# Cost Optimizer

> Ultimate cost optimization toolkit for OpenClaw/Claude Code. Smart model routing, context compression, heartbeat tuning, usage reports, config generation — save 60-80% on daily token costs. (中文) 智能模型路由、上下文压缩、Heartbeat 优化、消耗报告、配置生成，日均节省 60-80%。

- Skill: `fullstackcrew-alpha/cost-optimizer` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add fullstackcrew-alpha/cost-optimizer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fullstackcrew-alpha/cost-optimizer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: fullstackcrew-alpha (https://skillmd.com/u/fullstackcrew-alpha)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fullstackcrew-alpha/cost-optimizer

---


# Cost Optimizer — OpenClaw/Claude Code 成本优化终极工具包

> 核心问题：OpenClaw 默认配置下 token 消耗极高（日均 $15+，heartbeat 月均 $50-100）。
> 本 skill 通过智能路由、上下文压缩、heartbeat 优化三管齐下，将成本降低 60-80%。

## 参数解析

解析用户输入的第一个参数，路由到对应子命令：

| 参数 | 子命令 | 说明 |
|------|--------|------|
| `route` | `/cost-route` | Smart model routing |
| `compress` | `/cost-compress` | Context compression |
| `heartbeat` | `/cost-heartbeat` | Heartbeat optimization |
| `report` | `/cost-report` | Usage report |
| `config` | `/cost-config` | Config generation |
| `slim` | `/cost-slim` | Audit & slim down SOUL.md, AGENTS.md, MEMORY.md |
| `screenshot` | `/cost-screenshot` | Optimize screenshot resolution for vision tasks |
| `loops` | `/cost-loops` | Optimize agent loop settings to reduce overhead |
| (no argument) | `/cost-report` | Default: show usage report |

---

## 1. `/cost-route` — 智能模型路由

### 任务分类引擎

对用户的 prompt 进行多维度分析，确定最佳模型：

**Step 0: 加载定价与规则数据**

```
Read references/model-pricing.md — 获取最新模型定价，用于成本计算和节省估算
Read references/routing-rules.md — 获取路由规则详情和自定义方法
```

> 如果 references 文件缺失或损坏，使用 index.ts 中硬编码的 MODEL_PRICING 常量作为 fallback。

**Step 1: 提取分类特征**

从当前 prompt 中提取以下特征：

```
特征维度：
- keywords: 关键词匹配（见下方路由规则表）
- context_length: 当前对话上下文 token 数（用 index.ts#estimateTokens 估算）
- complexity_score: 复杂度评分（0-10）
  - 0-2: 简单查询、状态检查
  - 3-5: 单文件操作、格式化、补全
  - 6-8: 多文件代码生成、调试、推理
  - 9-10: 架构设计、多步骤复杂任务
- tool_calls: 预期工具调用数量
- code_ratio: prompt 中代码占比
```

**Step 2: 路由决策**

根据特征匹配路由规则（优先级从高到低）：

| 优先级 | 条件 | 目标模型 | 预估成本/1M tokens |
|--------|------|----------|-------------------|
| P0 | heartbeat / cron / 状态检查 / ping | `deepseek/v3` 或 `gemini-2.0-flash` | $0.07-0.10 |
| P1 | 简单查询（complexity 0-2）、文件列表、搜索 | `gemini-2.0-flash` | $0.10 |
| P2 | 文件读取、代码补全、格式化、lint 修复 | `claude-haiku-4-5` | $0.80 |
| P3 | 代码生成、单文件调试、测试编写 | `claude-sonnet-4-6` | $3.00 |
| P4 | 多文件重构、架构设计、复杂推理 | `claude-opus-4-6` | $15.00 |

