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proxy-token-optimizer

为openclaw-manager多租户LLM代理平台提供四大核心优化:按任务复杂度路由到高性价比模型、心跳调用用低价模型+拉长间隔、按需懒加载上下文、基于PostgreSQL的真实用量分析。适用于用户提出降本、模型选择、用量统计等需求,通过命令行调用,输入prompt/实例名等参数,输出JSON建议、配置补丁或用量报告,有效降低API成本。

whyhit2005
whyhit2005
热度 61.78
最近更新 2026-05-17 15:24:15
分类:数据依赖:代码执行 / 读写文件应用场景:数据处理 / 自动化脚本 / 财务 / 报告撰写 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add proxy-token-optimizer
Metadata
name
proxy-token-optimizer
description
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.
metadata
{"openclaw":{"always":true}}

Proxy Token Optimizer

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