Indexé dans Registry

claude-usage-analyst

Prix non confirmé★ 1,375 Stars GitHubRegistre mis à jour · 3 sept. 2026agent-skill

Vue d’ensemble

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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Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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.

Voir la source ↗

Métadonnées du fichier

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.

Voir le texte original

---
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.

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: 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

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: daymade/claude-code-skills
Licence: MIT
Version: 1.0.0
Dernier push GitHub: 3 sept. 2026
Registre mis à jour: 3 sept. 2026
Chemin des instructions: daymade-claude-code/claude-usage-analyst/SKILL.md @ 3717e0fbbafa

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

75/100

Solide

Confiance

60/100

Sandbox uniquement

Audit

76/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

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

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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Créateur: daymade
Source: daymade/claude-code-skills
Indexé par: Index communautaire OpenAgentSkill

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Revendication du propriétaire

Revendiquer cette fiche de skill

Cette fiche Indexé par Registry est attribuée à daymade, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.

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Affichez la fiche canonique, les signaux actuels de confiance et d’audit, ainsi que de vraies preuves Agent-Proven là où les développeurs évaluent le dépôt.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/daymade-claude-usage-analyst?metric=listed&label=Listed)](https://www.openagentskill.com/skills/daymade-claude-usage-analyst?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/daymade-claude-usage-analyst?metric=trust&label=Trust)](https://www.openagentskill.com/skills/daymade-claude-usage-analyst?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/daymade-claude-usage-analyst?metric=audit&label=Audit)](https://www.openagentskill.com/skills/daymade-claude-usage-analyst/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/daymade-claude-usage-analyst?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/daymade-claude-usage-analyst?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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