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.

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:

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:

⚠️ 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:

A. Prompt Simplicity

B. Model Tiering

C. Context Slimming

D. Tool Profile Optimization

E. Output Discipline

F. Session Lifecycle

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.

H. Behavioral Discipline

These are zero-config, zero-cost techniques. The savings come from how you use the system, not how it's configured.

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.

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

Baseline Measurements (example: Wave 2026-05-04)

Per-session token savings from bootstrap compression: ~6,720 tokens.

Benchmark: Compression by File Type

Task-to-Model Map

Deferred Items

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.