claude-usage-analyst

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Author updated Aug 24, 2026, 03:33 PM
Author repo claude-code-skills

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Analyze Claude Code and Claude Desktop Code token usage, cost, quota burn, model mix, cache read/write, and 5-hour block consump…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Overview”, “Workflow”, “Evidence Rules”, “Output Shape”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name claude-usage-analyst directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Overview / Workflow / Evidence Rules” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Other
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @daymade · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: macOS · Linux · Windows
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview claude-usage-analyst.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# 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.

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Overview → Workflow → Evidence Rules → Output Shape
terms    -> Use this skill to produce evidence-based usage explanations from local ccusage data. · 4. Read references/explanation-guide.md when writing the final answer. · - Base numeric claims on ccusage output or the bundled analyzer output. · 1. Short conclusion in plain language. · Keep the answer readable for non-technical users.
files/cmd -> ccusage · npm install -g ccusage@latest · npx ccusage@latest · --since/--until · --since · references/explanation-guide.md · ccusage claude
body sha256 -> ff3d52b15202