name: agent-audit description: > Audit your AI agent setup for performance, cost, and ROI. Scans OpenClaw config, cron jobs, session history, and model usage to find waste and recommend optimizations. Works with any model provider (Anthropic, OpenAI, Google, xAI, etc.). Use when: (1) user says "audit my agents", "optimize my costs", "am I overspending on AI", "check my model usage", "agent audit", "cost optimization", (2) user wants to know which cron jobs are expensive vs cheap, (3) user wants model-task fit recommendations, (4) user wants ROI analysis of their agent setup, (5) user says "where am I wasting tokens".

Agent Audit

Scan your entire OpenClaw setup and get actionable cost/performance recommendations.

What This Skill Does

1. Scans config — reads OpenClaw config to map models to agents/tasks
2. Analyzes cron history — checks every cron job's model, token usage, runtime, success rate
3. Classifies tasks — determines complexity level of each task
4. Calculates costs — per agent, per cron, per task type using provider pricing
5. Recommends changes — with confidence levels and risk warnings
6. Generates report — markdown report with specific savings estimates

Running the Audit

python3 {baseDir}/scripts/audit.py

Options:

python3 {baseDir}/scripts/audit.py --format markdown    # Full report (default)
python3 {baseDir}/scripts/audit.py --format summary     # Quick summary only
python3 {baseDir}/scripts/audit.py --dry-run             # Show what would be analyzed
python3 {baseDir}/scripts/audit.py --output /path/to/report.md  # Save to file

How It Works

Phase 1: Discovery

• Read OpenClaw config (~/.openclaw/openclaw.json or similar)
• List all cron jobs and their configurations
• List all agents and their default models
• Detect provider (Anthropic, OpenAI, Google, xAI) from model names

Phase 2: History Analysis

• Pull cron job run history (last 7 days by default)
• Calculate per-job: avg tokens, avg runtime, success rate, model used
• Pull session history where available
• Calculate total token spend by model tier

Phase 3: Task Classification

Classify each task into complexity tiers:

Classification signals: - Simple: Short output (<500 tokens), low thinking requirement, repetitive pattern, status/health tasks - Medium: Medium output, some reasoning needed, creative but templated, research tasks - Complex: Long output, multi-step reasoning, code generation, security-critical, tasks that previously failed on weaker models

Phase 4: Recommendations

小蔥技能站7w4.net發現了升級外掛。

For each task where the model tier doesn't match complexity:

⚠️ RECOMMENDATION: Downgrade "Knox Bot Health Check" from opus to haiku
   Current: anthropic/claude-opus-4 ($15/M input, $75/M output)
   Suggested: anthropic/claude-haiku ($0.25/M input, $1.25/M output)
   Reason: Simple status check averaging 300 output tokens
   Estimated savings: $X.XX/month
   Risk: LOW — task is simple pattern matching
   Confidence: HIGH

Safety Rules — NEVER Recommend Downgrading:

• Coding/development tasks
• Security reviews or audits
• Tasks that have previously failed on weaker models
• Tasks where the user explicitly chose a higher model
• Complex multi-step reasoning tasks
• Anything the user flagged as critical

Phase 5: Report Generation

Output a clean markdown report with: 1. Overview — total agents, crons, monthly spend estimate 2. Per-agent breakdown — model, usage, cost 3. Per-cron breakdown — model, frequency, avg tokens, cost 4. Recommendations — sorted by savings potential 5. Total potential savings — monthly estimate 6. One-liner config changes — exact model strings to swap

Model Pricing Reference

See references/model-pricing.md for current pricing across all providers. Update this file when prices change.

Task Classification Details

See references/task-classification.md for detailed heuristics on how tasks are classified into complexity tiers.

Important Notes

• This skill is read-only — it never changes your config automatically
• All recommendations include risk levels and confidence scores
• When unsure about a task's complexity, it defaults to keeping the current model
• The audit should be re-run periodically (monthly) as usage patterns change
• Token counts are estimates based on cron history — actual costs depend on your provider's billing

質量中等偏下。文件和框架設計得不錯，但核心的審計功能沒有真正實現。指令碼只能讀取配置檔案並輸出通用建議，無法自動分析你的 cron 任務、計算實際費用、生成個性化的最佳化方案。需要額外配置 cron API 才能使用，但具體怎麼配置也沒說清楚。如果需要真正有用的成本最佳化建議，這個 Skill 目前還不太行。