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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
Ringkasan
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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Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.
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.
Metadata berkas
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.
Lihat teks asli
---
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.
Tinjau sumber
Harga dan biaya penggunaan
- Dapatkan skill
- Harga belum dikonfirmasi
- Jalankan
- Persyaratan belum dikonfirmasi. Periksa biaya agen, API, dan layanan di sumbernya.
- Lisensi
- MIT
- Harga belum dikonfirmasi
- Harga belum dikonfirmasi. Tautan sumber dan instalasi yang ada tetap tersedia.
Gratis diperoleh bukan berarti gratis dijalankan. Harga bukan penilaian keamanan. Kirim informasi harga →
Sumber perlu ditinjau
Sumber berubah atau gagal disinkronkan. Tinjau sumber terbaru sebelum memasang.
Tinjau sebelum memasang: Hindari pemasangan otomatis
Lisensi: 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
Daftar alat adalah petunjuk metadata, bukan kompatibilitas teruji. Prompt adalah saran.
Mulai dengan tugas kecil
- 1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
- 2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
- 3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.
Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.
Sumber dan catatan penggunaan
Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.
- Repositori sumber
- tdimino/claude-code-minoan
- Lisensi
- MIT
- Versi
- 1.0.0
- Push GitHub terakhir
- 6 Sep 2026
- Direktori diperbarui
- 10 Sep 2026
- Jalur instruksi
- skills/core-development/claude-usage/SKILL.md @ 72da3657c7d7
Versi dilaporkan dalam metadata direktori; periksa rilis sumber.
Kualitas
60/100
Menjanjikan
Kepercayaan
53/100
Do not auto-install
Audit
69/100
Perlu ditinjau
- 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
- —
- Hasil
- —
Menyalin bukan memasang. Jumlah instalasi memerlukan laporan berhasil dan bukan jaminan kualitas menyeluruh.
Akses agent
API Registry menyediakan sinyal keputusan, kepercayaan, audit, use case, dan pemasangan tanpa mengikis UI.
Detail lainnya
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"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"
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"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"
}
}Untuk kreator
Sumber listing
Diindeks Registry
Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.
- Kreator
- tdimino
- Diindeks oleh
- Indeks komunitas OpenAgentSkill
Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.
Klaim skill iniKlaim pemilik
Klaim listing skill ini
Listing Diindeks Registry ini dikaitkan dengan tdimino, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.
Kit berbagi
Kit backlink kreator
Tambahkan badge bukti ke README Anda
Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.
[](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?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)Sinyal komunitas
Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.
