Skill: youxiyin/tsaver (version 2.1.0)

--- name: token-saver description: > Five-phase token audit & optimization framework for OpenClaw: Discover → Prioritize (3D matrix) → Optimize (9 category techniques) → Validate → Monitor. Universal; adapt via appendix. Trigger: "省点 token", "token 优化", "token saver", "token audit", "检查 token 消耗" Version history: v1.0 (2026-05-03) — 初始框架, 6 categories v1.5 (2026-05-04) — +G Provider Caching, +H Behavioral Discipline v2.0 (2026-05-12) — +I Context Engineering v2.1 (2026-05-14) — +J Intelligent Model Routing (OpenSquilla) + Quick Start guide, +Category Decision Tree + Monitor phase checkpoints --- # Token Saver > Universal token audit & optimization framework for OpenClaw agents. > Based on real-world practice (2026-05-04). ## Core Principles 1. **Tier your model usage** — Simple tasks use cheap models; complex reasoning uses expensive ones. Don't mix the two. 2. **Prompts say *what*, not *why*** — Background rationale and philosophy are noise to an agent. Strip them. 3. **Batch > Serial** — One call for 10 results costs marginally more than three calls for 3+3+4 results. Combine. 4. **Context = Cost** — Every file loaded at session start, every tool schema registered, every past message injected — all have a token price. 5. **Idle = Zero burn** — Nighttime, weekends, and idle periods should run nothing. Configure active hours. ## Output After each full execution, write a report (`token-audit-report-YYYY-MM-DD.md`) containing: before/after comparison table, estimated weekly savings per change, items deferred and why, recommended next step. --- ## Quick Start Not every audit needs the full Phase 1-5 treatment. Use these shortcuts based on your goal: ### 🚀 Express Audit (15 min) Trigger: "快速 token 审计" 1. Run **Phase 1A** (enumerate cron tasks) + **Phase 1D** (model tier map) 2. Skip directly to **Phase 3** category table and pick the lowest-hanging fruit 3. Apply **Safe** techniques only, no user confirmation needed 4. Skip Phase 4 and Phase 5 — just log the changes ### 🎯 Quick Wins (<5 min) Trigger: "快速省 token" Go straight to these high-impact, zero-risk techniques: 1. **A3** (constrain output — add conciseness instruction) 2. **B1** (right-size each task — cheapest viable model) 3. **G1** (fixed prefix first — static prefix + dynamic suffix) 4. **H1/H2** (default to working path, fail once and switch) Apply directly without audit preamble. ### 🔄 When to run full audit | Indicator | Action | |-----------|--------| | First time using this skill | **Full Phase 1-5** — establish baseline | | Cron tasks changed significantly | **Phase 1-3** — re-discover + re-optimize | | New provider/API added | **Phase 1B + 3** — check config + optimize | | >30 days since last audit | **Phase 1-2** — measure drift, then re-optimize | | Just want a quick check | **Quick Wins** or **Express Audit** | --- ## Phase 1: DISCOVER — Map the Full Token Landscape ### 1A Enumerate All Automated Tasks Read your cron/scheduled task configuration (e.g. `~/.openclaw/cron/jobs.json`). For each task record: - `name` - `model` (or "default" if unset) - `message` / `prompt` length in chars - `schedule` frequency (daily / weekly / other) - `delivery.mode` (announce / none) - `sessionTarget` (isolated / main) ### 1B Analyze Agent Configuration Inspect your gateway config (e.g. `openclaw.json`): - `agents.defaults.heartbeat.*` — interval, active hours, isolated session, light context flag - `agents.defaults.compaction.mode` — message retention aggressiveness - `agents.list[].tools.profile` — full, coding, or custom - `agents.list[].model` — per-agent model override ### 1C Measure Context Load List every file that is injected at session start (typically files in the workspace root directory). Measure each in chars and estimate token cost (~3 chars per token for CJK-heavy text, ~4 for English-heavy). If LCM (Lossless Context Management) is active, note the number and average size of compacted summary blocks injected per turn. If