claude-usage-analyst: Coding Workflows for AI Agents | OpenAgentSkill

Overview

Analyze Claude Code and Claude Desktop Code token usage, cost, quota burn, model mix, cache read/write, and 5-hour block consumption using ccusage evidence. Use when the user asks why Claude quota was exhausted, whether a model such as fable/opus/sonnet is unusually expensive, how many tokens were spent today or historically, or needs a human-friendly explanation of local Claude Code CLI/Desktop usage.

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Claude Usage Analyst

Overview

Use this skill to produce evidence-based usage explanations from local ccusage data. Separate observed numbers from interpretation, and explain quota burn in human terms.

Workflow

Verify ccusage is available:
```
ccusage --version
```
If missing, install or update with npm install -g ccusage@latest or run with npx ccusage@latest.
Run the bundled analyzer for the requested window:
```
python3 /path/to/claude-usage-analyst/scripts/analyze_claude_usage.py \
  --since YYYY-MM-DD --until YYYY-MM-DD --timezone Asia/Shanghai
```
Default --since/--until is today in the selected timezone. For historical comparison, set --since to an earlier date such as the first day of the month; otherwise rank/median fields only describe the single target day.

If the user asks about a specific model comparison, pass aliases:

python3 scripts/analyze_claude_usage.py --model-a fable --model-b opus-4-8

Read references/explanation-guide.md when writing the final answer.

Evidence Rules

Base numeric claims on ccusage output or the bundled analyzer output.
State the scope: ccusage claude measures local Claude Code usage logs, including Claude Desktop's Claude Code sessions when those local logs exist. It is not a complete ordinary Claude.ai chat bill.
Report dates with timezone.
Explain cache clearly: cache read tokens are still usage/quota pressure even though the user did not type those words.
Do not infer Anthropic plan quota rules from local token counts unless the user provides plan details. Say "quota-like pressure" or "ccusage estimated cost/token burn" when exact plan accounting is unknown.
When comparing models, compare both token volume and estimated cost. A model can have similar token volume but higher cost.

Output Shape

Use this structure unless the user asks otherwise:

Short conclusion in plain language.
Evidence table: total tokens, cost, input, output, cache create, cache read.
Model comparison table.
5-hour block table when quota exhaustion is discussed.
Explanation of why the burn happened.
Confidence and caveats.

Keep the answer readable for non-technical users. Avoid unexplained terms like "cache read" without a one-sentence translation.

View source ↗

File metadata

name: claude-usage-analyst
description: Analyze Claude Code and Claude Desktop Code token usage, cost, quota burn, model mix, cache read/write, and 5-hour block consumption using ccusage evidence. Use when the user asks why Claude quota was exhausted, whether a model such as fable/opus/sonnet is unusually expensive, how many tokens were spent today or historically, or needs a human-friendly explanation of local Claude Code CLI/Desktop usage.

View original text

---
name: claude-usage-analyst
description: Analyze Claude Code and Claude Desktop Code token usage, cost, quota burn, model mix, cache read/write, and 5-hour block consumption using ccusage evidence. Use when the user asks why Claude quota was exhausted, whether a model such as fable/opus/sonnet is unusually expensive, how many tokens were spent today or historically, or needs a human-friendly explanation of local Claude Code CLI/Desktop usage.
---

# Claude Usage Analyst

## Overview

Use this skill to produce evidence-based usage explanations from local `ccusage` data. Separate observed numbers from interpretation, and explain quota burn in human terms.

## Workflow

1. Verify `ccusage` is available:
```bash
ccusage --version
```
If missing, install or update with `npm install -g ccusage@latest` or run with `npx ccusage@latest`.

2. Run the bundled analyzer for the requested window:
```bash
python3 /path/to/claude-usage-analyst/scripts/analyze_claude_usage.py \
--since YYYY-MM-DD --until YYYY-MM-DD --timezone Asia/Shanghai
```
Default `--since/--until` is today in the selected timezone.
For historical comparison, set `--since` to an earlier date such as the first day of the month; otherwise rank/median fields only describe the single target day.

3. If the user asks about a specific model comparison, pass aliases:
```bash
python3 scripts/analyze_claude_usage.py --model-a fable --model-b opus-4-8
```

4. Read `references/explanation-guide.md` when writing the final answer.

## Evidence Rules

- Base numeric claims on `ccusage` output or the bundled analyzer output.
- State the scope: `ccusage claude` measures local Claude Code usage logs, including Claude Desktop's Claude Code sessions when those local logs exist. It is not a complete ordinary Claude.ai chat bill.
- Report dates with timezone.
- Explain cache clearly: cache read tokens are still usage/quota pressure even though the user did not type those words.
- Do not infer Anthropic plan quota rules from local token counts unless the user provides plan details. Say "quota-like pressure" or "ccusage estimated cost/token burn" when exact plan accounting is unknown.
- When comparing models, compare both token volume and estimated cost. A model can have similar token volume but higher cost.

## Output Shape

Use this structure unless the user asks otherwise:

1. Short conclusion in plain language.
2. Evidence table: total tokens, cost, input, output, cache create, cache read.
3. Model comparison table.
4. 5-hour block table when quota exhaustion is discussed.
5. Explanation of why the burn happened.
6. Confidence and caveats.

Keep the answer readable for non-technical users. Avoid unexplained terms like "cache read" without a one-sentence translation.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
SKILL.md uses a placeholder path `/path/to/claude-usage-analyst/scripts/analyze_claude_usage.py`, which will fail unless the agent resolves the actual skill directory.
No sample output or test fixture is included, making it harder to verify the analyzer behaves correctly across environments.
The skill instructs installing `ccusage` globally with npm, which requires appropriate user permissions; this is acceptable but should be called out as an environment-changing step.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Start with one small task

Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: daymade/claude-code-skills
License: MIT
Version: 1.0.0
Last GitHub push: Sep 3, 2026
Registry updated: Sep 3, 2026
Instruction path: daymade-claude-code/claude-usage-analyst/SKILL.md @ 3717e0fbbafa

Version reported in registry metadata; check source releases before relying on it.

Quality

78/100

Strong

Trust

61/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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}

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Creator: daymade
Source: daymade/claude-code-skills
Indexed by: OpenAgentSkill community index

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Overview

Claude Usage Analyst

Overview

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Output Shape

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