**关键词匹配表：**

```
P0 (最便宜):
  - heartbeat, ping, status, health, alive, cron, schedule
  - "是否在线", "检查状态", "心跳"

P1 (低成本):
  - list, find, search, grep, count, ls, pwd, which, where
  - "找到", "搜索", "列出", "有几个"

P2 (中低成本):
  - read, cat, format, lint, fix typo, rename, move
  - complete, autocomplete, suggest, snippet
  - "读取", "格式化", "补全", "重命名"

P3 (中等成本):
  - write, create, implement, generate, test, debug, explain
  - refactor (单文件), fix bug, add feature
  - "写", "创建", "实现", "生成", "测试", "调试"

P4 (高成本 - 仅在必要时):
  - architect, design, plan, review (全局), migrate
  - refactor (多文件), "从零开始", "重新设计"
  - complexity_score >= 9
  - context_length > 100k tokens
```

**Step 3: 降级策略**

```
降级链: opus → sonnet → haiku → gemini-flash → deepseek/v3
触发条件:
  - 目标模型 API 返回 429/503 → 降一级
  - 响应时间 > 30s → 降一级
  - 用户设置了成本上限且当前会话已超 80% → 强制降一级
  - 降级后在日志中记录: [COST-ROUTE] 降级: {原模型} → {新模型}, 原因: {reason}
```

**Step 4: 输出路由建议**

```markdown
## 🔀 模型路由建议

| 维度 | 值 |
|------|-----|
| 任务分类 | {category} |
| 复杂度评分 | {score}/10 |
| 上下文长度 | {tokens} tokens |
| 推荐模型 | {model} |
| 预估成本 | ${cost}/次 |
| 对比默认 | 节省 {savings}% |

> 路由依据: {匹配的关键词/规则}
```

**用户自定义覆盖：**

用户可在 `openclaw.json` 中添加自定义路由规则：

```json
{
  "cost-optimizer": {
    "routing": {
      "overrides": [
        {
          "pattern": "deploy|发布",
          "model": "claude-sonnet-4-6",
          "reason": "部署操作需要中等智能但不需要最强模型"
        }
      ]
    }
  }
}
```

---

## 2. `/cost-compress` — 上下文压缩

### 触发条件

- **自动触发**：当对话上下文估算超过 50k tokens 时，输出压缩建议
- **手动触发**：用户执行 `/cost-optimizer compress`

### 压缩策略

**Step 1: 分析当前上下文**

扫描对话历史，按以下类别统计 token 占比：

```
类别           | 描述                    | 压缩率
-------------- | ---------------------- | ------
recent_turns   | 最近 5 轮对话           | 0%（完整保留）
old_turns      | 更早的对话轮次          | 80-90%
tool_results   | 工具调用返回结果        | 70-85%
file_contents  | 文件完整内容            | 90-95%
code_blocks    | 代码块                  | 50-70%
system_context | 系统 prompt / 角色定义   | 0%（不压缩）
```

**Step 2: 执行压缩**

对每种类别应用不同的压缩策略：

1. **历史对话压缩**：
   - 保留最近 5 轮完整内容
   - 第 6-10 轮：提取关键决策点和结论
   - 第 10 轮以前：仅保留一句话摘要
   - 格式：`[轮次 N 摘要] 用户请求 X，助手执行了 Y，结果是 Z`

2. **工具调用结果压缩**：
   - 成功的文件读取 → 替换为 `[已读取 {path}, {lines} 行, 关键内容: {summary}]`
   - 成功的搜索结果 → 替换为 `[搜索 "{query}": 找到 {n} 个匹配, 主要在 {files}]`
   - 失败的工具调用 → 保留错误信息，移除重试的中间结果
   - Bash 输出 → 保留退出码和关键输出行（首尾各 5 行）

3. **文件内容压缩**：
   - 替换为路径+行号摘要：`[文件 {path}: {lines} 行, 函数: {func_list}, 关键逻辑在 L{start}-L{end}]`
   - 如果文件在后续被修改，只保留最终版本的摘要