tool schemas are accessible, estimate total schema chars: (count of registered tools × average schema size in chars). ### 1D Map Models to Tiers Categorize all available models into three tiers based on capability and cost: - **🏆 Premium** (strong reasoning, high cost): e.g. deepseek-v4-pro, gpt-5.x - **🟡 Standard** (balanced): e.g. deepseek-v4-flash, minimax-m2.7 - **🟢 Economy** (lightweight): e.g. minimax-m2.7-highspeed, ollama local Map each task from 1A to its current model tier. > ⚠️ **Checkpoint**: Before moving to Phase 2, present your Phase 1 findings > (task inventory, file sizes, model tier map) to the user. > Confirm that the inventory is complete and the measurements are correct. > This prevents optimizing the wrong things. --- ## Phase 2: PRIORITIZE — Build Your Decision Matrix Score each finding from Phase 1 along three independent dimensions: | Dimension | Scale | Assessment | |-----------|-------|------------| | **Token Impact** 🎯 | High / Med / Low | Tokens per occurrence × occurrences per period | | **Risk** ⚠️ | Safe / Moderate / High | Can you undo it? Does it affect core function? | | **Effort** 🔧 | Easy / Med / Hard | Single config change? Multi-file edit? Needs research? | ### How to Score Compute a relative priority for each finding by inverting Risk and Effort: ``` Priority = ImpactWeight × (1 / RiskWeight) × (1 / EffortWeight) ``` Where each dimension maps to a simple numeric weight: - Impact: High=3, Med=2, Low=1 - Risk: Safe=1, Moderate=2, High=3 - Effort: Easy=1, Med=2, Hard=3 Focus on items scoring ≥ 1.5 first. Skip items < 1.0 unless they are trivially easy (effort=1) and safe (risk=1). ### Common High-Impact Patterns These patterns tend to score high across most deployments: | Pattern | Typical Impact | Typical Risk | Typical Effort | |---------|---------------|-------------|----------------| | Overly verbose task prompts | High | Safe | Easy | | Heavy models on simple tasks | High | Safe | Easy | | No active hours on heartbeat | Med-High | Safe | Easy | | Duplicated content across bootstrap files | Med-High | Safe | Easy-Med | | Full tool profile on task-specific agents | High | Moderate | Easy | | Idle-time session not configured | Med | Safe | Easy | | Outdated tool/plugin configs still loaded | Low-Med | Safe | Easy | > ⚠️ **Checkpoint**: Show your top-3 priority items to the user. > Confirm direction before starting optimization. > If the highest-score items seem wrong, revisit Phase 1 measurements. --- ## Phase 3: OPTIMIZE — Apply Categorical Techniques > ⚠️ **User confirmation gate**: Techniques marked **Moderate** or **High** risk > involve config changes, profile switches, or task merging. Before applying them, > present the proposed change using this template and get explicit approval: > > ``` > ## Proposed Change > **Technique**: [category/technique name] > **Target**: [file/config path] > **Before**: [current state, chars/tokens if measurable] > **After**: [proposed state, estimated savings] > **Risk**: [Moderate/High] > **Rollback**: [how to undo] > ``` > > Techniques marked **Safe** can be applied directly. Each category below contains a set of techniques. Apply them in priority order from Phase 2 — start with the highest-score items first, regardless of which category they fall into. ### Failure Recovery If a technique causes a problem: - **Config change**: Restore the backed-up config file and reload. - **Cron merge broken**: Restore the old separate cron job from version control or re-create it from the original prompt. - **Profile switch issue**: Revert to "full" profile, report the missing tool. - **Prompt compression over-aggressive**: Restore from the diff backup (keep pre-optimization prompt versions in a `prompts/backup/` directory). ### Category Selection Guide Match your Phase 2 findings to the best starting category: | Finding | Start With | |---------|-----------| | Verbose task prompts (background context, philosophy) | **A** Prompt Simplicity | | Heavy models