proxy-token-optimizer

Optimize LLM token usage and API costs for the openclaw-manager proxy platform. Provides model-tier routing (route simple prompts to glm-4.7-flashx instead of glm-4.7), heartbeat cost reduction (force heartbeat calls to use the cheapest model with optimized intervals), context lazy loading (generate optimized AGENTS.md that loads only necessary context files per prompt complexity), and platform-level usage analytics (query real usage_records from PostgreSQL to generate reports and quota-matching advice). Use this skill whenever the user mentions token optimization, reducing API costs, model routing, heartbeat optimization, context loading strategy, usage reports, quota analysis, or anything related to saving money on LLM API calls in the openclaw-manager project. Also trigger when the user asks about which model to use for different task types, or wants to analyze per-instance token consumption patterns.

Reduces LLM API costs for the openclaw-manager multi-tenant proxy platform through four strategies:

1. Model-tier routing — Route prompts to the cheapest capable model
2. Heartbeat optimization — Cheapest model + longer intervals for heartbeat calls
3. Context lazy loading — Load only the context files each prompt actually needs
4. Platform usage analytics — Real data from PostgreSQL, not estimates

Why these strategies matter

The openclaw-manager platform proxies LLM requests for multiple OpenClaw instances through providers like zai-proxy, zai-coding-proxy, and kimi-coding-proxy. Each provider offers models at different price points (e.g., glm-4.7 vs glm-4.7-flashx). Without optimization, every request — including simple greetings and heartbeat pings — uses the default (expensive) model, and every session loads the full context regardless of need. These four strategies target the highest-impact cost drivers.

Quick start

All instance-side scripts run locally with no dependencies. Platform-side scripts need DB access.

# Model routing — which model should handle this prompt?
python3 scripts/model_router.py "thanks!"
# → {"tier": "cheap", "recommended_model": "zai-proxy/glm-4.7-flashx"}

# Context optimization — which files does this prompt need?
python3 scripts/context_optimizer.py recommend "hi"
# → {"context_level": "minimal", "recommended_files": ["SOUL.md", "IDENTITY.md"]}

# Heartbeat config — generate openclaw.json patch
python3 scripts/heartbeat_config.py patch
# → {"agents": {"defaults": {"heartbeat": {"every": "55m", "model": "zai-proxy/glm-4.7-flashx"}}}}

# Unified CLI (all commands in one place)
python3 scripts/cli.py --help

Scripts reference

Instance-side (pure local, no network, no DB)

scripts/model_router.py

Routes prompts to the right model tier based on complexity analysis.

Tier logic:

• cheap → glm-4.7-flashx: Greetings, acknowledgments, heartbeats, cron jobs, log parsing. Cost savings: 5-10x vs standard.
• standard → glm-4.7: Code writing, debugging, explanations. Default for unclear prompts.
• premium → glm-4.7 (or k2p5 for kimi): Architecture design, deep analysis, strategy planning.

Supports Chinese and English patterns. Provider-aware — works with zai-proxy, zai-coding-proxy, and kimi-coding-proxy.

python3 scripts/model_router.py "<prompt>" [provider]
python3 scripts/model_router.py compare  # show all provider models

scripts/context_optimizer.py

Analyzes prompt complexity to recommend which context files to load, reducing unnecessary token consumption.

Context levels:

Level	When	Files loaded	Token savings
minimal	"hi", "thanks", short msgs	SOUL.md + IDENTITY.md (2)	~80%
standard	"write a function", normal work	+ memory/TODAY.md + conditional	~50%
full	"design architecture", complex tasks	+ MEMORY.md + all conditional	~30%

Also generates an optimized AGENTS.md template with lazy-loading rules baked in:

python3 scripts/context_optimizer.py recommend "<prompt>"
python3 scripts/context_optimizer.py generate-agents  # creates AGENTS.md.optimized

scripts/heartbeat_config.py

Generates openclaw.json configuration patches for heartbeat optimization:

• Forces heartbeat model to glm-4.7-flashx (cheapest available)
• Sets interval to 55 minutes (keeps prompt cache warm within 1-hour TTL, avoids cache rebuild cost)

python3 scripts/heartbeat_config.py recommend [cache_ttl_minutes]
python3 scripts/heartbeat_config.py patch  # output JSON patch for openclaw.json

Platform-side (requires DB connection)

These scripts query the usage_records PostgreSQL table for real data. Run from the openclaw-manager project root with the virtualenv activated.

scripts/usage_report.py

Generates usage reports from actual database records — not estimates.

python3 scripts/usage_report.py overview [days]     # platform-wide summary
python3 scripts/usage_report.py instance <name> [days]  # single instance detail

Overview includes: total calls/tokens, per-provider breakdown, per-model breakdown, top 10 instances by consumption, 7-day daily trend.

Instance report includes: per-model distribution, daily trend, lifetime totals.

scripts/quota_advisor.py

Compares actual 24-hour usage against quota plan limits to find mismatches:

• Wasteful: Usage below 20% of plan limit → suggest downgrade
• Throttled: Usage above 80% of plan limit → suggest upgrade

python3 scripts/quota_advisor.py analyze  # check all instances
python3 scripts/quota_advisor.py plans    # show available quota plans

Unified CLI

scripts/cli.py wraps all the above into a single entry point:

python3 scripts/cli.py route "<prompt>"       # model routing
python3 scripts/cli.py context "<prompt>"     # context recommendation
python3 scripts/cli.py generate-agents        # generate AGENTS.md
python3 scripts/cli.py heartbeat              # heartbeat config
python3 scripts/cli.py overview [days]        # platform usage (needs DB)
python3 scripts/cli.py report <name> [days]   # instance report (needs DB)
python3 scripts/cli.py advisor                # quota advice (needs DB)

Project integration points

This skill works with existing openclaw-manager infrastructure:

Component	File	How this skill uses it
Provider config	config/model.yaml	Model names/endpoints for routing
Proxy routing	config_service.py	Where _inject_proxy_providers() registers models
Usage recording	proxy_common/usage_recorder.py	Source of real usage data
Quota plans	config/llm_proxy.yaml	Plan definitions for quota advisor
Instance model	app/models.py	Instance metadata for reports

Expected savings

Strategy	Mechanism	Impact
Context lazy loading	Fewer tokens per request	50-80% context reduction
Model routing (flashx)	Lower per-token price	5-10x on simple tasks
Heartbeat → flashx	Lower heartbeat cost	Significant per-instance savings
Heartbeat interval 55min	Fewer API calls	~45% fewer heartbeat calls