4. **代码块压缩**：
   - 保留函数签名和关键逻辑
   - 移除注释和空行
   - 对未被后续引用的代码块，替换为 `[代码块: {language}, {lines} 行, 功能: {summary}]`

**Step 3: 生成压缩快照**

将压缩后的上下文摘要写入 `.context-snapshot.md`：

```markdown
# Context Snapshot
> 生成时间: {timestamp}
> 原始 tokens: {original} → 压缩后: {compressed} (节省 {ratio}%)

## 关键决策
- {decision_1}
- {decision_2}

## 活跃文件
- {file_1}: {summary}
- {file_2}: {summary}

## 待处理事项
- {todo_1}
- {todo_2}

## 最近对话（完整）
{last_5_turns}
```

**Step 4: 输出压缩报告**

```markdown
## 📦 上下文压缩报告

| 指标 | 值 |
|------|-----|
| 压缩前 | {original_tokens} tokens (~${original_cost}) |
| 压缩后 | {compressed_tokens} tokens (~${compressed_cost}) |
| 节省 | {saved_tokens} tokens (~${saved_cost}, {ratio}%) |
| 保留轮次 | 最近 {n} 轮完整保留 |
| 快照位置 | .context-snapshot.md |

> 建议：{下一步建议，如"开启新对话并加载快照"}
```

---

## 3. `/cost-heartbeat` — Heartbeat 优化

### 当前问题分析

OpenClaw 默认 heartbeat 配置：
- 间隔：15-30 分钟（过于频繁）
- 内容：全量状态检查（过于冗余）
- 模型：使用默认模型（过于昂贵）
- 月成本估算：$50-100（仅 heartbeat）

### 优化方案

**Step 1: 检查当前 heartbeat 配置**

读取以下位置的配置：
```bash
# OpenClaw 配置
cat ~/.openclaw/config.json 2>/dev/null
cat ./openclaw.json 2>/dev/null

# Claude Code 配置
cat ~/.claude/settings.json 2>/dev/null
cat ./.claude/settings.json 2>/dev/null
```

**Step 2: 生成优化配置**

```json
{
  "heartbeat": {
    "enabled": true,
    "base_interval_minutes": 45,
    "smart_adjustment": {
      "enabled": true,
      "rules": [
        {
          "condition": "active_development",
          "description": "检测到频繁文件变更（5分钟内 > 3次）",
          "interval_minutes": 30,
          "check": "find . -name '*.ts' -o -name '*.js' -o -name '*.py' -newer /tmp/.last-heartbeat 2>/dev/null | wc -l"
        },
        {
          "condition": "idle",
          "description": "无文件变更超过 30 分钟",
          "interval_minutes": 60
        },
        {
          "condition": "night_hours",
          "description": "本地时间 23:00-07:00",
          "interval_minutes": 120,
          "alternative": "disable"
        }
      ]
    },
    "content": {
      "mode": "minimal",
      "checks": [
        "process_alive",
        "disk_space_critical",
        "active_tasks_count"
      ],
      "skip": [
        "full_status_report",
        "dependency_check",
        "code_analysis",
        "git_log_summary"
      ]
    },
    "model": "deepseek/v3",
    "fallback_model": "gemini-2.0-flash",
    "max_tokens_per_heartbeat": 200,
    "cost_cap_monthly_usd": 5.00
  }
}
```

**Step 3: 估算节省**

```markdown
## 💓 Heartbeat 优化报告

### 当前配置 vs 优化配置

| 指标 | 当前 | 优化后 | 节省 |
|------|------|--------|------|
| 平均间隔 | {current}min | {optimized}min | — |
| 日均次数 | {current_daily} | {optimized_daily} | {reduction}% |
| 每次 tokens | ~{current_tokens} | ~{optimized_tokens} | {token_reduction}% |
| 使用模型 | {current_model} | deepseek/v3 | — |
| 每次成本 | ${current_cost} | ${optimized_cost} | {cost_reduction}% |
| **月均成本** | **${current_monthly}** | **${optimized_monthly}** | **${monthly_savings}** |

### 推荐配置

已写入 `openclaw.json` 的 heartbeat 部分。

### 智能间隔说明
- 🟢 活跃开发期（频繁文件变更）→ 30 分钟
- 🟡 普通工作期 → 45 分钟（默认）
- 🔴 静默期（无变更 > 30min）→ 60 分钟
- 🌙 夜间（23:00-07:00）→ 120 分钟或禁用
```