on simple automation tasks | **B** Model Tiering | | Bootstrap files >2K chars each, duplicated content | **C** Context Slimming | | Full tool profile, rarely-used tools registered | **D** Tool Profile Optimization | | Verbose agent output, too many turns per task | **E** Output Discipline | | No active hours, co-located tasks running separately | **F** Session Lifecycle | | Repeated system prompts without caching structure | **G** Provider-Side Caching | | Agent retries failed approaches instead of switching | **H** Behavioral Discipline | | Simple/complex tasks both use premium model | **J** Intelligent Model Routing | ### Category Decision Tree If you're not sure which category to start with, follow this tree from top to bottom — the first match tells you your likely best starting category: ``` 1. Is the main session slow or expensive? → Check B (tiering) and J (routing) → Also check D (too many tools loaded?) 2. Are cron jobs consuming more than expected? → Check A (prompts too wordy?), then B (wrong model?) → If F (same-tier jobs not batched?) 3. Is context getting cut off mid-task? → Check C (bootstrap too large?) → I (progressive disclosure?) → Then J3 (incremental delivery?) 4. Are agent outputs too verbose? → Check E (output discipline) → H (behavioral discipline) 5. Is the same heavy prompt repeated across tasks? → Check G (provider-side caching: fixed prefix first?) 6. Are you seeing the same errors repeatedly? → Check H2 (fail once, switch) → H4 (fix root cause) 7. Default (no obvious symptom): Run Phase 1 from scratch → Phase 2 will tell you where to go ``` > **Pro tip**: Start with G (Provider-Side Caching) if you use DeepSeek. > Cache pricing is 0.83% of uncached — fixing prefix structure alone > can cut token costs by 90%+. ### A. Prompt Simplicity | Technique | Description | Risk | |-----------|-------------|------| | **A1** Strip preamble | Remove background/rationale paragraphs from task prompts. Keep only: trigger, action, output format. *Before:* "你是系统监控助手。每天检查服务器状态：CPU使用率>80%告警、内存>90%告警、磁盘>85%告警、SSL证书<30天告警。每个告警按严重程度分别处理：严重→立即通知值班、一般→发运维邮件、提示→记录日志。" *After:* "系统监控。检查：CPU(>80%) Mem(>90%) Disk(>85%) SSL(<30d)。告警：严重→立即、一般→邮件、提示→日志。" (360→110 chars, -69%) | Safe | | **A2** Bullet points > prose | Replace multi-sentence descriptions with keyword checklists. | Safe | | **A3** Constrain output | Add "Answer concisely in ≤3 lines" or equivalent to reduce generated tokens. | Safe | | **A4** Remove redundancy | Delete "What NOT to do" sections — proper instructions make negatives implicit. | Safe | | **A5** Reference > inline | Replace full instructions for sub-tasks with file references ("See X.md") when the referenced file is always loaded. | Safe | ### B. Model Tiering | Technique | Description | Risk | |-----------|-------------|------| | **B1** Right-size each task | Map every automated task to the cheapest model that can do it adequately. Test borderline cases. | Safe | | **B2** Define tier boundaries | Document which model(s) belong to each tier so new tasks are assigned correctly. | Safe | | **B3** Batch same-tier runs | Schedule same-tier tasks back-to-back to reuse the same session (single context load). | Moderate | ### C. Context Slimming | Technique | Description | Risk | |-----------|-------------|------| | **C1** Measure every boot file | List all files loaded at session start and identify those > 2K chars for potential trimming. | Safe | | **C2** Cross-reference dedup | When the same content appears in 2+ files (e.g. "Core Principles" in SOUL.md and IDENTITY.md), keep it in one authoritative file and replace the others with a `详见 <file>` reference. | Safe | | **C3** Archive aged-out content | Move old diary entries, superseded milestones, and historical promoted entries to a dedicated archive directory. | Safe | | **C4** Trim to one-liner | Convert verbose descriptions to single-line summaries. *Before:* "This project's coding conventions were established after three code reviews revealed