Wave Token Saver

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

Audits

Pending

ClawScanReview

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPending

Multi-engine malware detections and file reputation.

Install

openclaw skills install wave-token-saver

Token Saver

Universal token audit & optimization framework for OpenClaw agents. Based on real-world practice (2026-05-04).

Core Principles

Tier your model usage — Simple tasks use cheap models; complex reasoning uses expensive ones. Don't mix the two.
Prompts say what, not why — Background rationale and philosophy are noise to an agent. Strip them.
Batch > Serial — One call for 10 results costs marginally more than three calls for 3+3+4 results. Combine.
Context = Cost — Every file loaded at session start, every tool schema registered, every past message injected — all have a token price.
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"

Run Phase 1A (enumerate cron tasks) + Phase 1D (model tier map)
Skip directly to Phase 3 category table and pick the lowest-hanging fruit
Apply Safe techniques only, no user confirmation needed
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:

A3 (constrain output — add conciseness instruction)
B1 (right-size each task — cheapest viable model)
G1 (fixed prefix first — static prefix + dynamic suffix)
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:

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:

{
  "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.