**Step 4: 应用配置**

如果用户确认，将优化配置合并到 `openclaw.json` 和/或 `.claude/settings.json`。

---

## 4. `/cost-report` — 消耗报告

### 数据收集

**Step 0: 加载定价数据**

```
Read references/model-pricing.md — 获取各模型单价，用于费用计算和节省估算
```

> 如果 references 文件缺失，使用 index.ts 中的 MODEL_PRICING 常量。

**Step 1: 读取使用日志**

```bash
# OpenClaw 使用日志
USAGE_LOG="$HOME/.openclaw/usage-log.jsonl"

# 如果日志不存在，基于当前会话估算
```

日志格式（每行一个 JSON）：
```json
{
  "timestamp": "2026-03-20T10:30:00Z",
  "session_id": "abc123",
  "model": "claude-sonnet-4-6",
  "input_tokens": 15000,
  "output_tokens": 3000,
  "task_type": "code_generation",
  "cost_usd": 0.054,
  "routed": false,
  "original_model": null
}
```

**Step 2: 计算统计数据**

使用 `index.ts` 中的工具函数计算：

- 本次会话消耗（基于对话长度估算）
- 本日/本周/本月累计（基于日志）
- 按模型分类的消耗分布
- 按任务类型的消耗分布
- 如果启用了路由，计算实际节省

**Step 3: 输出报告**

```markdown
## 📊 成本消耗报告

### 本次会话
| 指标 | 值 |
|------|-----|
| 总 tokens | {total_tokens} (输入: {input}, 输出: {output}) |
| 使用模型 | {models_used} |
| 估算成本 | ${session_cost} |
| 会话时长 | {duration} |

### 本周累计 ({week_start} - {week_end})
| 模型 | Tokens | 成本 | 占比 |
|------|--------|------|------|
| claude-opus-4-6 | {tokens} | ${cost} | {pct}% |
| claude-sonnet-4-6 | {tokens} | ${cost} | {pct}% |
| claude-haiku-4-5 | {tokens} | ${cost} | {pct}% |
| deepseek/v3 | {tokens} | ${cost} | {pct}% |
| **合计** | **{total}** | **${total_cost}** | **100%** |

### 按任务类型
| 类型 | 次数 | 平均 Tokens | 总成本 | 占比 |
|------|------|-------------|--------|------|
| 代码生成 | {n} | {avg} | ${cost} | {pct}% |
| 调试修复 | {n} | {avg} | ${cost} | {pct}% |
| 文件操作 | {n} | {avg} | ${cost} | {pct}% |
| heartbeat | {n} | {avg} | ${cost} | {pct}% |
| 其他 | {n} | {avg} | ${cost} | {pct}% |

### 趋势（最近 7 天）
{ascii_bar_chart}

### 💡 节省建议
{savings_recommendations}

> 如果本周全部使用智能路由，预计可节省 **${potential_savings}**（{savings_pct}%）
```

**ASCII 柱状图格式：**
```
日期       | 成本     | 分布
03-14 Mon  | $12.30  | ████████████░░░░░░░░
03-15 Tue  | $8.50   | ████████░░░░░░░░░░░░
03-16 Wed  | $15.20  | ███████████████░░░░░
03-17 Thu  | $6.30   | ██████░░░░░░░░░░░░░░
03-18 Fri  | $11.00  | ███████████░░░░░░░░░
03-19 Sat  | $3.20   | ███░░░░░░░░░░░░░░░░░
03-20 Sun  | $1.50   | █░░░░░░░░░░░░░░░░░░░
           +---------+--------------------
             总计: $58.00  日均: $8.29
```