inconsistent patterns: use 2-space indent for HTML/CSS, 4-space for Python, tabs for Go. Prefix private methods with underscore. No Hungarian notation. Import order: stdlib, third-party, local." *After:* "Coding conventions (see CONTRIBUTING.md) — 6 rules, numbered." Actionable instructions stay; background context goes. | Safe | ### D. Tool Profile Optimization | Technique | Description | Risk | |-----------|-------------|------| | **D1** Size your tool schema | Count all registered tools and estimate total schema chars. This is typically the single largest per-turn overhead. | Safe (measure only) | | **D2** Switch profile per agent | Use "coding" profile for sub-agents/cron jobs (excludes browser, canvas, media generation, feishu tools). Use "full" only where those tools are actually needed. | Moderate (test on sub-agents first) | | **D3** Disable unused tools | If you have disabled skills or orphaned plugin tools still registering schemas, disable or remove them from the registry. Check `skills.entries` and `plugins.load.paths`. | Safe | | **D4** Create custom profile | If neither "full" nor "coding" fits, define a custom profile with exactly the 15-25 tools your use-case needs. Requires config reload. | High | ### E. Output Discipline | Technique | Description | Risk | |-----------|-------------|------| | **E1** No operation narration | Remove "I'll...", "Let me check..." patterns. Do the action directly. | Safe (behavioral) | | **E2** Lead with conclusion | Put the answer first. Add explanation only when needed. | Safe (behavioral) | | **E3** Batch turns | Read → plan → apply all changes in as few turns as possible, instead of read→think→edit→think→verify per-item. Each extra turn adds LCM context overhead. | Safe (behavioral) | | **E4** Sub-agent conciseness | When spawning sub-agents, specify a concise return format. Their full output is injected into context if returned. | Safe | ### F. Session Lifecycle | Technique | Description | Risk | |-----------|-------------|------| | **F1** Set active hours | Configure `heartbeat.activeHours` so no work runs during idle time (overnight, weekends). | Safe | | **F2** Isolated sessions | Set `heartbeat.isolatedSession: true` so periodic checks don't accumulate in the main session. | Safe | | **F3** Light context | Set `heartbeat.lightContext: true` to skip loading all bootstrap files — only HEARTBEAT.md is injected. | Safe | | **F4** Merge co-located tasks | If two cron jobs run within minutes of each other (e.g. both at 23:xx), merge them into one session with a combined prompt. Copy both prompts into one job's `message` field separated by a blank line, then remove the later job. Saves one full startup context per day. | Moderate | | **F5** Merge example | Before: Job A at 23:00 (System health check), Job B at 23:10 (Log cleanup). After: Single job at 23:00 with prompt "Do A then B.~A: ...~B: ..." | Moderate | | **F6** Configure queue | If the platform supports message queue settings (debounce, collect), tune them to prevent rapid-turn accumulation during tool execution. | Safe | ### G. Provider-Side Caching > **Impact is 10× any other category.** DeepSeek V4 Pro cached price is 0.83% of > uncached. Cache hit rates of 91-96% are achievable with proper prompt structure. | Technique | Description | Risk | |-----------|-------------|------| | **G1** Fixed prefix first | Design all prompts as `[static prefix] + [dynamic suffix]`. Static prefix includes system instructions, bootstrap summary, and tool schemas. Dynamic suffix includes runtime instruction. This maximizes KV cache hits on the provider side. *Wrong:* "Analyze this code for memory leaks...你是代码审查助手，审查规则如下：..." *Right:* "你是代码审查助手，审查规则如下：...