_tokens` monotonically increases across chunks, so only the final chunk per message ID has the correct value. Without dedup, costs were overcounted by 2-5x.
- Cost is estimated from the pricing table. Anthropic billing may differ due to batch discounts or promotional pricing.
- Cache write pricing uses the 1-hour rate by default (Claude Code's caching mode). When JSONL provides `cache_creation.ephemeral_5m_input_tokens` / `ephemeral_1h_input_tokens`, rates are split accordingly.
- Synthetic entries (`model: "<synthetic>"`) and billing error entries (all-zero token counts) are automatically filtered.
- Malformed JSON lines and duplicate streaming chunks are counted and reported in the output footer.
- Requires Python 3.9+ for `zoneinfo` module.
- PDF reports require Playwright with Chromium (`playwright install chromium`). Includes a Pricing Methodology section with full rate table, formula, and confidence badge.
查看并核实来源
获取价格与运行成本
- 获取 Skill
- 价格未确认
- 运行 Skill
- 尚未确认运行要求,请查看来源中的 Agent、API 和服务费用。
- 许可证
- MIT
- 价格未确认
- 我们尚未确认此 Skill 的价格,现有来源与安装入口仍可使用。
免费获取不代表免费运行,价格标签不代表安全评级。 提交价格信息 →
来源需要复核
已跟踪的来源发生变化或同步失败,请在安装前复核当前来源。
安装前审查: 避免自动安装
许可证: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- SKILL.md lacks an explicit 'Limitations' section, though it mentions hardcoded pricing and timezone handling.
- No installation/setup instructions are provided in SKILL.md; users must infer placement from the path in usage examples.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 41 GitHub stars
- Stars/forks activity: 41 stars, 4 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
工具列表来自元数据,并非已测试的兼容性;Agent 提示词是建议的交接方式。
从一个小任务开始
- 1阅读来源,确认输入、预期输出、依赖和权限。
- 2先让 Agent 提出计划,批准环境配置和费用,再进行隔离的小规模测试。
- 3检查输出和变更文件,只报告实际执行结果,并保留来源版本以便复现。
请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。
来源与使用须知
仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。
- 来源仓库
- tdimino/claude-code-minoan
- 许可证
- MIT
- 版本
- 1.0.0
- 最近 GitHub 推送
- 2026年9月6日
- 目录更新于
- 2026年9月10日
版本来自目录元数据,使用前请核实来源发布记录。
质量
60/100
有潜力
信任
53/100
Do not auto-install
审计
69/100
需审查
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- SKILL.md lacks an explicit 'Limitations' section, though it mentions hardcoded pricing and timezone handling.
- No installation/setup instructions are provided in SKILL.md; users must infer placement from the path in usage examples.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 41 GitHub stars
- Stars/forks activity: 41 stars, 4 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- 结果
- —
复制不等于安装。安装数需有成功安装回报,不代表全面的质量保证。
Agent 接入
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
更多详情
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"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"SKILL.md lacks an explicit 'Limitations' section, though it mentions hardcoded pricing and timezone handling.",
"No installation/setup instructions are provided in SKILL.md; users must infer placement from the path in usage examples.",
"Low GitHub adoption signal",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"GitHub adoption: 41 GitHub stars"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 60,
"label": "Promising"
},
"supply": {
"track": "Coding and developer agents",
"scenario": "Coding agents",
"maintenance": "1mo since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"Low GitHub adoption signal",
"SKILL.md lacks an explicit 'Limitations' section, though it mentions hardcoded pricing and timezone handling.",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"No installation/setup instructions are provided in SKILL.md; users must infer placement from the path in usage examples."
],
"agent_contract": {
"task_input": "Use claude-usage in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 61/100 Manual review",
"Audit: 69/100 Needs review",
"Safety: 25/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "tdimino-claude-usage (claude-usage)",
"install_command": "",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "tdimino-claude-usage",
"task": "Use claude-usage in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/tdimino-claude-usage",
"api": "https://www.openagentskill.com/api/agent/skills/tdimino-claude-usage",
"audit": "https://www.openagentskill.com/skills/tdimino-claude-usage/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=tdimino-claude-usage&task=Use%20claude-usage%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20claude-usage%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20claude-usage%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/tdimino-claude-usage/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/tdimino-claude-usage"
}
}创作者工具
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- 创作者
- tdimino
- 收录方
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[](https://www.openagentskill.com/skills/tdimino-claude-usage?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/tdimino-claude-usage/audit)
[](https://www.openagentskill.com/skills/tdimino-claude-usage?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)社区信号
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