---

## 5. `/cost-config` — 配置生成

### 预设方案

提供三档预设，用户可选择：

| 预设 | 说明 | 预估日均成本 | 适用场景 |
|------|------|-------------|---------|
| `conservative` | 保守优化，最小化风险 | $8-12 | 不确定时的安全选择 |
| `balanced` | 平衡成本与质量（推荐） | $4-8 | 日常开发 |
| `aggressive` | 激进节省，可能影响质量 | $1-4 | 预算紧张、简单任务为主 |

**Step 1: 分析当前配置**

读取现有配置文件：
```bash
cat ./openclaw.json 2>/dev/null || echo "{}"
cat ./.claude/settings.json 2>/dev/null || echo "{}"
```

**Step 2: 生成配置**

根据选择的预设生成完整的 `openclaw.json`（参考本 skill 目录下的 `openclaw.json` 模板）。

**Step 3: 输出配置 diff**

```markdown
## ⚙️ 配置生成 — {preset} 方案

### 变更预览

\`\`\`diff
--- openclaw.json (当前)
+++ openclaw.json (优化后)
@@ model_routing @@
- "default_model": "claude-opus-4-6"
+ "default_model": "claude-sonnet-4-6"
+ "routing_rules": [...]

@@ heartbeat @@
- "interval_minutes": 15
+ "interval_minutes": 45
+ "model": "deepseek/v3"

@@ context @@
+ "max_context_tokens": 80000
+ "auto_compress_threshold": 50000
\`\`\`

### 预估效果
| 指标 | 优化前 | 优化后 | 节省 |
|------|--------|--------|------|
| 日均成本 | ~${before}/天 | ~${after}/天 | ${savings}/天 |
| 月均成本 | ~${before_m}/月 | ~${after_m}/月 | ${savings_m}/月 |
| heartbeat 月成本 | ~${hb_before}/月 | ~${hb_after}/月 | ${hb_savings}/月 |

确认应用？输入 `yes` 应用，`no` 取消，或指定其他预设（conservative/balanced/aggressive）。
```

**Step 4: 应用配置**

用户确认后：
1. 备份当前配置：`cp openclaw.json openclaw.json.bak.{timestamp}`
2. 写入新配置
3. 验证配置格式正确（JSON 解析测试）
4. 输出确认信息

---

## 通用工具函数

本 skill 依赖 `index.ts` 中的工具函数，所有子命令共享：

- `classifyTask(prompt)` — 任务分类，返回 P0-P4 等级
- `estimateTokens(text)` — 基于字符数的 token 快速估算
- `routeModel(category, contextSize)` — 根据分类和上下文大小选择模型
- `compressContext(messages, budget)` — 上下文压缩
- `generateUsageReport(logPath)` — 从日志生成报告
- `calculateSavings(actual, optimized)` — 计算节省金额

详细类型定义和实现见 `index.ts`。

---

## 错误处理

所有文件操作均需显式 fallback，避免因单点故障中断整个流程：

| 操作 | 失败场景 | Fallback 行为 |
|------|---------|--------------|
| 读取 `openclaw.json` | 文件不存在 / JSON 解析失败 | 使用内置默认配置，输出 `⚠️ 配置文件读取失败，使用默认值` |
| 读取 `~/.openclaw/config.json` | 权限不足 / 路径不存在 | 跳过全局配置，仅使用项目级配置 |
| 读取 `references/*.md` | 文件缺失 | 使用 `index.ts` 中硬编码的 `MODEL_PRICING` 常量 |
| 写入日志 `~/.openclaw/cost-optimizer.log` | 目录不存在 / 磁盘满 | 回退到标准输出打印日志，输出 `⚠️ 日志写入失败，回退到 stdout` |
| 写入 `usage-log.jsonl` | 权限不足 | 跳过日志写入，输出 `⚠️ 使用日志写入失败，本次数据未记录` |
| 解析 `usage-log.jsonl` | 某行 JSON 格式损坏 | 跳过损坏行，继续解析后续行，报告中标注 `⚠️ 跳过 {n} 条损坏记录` |
| 写入 `openclaw.json`（配置应用） | 写入失败 | 不覆盖原文件，输出错误信息和配置内容到 stdout，让用户手动粘贴 |
| 备份配置文件 | 备份失败 | 中止配置写入，输出 `⚠️ 备份失败，已中止写入以保护现有配置` |