现在分析这段代码的内存泄漏：..." | Safe | | **G2** Session contiguity | Don't insert unrelated messages between consecutive calls to the same model — this breaks the KV cache prefix. Batch related calls into a single turn instead. | Safe | | **G3** Monitor cache rate | Check provider dashboards for cache hit rate. If <80%, your prefix structure likely has variability. Fix it. | Safe | | **G4** Route to best caching provider | Different providers have wildly different cached prices. DeepSeek V4 Pro: 0.83% of uncached. MiniMax: ~20%. Route routine tasks to the provider with the best cache economics. | Moderate | ### H. Behavioral Discipline > These are zero-config, zero-cost techniques. The savings come from how you use > the system, not how it's configured. | Technique | Description | Risk | |-----------|-------------|------| | **H1** Default to working path | Use known-working tools before alternatives. Don't retry tools known to be broken in the current deployment — each retry is a wasted tool call + error response. *Bad:* web_search (broken) → error → web_search again → error → baidu-search → works *Good:* baidu-search → works (first attempt) | Safe | | **H2** Fail once, switch | If a method fails, switch immediately to a known alternative. Don't retry the same approach with slightly different parameters. Each retry costs full tool-call tokens. | Safe | | **H3** Batch > Poll | Gather all data before acting instead of incrementally. One `exec` or `read` call that returns 10 results costs less than 5 separate calls returning 2 each. | Safe | | **H4** Fix root cause | If a tool works inconsistently due to a known config issue (API key expired, wrong provider), fix the config. Working around it each time costs more in accumulated failed calls. | Safe | ### I. Context Engineering (2026-05-12 新增) > Context Engineering 是 2026 年从 Prompt Engineering 演进出的上层方法论。 > 核心原则：渐进式披露 (Progressive Disclosure) — 仅在任务需要时加载特定模块。 > 北京大学论文《Meta Context Engineering via Agentic Skill Evolution》实测： > Token 消耗降低 60%，任务成功率提升 45%。 | Technique | Description | Risk | |-----------|-------------|------| | **I1** Progressive disclosure | 按任务类型分级加载系统能力描述。不需要的技能说明不加载。与 token-saver 的 C1-C4 互补：C 系列负责文件级裁剪，I1 负责能力级裁剪。 Wrong: Agent 启动时加载全部技能描述 Right: Agent 启动时按任务类型加载对应技能（搜索类任务只加载搜索相关技能描述） | Safe | | **I2** Semantic context scoring | 用语义重要性评分代替固定滑动窗口。计算历史交互与当前 query 的语义相似度，只保留 top-3 + 最近 1 轮。 推荐实现：Sentence-BERT 计算余弦相似度，按得分排序裁剪到 token budget。 参考开源项目：Agent Skills for Context Engineering | Safe | | **I3** Fixed prefix pattern | 设计所有 prompt 为「static prefix + dynamic suffix」。静态前缀包括系统指令、bootstrap 摘要、工具 schema 描述。动态后缀包括本次运行指令。最大化 provider 端 KV cache 命中。 Wrong: 每次运行时先写任务指令再补系统描述 Right: 系统描述在前，任务指令在后 | Safe | | **I4** Tool call circuit breaking | 在调用高成本外部工具（SQL 查询、图像生成、文件写入）前增加预检代理。校验参数合法性 + 资源预估。超限或非法直接拒绝，不触发实际调用。 适用场景：web_fetch 超长 URL、exec 高风险命令、文件写入大文件。 | Safe | | **I5** Cost-aware context pruning | 按 taskType 和 budget tokens 动态决定上下文精度。简单任务用精简 prompt，复杂任务才加载完整能力描述。 实现：在 cron payload 中嵌入 task_type 字段，根据该字段调度上下文模板。 | Safe | ### J. Intelligent Model Routing (2026-05-14 新增) > 基于 OpenSquilla（开源 Token 优化引擎）方案。 > Core insight: 路由决策本身不消耗 Token — 在本地判定任务复杂度后决定走哪个模型。 > 与 B 系列（Model Tiering）的区别：B 说"每个任务应该固定分配一个 tier"， > J 说"同一个任务的每一次调用动态判断走哪个 tier"。 | Technique | Description | Risk | |-----------|-------------|------| | **J1** Dynamic inbound grading | 对每次入站请求做轻量复杂度判定（prompt length + expected reasoning depth + tool count），自动路由到对应 tier 的模型。 判定规则示例： - 🟢 Economy: 简单信息查询（token 消耗 < 1K，无推理要求）→ sensenova-6.7-flash-lite / minimax-m2.7 - 🟡 Standard: 一般分析任务（1K-5K，轻度推理）→ deepseek-v4-flash - 🏆 Premium: 深度推理（>5K，复杂推理/多工具）→ deepseek-v4-pro 实现：在 cron/子代理 payload 中嵌入 `model` 字段，或使用中间路由代理做分类。 *Before:* 所有每日 cron 统一用 deepseek-v4-pro，包括简单版本检查 *After:* 版本检查→sensenova-6.7-flash-lite，领域探针→deepseek-v4-flash， 深度分析→deepseek-v4-pro。按任务特征分级，非一刀切。 | Safe | | **J2** Local routing decision | 路由判定在本地完成（不需要发 request 到远程判断路由）。 方案：在任务 payload 中预置 `routing_hint` 字段（economy/standard/premium）， 由编排层基于任务特征（prompt length、任务类型、预期工具调用数）决定。 *Bad:* 每次调用先发一个轻量请求问"这个走哪个模型？"（浪费 Token） *Good:* 任务描述本身包含路由线索，编排层本地匹配 | Safe | | **J3** Incremental context delivery | OpenSquilla 核心方案之一：先发送最小必要上下文，不足时增量补充。 与 C 系列（Context Slimming）区别：C 是文件级裁剪（减少启动加载量）， J3 是调用级递进（先发主干，模型需要时再发细节）。 实现：prompt 中先给出高度压缩的摘要版本，后面用"如需详情请见"标记展开点。 