### 实现原则

1. **读取失败 → 降级到默认值**：永远不因读取失败而中断流程
2. **写入失败 → 回退到 stdout**：确保信息不丢失，用户可手动操作
3. **解析失败 → 跳过并报告**：损坏数据不影响其余有效数据的处理
4. **配置写入 → 备份优先**：备份失败则拒绝写入，保护用户现有配置

---

## 审计与日志

所有路由决策和配置变更都会记录日志：

```bash
# 日志位置
~/.openclaw/cost-optimizer.log

# 日志格式
[2026-03-20T10:30:00Z] [ROUTE] task=code_generation complexity=6 model=claude-sonnet-4-6 tokens=15000 cost=$0.054
[2026-03-20T10:35:00Z] [COMPRESS] before=80000 after=25000 ratio=68.75% snapshot=.context-snapshot.md
[2026-03-20T11:00:00Z] [HEARTBEAT] interval=45min model=deepseek/v3 tokens=150 cost=$0.00001
[2026-03-20T11:05:00Z] [CONFIG] preset=balanced changes=3 backup=openclaw.json.bak.1710924300
```

---

## 6. `/cost-slim` — Context File Audit & Optimization

The single biggest hidden cost in OpenClaw: **SOUL.md, AGENTS.md, and MEMORY.md are injected into EVERY message.** A 5,000-token SOUL.md × 100 messages/day = 500,000 wasted input tokens/day (~$7.50 on Opus).

### Step 1: Measure current context overhead

Scan and count tokens in all context files:

```bash
# OpenClaw context files
for f in SOUL.md AGENTS.md MEMORY.md USER.md HEARTBEAT.md; do
  if [ -f "$f" ]; then
    chars=$(wc -c < "$f")
    est_tokens=$((chars * 100 / 400))  # rough: 1 token ≈ 4 chars for English, 2 chars for Chinese
    echo "$f: $chars chars ≈ $est_tokens tokens"
  fi
done

# Claude Code context files
for f in CLAUDE.md; do
  if [ -f "$f" ]; then
    chars=$(wc -c < "$f")
    est_tokens=$((chars * 100 / 400))
    echo "$f: $chars chars ≈ $est_tokens tokens"
  fi
done
```

### Step 2: Apply token budgets

| File | Target Budget | Why |
|------|--------------|-----|
| SOUL.md | < 1,000 tokens (~2,000 chars EN / ~1,000 chars CN) | Loaded on EVERY message. Every token here is multiplied by total messages |
| AGENTS.md | < 800 tokens | Same reason — injected per message |
| MEMORY.md | < 3,000 tokens | Loaded per session. Don't dump project docs here |
| USER.md | < 500 tokens | Brief user context only |
| HEARTBEAT.md | < 300 tokens | Minimal heartbeat instructions |
| **Total overhead** | **< 5,600 tokens** | Target: keep combined context under 6K |

### Step 3: Slim down SOUL.md

Read the current SOUL.md and rewrite it following these rules:

**Rules for an efficient SOUL.md:**

1. **No redundant instructions** — If the model already does it by default, don't say it
   - ❌ "You are a helpful assistant" (it already is)
   - ❌ "Write clean code" (it already tries to)
   - ✅ "Reply in Chinese" (non-default behavior)

2. **No long lists when a short rule works**
   - ❌ "You are proficient in React, Vue, Angular, Svelte, Next.js, Nuxt, Remix, Astro, SolidJS..."
   - ✅ "Full-stack engineer. Adapt to the project's framework."