结合 OpenClaw 的 lossless-claw 机制：LCM 摘要本身已经是一个渐进式实现。 | Safe | | **J4** Cache-aware routing | 统计各 provider 的 KV cache 命中率，优先路由到命中率高的 provider。 当前已知：DeepSeek V4 Pro cache price = 0.83% of uncached。 如果命中了缓存的 prompt prefix（含 bootstrap + 技能描述），成本降低 99%+。 实现：在路由时优先选择上次使用同一模型+同一 prefix 结构的 provider。 | Moderate | | **J5** Fallback chain | 当分配的路由模型不可用（API 超时/限流/报错），自动降级到备用模型。 与 H2（Fail once, switch）的区别：H2 是行为纪律（失败了就换方法）， J5 是配置级的自动 failover 链。 示例 Fallback Chain： 1️⃣ deepseek-v4-pro → 2️⃣ deepseek-v4-flash → 3️⃣ sensenova-6.7-flash-lite 实现：在 cron/子代理 payload 中使用 `fallbacks` 字段配置降级顺序。 *Before:* cron job 用 minimax-m2.7，卡死后无降级 → 任务永远失败 *After:* 同一 cron 配置 `fallbacks: ["deepseek-v4-flash", "deepseek-v4-pro"]` → minimax 卡死后自动切到 deepseek-v4-flash 执行 | Safe | > **权衡**：智能路由的收益上限取决于任务复杂度分布。如果 80% 的任务是简单查询， > 智能路由的 Token 节省可达 60-80%。如果大部分任务已经是标准/经济 tier， > 额外收益有限。建议做一次任务复杂度分布扫描后再选择启用哪些 J 技术。 --- ## Phase 4: VALIDATE — Confirm Results ### 4A Prompt Length Delta Before/after comparison of all modified prompts and files. Include total chars and estimated tokens saved. ### 4B Config Integrity After editing JSON configuration files, validate: ```bash python3 -c "import json; json.load(open('<config-path>')); print('OK')" ``` ### 4C Functional Test - Verify cron tasks still start correctly (check `cron action=runs` or next scheduled trigger) - Verify heartbeat runs in configured active window - Read through compressed cron prompts to ensure key instructions survive ### 4D Generate Report Write `token-audit-report-YYYY-MM-DD.md` summarizing: - Changes made and per-change token savings - Total estimated weekly token reduction - Items deferred and why - Recommended next optimization Log each optimization cycle in `results.tsv` (see skill directory for format reference). This creates an audit trail for the quarterly deep audit (5B). --- ## Phase 5: MONITOR — Guard Against Regrowth ### 5A Periodic Token Watch (Optional) Optionally create a weekly cron (cheapest available model) that checks prompt lengths haven't crept back: ```json { "name": "token-watch-weekly", "schedule": { "kind": "cron", "expr": "0 10 * * 1", "tz": "Asia/Shanghai" }, "payload": { "kind": "agentTurn", "model": "<cheapest-model>", "message": "Check all cron prompt lengths. Flag any that grew >20% since last baseline.", "timeoutSeconds": 120 }, "sessionTarget": "isolated", "delivery": { "mode": "none" } } ``` ### 5B Quarterly Deep Audit Run the full Phase 1-4 cycle every quarter using the cheapest available model. Compare results against previous reports to spot regrowth trends. The quarterly audit MUST include: - Compare each cron's prompt length against last audit baseline - Check if unused categories crept back (complacency regrowth) - Verify all J routing hints still reflect actual task complexity - Re-run the Priority Matrix from Phase 2 to catch new high-impact items ### 5C Real-Time Drift Alerts (Optional) When token consumption suddenly spikes, it's usually one of three causes. Know which one: | Symptom | Likely Cause | Fix | |---------|-------------|-----| | A specific cron doubled in token count | Prompt crept back (someone added preamble) | A1 Strip preamble | | All crons increased proportionally | Model changed (e.g. dev switched back to pro from flash) | B1 Right-size, check default model | | Session-level spikes, not cron | Tool profile expanded / new plugin registered | D1 Size tool schemas, check profile | | Intermittent spikes | Prefix changed → KV cache missed | G1 Fixed prefix, check for variation | If you have access to provider dashboards: - **Monitor cache hit rate** — DeepSeek dashboard shows prefix cache stats - **If hit rate drops below 80%** -> inspect recent prompt changes for prefix variation - **Track per-model token consumption weekly** — a 2× week-over-week jump is a red flag --- ## Safety Boundaries ### Configs That Need Gateway Restart Some configuration paths require a gateway restart to take effect: - `agents.defaults.heartbeat.