3. **No examples unless critical** — Examples bloat SOUL.md. Put them in reference files loaded on demand.

4. **Merge overlapping rules**
   - ❌ "Be concise." + "Don't be verbose." + "Keep responses short." (3 rules, same meaning)
   - ✅ "Be concise." (1 rule)

5. **Use bullet points, not paragraphs** — Bullets are shorter and models parse them better.

### Step 4: Slim down AGENTS.md

- Remove agent definitions you haven't used in the past week
- Reduce each agent description to 1 line (name + one-sentence capability)
- Move detailed agent prompts to separate files loaded on demand

### Step 5: Slim down MEMORY.md

- Remove entries older than 2 weeks unless explicitly marked as permanent
- Keep only index pointers (file paths), not inline content
- Target: each memory entry = 1 line in the index

### Step 6: Output optimization report

```markdown
## 📐 Context Slim Report

### Before
| File | Tokens | Status |
|------|--------|--------|
| SOUL.md | {before_tokens} | {over_budget ? "🔴 OVER" : "🟢 OK"} |
| AGENTS.md | {before_tokens} | ... |
| MEMORY.md | {before_tokens} | ... |
| **Total** | **{total_before}** | |

### After
| File | Tokens | Saved |
|------|--------|-------|
| SOUL.md | {after_tokens} | {saved} ({pct}%) |
| AGENTS.md | {after_tokens} | {saved} ({pct}%) |
| MEMORY.md | {after_tokens} | {saved} ({pct}%) |
| **Total** | **{total_after}** | **{total_saved} ({total_pct}%)** |

### Monthly savings estimate
- Messages/day: ~{n}
- Token reduction per message: {saved_per_msg}
- Monthly savings: **${monthly_savings}** (at {model} pricing)

### Optimized SOUL.md preview
{show the proposed slim version for user confirmation}
```

Confirm with user before applying changes. Back up originals as `SOUL.md.bak.{timestamp}`.

---

## 7. `/cost-screenshot` — Screenshot Resolution Optimization

When using browser automation (Playwright, Puppeteer, idb, ADB) for testing, screenshots are a **massive hidden token cost**. A 4K screenshot ≈ 1,600 tokens. An optimized screenshot ≈ 300 tokens.

### Step 1: Detect current screenshot settings

```bash
# Check screen resolution
system_profiler SPDisplaysDataType 2>/dev/null | grep Resolution || xrandr 2>/dev/null | grep '*'

# Check if browser automation is configured
ls playwright.config.* puppeteer.config.* 2>/dev/null
```

### Step 2: Calculate optimal resolution

The goal: **find the minimum resolution where OCR/UI recognition still works reliably.**

| Use Case | Recommended Resolution | Approx Size | Approx Tokens |
|----------|----------------------|-------------|---------------|
| Full 4K (no optimization) | 3840 × 2160 | ~2 MB | ~1,600 |
| UI element detection | 1280 × 720 | ~200 KB | ~500 |
| Text OCR (general) | 1024 × 768 | ~120 KB | ~400 |
| Text OCR (minimum viable) | 800 × 600 | ~80 KB | ~300 |
| Layout-only checks | 640 × 480 | ~40 KB | ~200 |

**Rule of thumb: 1280×720 is the sweet spot** — enough for both OCR and UI element detection.

### Step 3: Generate optimization config

For Playwright:
```javascript
// playwright.config.ts — add to use section
use: {
  viewport: { width: 1280, height: 720 },
  screenshot: {
    quality: 60,  // JPEG quality (default 100)
    type: 'jpeg',  // JPEG is smaller than PNG
  },
}
```

For OpenClaw browser tool:
```bash
# Before screenshot, resize viewport
mcp__playwright__browser_resize --width 1280 --height 720
# Or for Claude-in-Chrome
mcp__claude-in-chrome__resize_window --width 1280 --height 720
```

For manual screenshot compression:
```bash
# Using ImageMagick (if available)
convert screenshot.png -resize 1280x720 -quality 60 screenshot_optimized.jpg

# Using sips (macOS built-in)
sips -Z 1280 screenshot.png --out screenshot_optimized.jpg
```

### Step 4: Output savings estimate

```markdown
## 📸 Screenshot Optimization Report

| Metric | Before | After | Savings |
|--------|--------|-------|---------|
| Resolution | {original} | {optimized} | — |
| Avg file size | {before_size} | {after_size} | {pct}% |
| Tokens per image | ~{before_tokens} | ~{after_tokens} | {token_pct}% |
| Screenshots/session | ~{count} | ~{count} | — |
| **Tokens/session** | **{total_before}** | **{total_after}** | **{total_pct}%** |
| **Cost/session** | **${cost_before}** | **${cost_after}** | **${cost_saved}** |

Recommendation applied: viewport set to {width}×{height}, JPEG quality {quality}%.
```

---

## 8. `/cost-loops` — Agent Loop Optimization

In harness mode and team mode, **each agent loop iteration re-transmits the full conversation context.** Reducing iterations and increasing work per iteration directly cuts token overhead.

### Step 1: Analyze current loop patterns

Check for common loop-heavy patterns:

```bash
# Check if openclaw.json has loop/iteration settings
cat openclaw.json 2>/dev/null | python3 -c "
import sys, json
try:
    cfg = json.load(sys.stdin)
    print(json.dumps(cfg.get('agents', {}), indent=2))
except: print('No agent config found')
"
```

### Step 2: Apply loop optimization rules

| Pattern | Problem | Optimized |
|---------|---------|-----------|
| Test 10 pages, 1 per loop | 10× context re-transmission | Test 3-4 pages per loop, 3 iterations |
| Retry on failure, no limit | Infinite loop risk, burns tokens | Set `max_iterations: 5`, then stop or escalate |
| Agent checks status every loop | Redundant reads each iteration | Batch status checks, report once at end |
| Sub-agent per file | N agents × full context each | 1 agent processes multiple files sequentially |

### Step 3: Generate recommended settings

```json
{
  "agents": {
    "max_iterations": 5,
    "batch_size": "auto",
    "on_limit_reached": "summarize_and_stop",
    "loop_optimization": {
      "combine_small_tasks": true,
      "min_work_per_iteration": 3,
      "checkpoint_interval": 2
    }
  }
}
```

### Step 4: Calculate overhead reduction

```markdown
## 🔄 Loop Optimization Report

### Current pattern
- Avg iterations per task: {current_iterations}
- Context size per iteration: ~{context_tokens} tokens
- Context overhead: {iterations} × {context_tokens} = **{total_overhead} tokens**

### Optimized pattern
- Avg iterations per task: {optimized_iterations}
- Work per iteration: {work_per_iteration}× more
- Context overhead: {opt_iterations} × {context_tokens} = **{opt_overhead} tokens**
- **Overhead reduction: {reduction_pct}%**

### Monthly impact
- Tasks/day: ~{tasks}
- Monthly savings from loop optimization: **${monthly_savings}**
```

---

## 平台兼容性

| 功能 | OpenClaw | Claude Code |
|------|----------|-------------|
| 模型路由 | ✅ 完整支持 | ✅ 通过 /model 切换建议 |
| 上下文压缩 | ✅ 完整支持 | ✅ 生成快照文件 |
| Heartbeat 优化 | ✅ 完整支持 | ⚠️ 需手动配置 cron |
| 消耗报告 | ✅ 读取日志 | ✅ 基于会话估算 |
| 配置生成 | ✅ openclaw.json | ✅ settings.json |