*` (edit config file + restart) - `agents.list[].tools.profile` - `gateway.*`, `auth.*` - `plugins.*` — certain sub-fields ### What NOT to Compress These core mechanisms must be preserved even in an aggressive token budget: - Error detection logic (consecutive errors, failure alerts) - Essential signal handling (high-priority alerts → auto-escalation) - Drift detection for recurring tasks ### External References - OpenClaw Cron Jobs: https://docs.openclaw.ai/automation/cron.md - OpenClaw Standing Orders: https://docs.openclaw.ai/automation/standing-orders.md - OpenClaw Gateway Config: `openclaw gateway config` CLI - OpenClaw Agent Profiles: `agents.list[].tools.profile` in openclaw.json - Test Prompts: `test-prompts.json` in skill directory - Results Log: `results.tsv` in skill directory --- ## Appendix: Local Deployment Configuration This section is populated by the first execution of the Token Saver in a specific deployment. Replace the example values below with real ones. ### Configuration Paths | Item | Example Path | |------|-------------| | Cron jobs | `~/.openclaw/cron/jobs.json` | | Gateway config | `~/.openclaw/openclaw.json` | | Workspace root | `~/.openclaw/workspace/` | | Bootstrap files | AGENTS.md, SOUL.md, USER.md, MEMORY.md, HEARTBEAT.md, IDENTITY.md, TOOLS.md, STANDING-ORDERS.md | ### Baseline Measurements (example: Wave 2026-05-04) | File | Initial Size | After First Pass | Reduction | Techniques Used | |------|-------------|------------------|-----------|----------------| | SOUL.md | 7,034 | 3,521 | -50% | C2 (cross-ref), C4 (one-liner), A2 | | STANDING-ORDERS.md | 10,960 | 3,816 | -65% | C2 (cross-ref), A4 (remove redundancy) | | IDENTITY.md | 6,228 | 4,313 | -31% | C2 (dedup with SOUL.md), C4 | | AGENTS.md | 5,072 | 2,691 | -47% | C2 (ref to STANDING-ORDERS), C4 | | TOOLS.md | 8,893 | 7,488 | -16% | C4 (remove stale entries) | | MEMORY.md | 30,224 | 26,420 | -13% | C3 (archive promoted entries) | | **Total** | **68,411** | **48,249** | **-29%** | — | Per-session token savings from bootstrap compression: ~6,720 tokens. ### Benchmark: Compression by File Type | File Type | Typical Savings | Best Technique | |-----------|----------------|----------------| | Program/Protocol (STANDING-ORDERS.md) | 55-65% | A4 (remove boilerplate sections) | | Guide/Identity (SOUL.md, IDENTITY.md) | 30-50% | C2 (cross-reference dedup) | | Instructions (AGENTS.md) | 40-50% | C2 (replace lists with file refs) | | Knowledge base (MEMORY.md) | 10-20% | C3 (archive old entries only) | | Config/state table (TOOLS.md) | 10-20% | C4 (remove stale entries only) | ### Task-to-Model Map | Task | Model Tier | Model | |------|-----------|-------| | Version check | Economy | minimax-m2.7 | | Demand scanning | Standard | deepseek-v4-pro (needs search) | | Domain probe | Economy | minimax-m2.7 | | Dreaming (memory integration) | Economy | minimax-m2.7 | | Doc maintenance | Economy | minimax-m2.7 | | WaveCap daily expansion | Standard | deepseek-v4-pro (needs reasoning) | | Weekly review | Premium | deepseek-v4-pro | | Friday topic selection | Premium | deepseek-v4-pro | | Main session | Standard | deepseek-v4-flash | ### Deferred Items | Item | Reason | Condition to Revisit | |------|--------|---------------------| | Tool profile for main agent | High risk (may break unexpected features) | After sub-agent coding profile proven in production for 1 week | | Cron task merging | Needs user confirmation; may affect reliability | Next token audit cycle | | Compaction mode change (safeguard→normal) | Needs config reload | When gateway restarted for other reasons | ### Deployment-Specific Constraints - **Network**: GFW blocks chatgpt.com, api.openai.com. All OpenAI/Codex models unavailable. - **Models available**: deepseek-v4-pro (premium), deepseek-v4-flash (standard), minimax-m2.7 (economy). - **File paths**: Standard OpenClaw paths under `~/.openclaw/`. - **Git**: Workspace is a git repository; all changes version-controlled.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)