Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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Token Guard
Prevents LLM API 429 errors by estimating tokens, tracking quotas, throttling requests, detecting duplicates, caching responses, and auto-fallback by model.
skill openclawclawhub Free
Prevents LLM API 429 errors by estimating tokens, tracking quotas, throttling requests, detecting duplicates, caching responses, and auto-fallback by model.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/token_guard.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.5.0
Provenance
- Publisher
- edmonddantesj
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
TokenGuard โ LLM API 429 Prevention Engine
Version: 1.5.0 Author: Aoineco & Co. License: MIT Tags: rate-limit, 429, token-management, cost-optimization, llm-guard, high-performance
Description
Prevents LLM API 429 (Rate Limit / Resource Exhausted) errors by intercepting requests before they're sent. Designed for users on free/low-cost API plans who need maximum intelligence per dollar. Core philosophy: "Intelligence is measured not by how much you spend, but by how little you need."
Problem
When using LLM APIs (especially Google Gemini Flash with 1M TPM limit): Large documents (docx, PDFs) can consume the entire minute quota in one request Failed requests still count toward token usage Retry loops after 429 errors waste more tokens โ death spiral No built-in way to detect runaway/duplicate requests
Features
FeatureDescriptionPre-flight Token EstimationEstimates token count before API call (CJK-aware, no tiktoken dependency)Real-time Quota TrackingTracks per-model per-minute token usage with sliding windowSmart ThrottleAuto-waits when quota > 80%, blocks at > 95%Duplicate DetectionBlocks identical requests within 60s window (3+ = runaway)Response CachingCaches successful responses for duplicate requestsAuto Model FallbackSwitches to cheaper/available model when primary is exhausted429 Error ParserExtracts exact retry delay from Google/Anthropic error responsesBatch vs Mistake DetectionDistinguishes intentional bulk processing from error loops
Supported Models
Pre-configured quotas for: gemini-3-flash (1M TPM) gemini-3-pro (2M TPM) claude-haiku (50K TPM) claude-sonnet (200K TPM) claude-opus (200K TPM) gpt-4o (800K TPM) deepseek (1M TPM) Custom quotas can be added for any model.
Usage
from token_guard import TokenGuard guard = TokenGuard() # Before every API call: decision = guard.check(prompt_text, model="gemini-3-flash") if decision.action == "proceed": response = call_your_api(prompt_text) guard.record_usage(decision.estimated_tokens, model="gemini-3-flash") guard.cache_response(prompt_text, response) elif decision.action == "wait": time.sleep(decision.wait_seconds) # retry elif decision.action == "fallback": response = call_your_api(prompt_text, model=decision.fallback_model) elif decision.action == "block": print(f"Blocked: {decision.reason}") # If you get a 429 error: guard.record_429("gemini-3-flash", retry_delay=53.0)
Integration with OpenClaw
Add to your agent's config or use as a middleware: skills: - token-guard The agent can invoke TokenGuard before any LLM API call to prevent quota exhaustion.
File Structure
token-guard/ โโโ SKILL.md # This file โโโ scripts/ โโโ token_guard.py # Main engine (zero external dependencies)
Status Output Example
{ "models": { "gemini-3-flash": { "tpm_limit": 1000000, "used_this_minute": 750000, "remaining": 250000, "usage_pct": "75.0%", "status": "๐ข OK" } }, "stats": { "total_checks": 42, "tokens_saved": 128000, "blocks": 3, "fallbacks": 2 } }
Zero Dependencies
Pure Python 3.10+. No pip install needed. No tiktoken, no external API calls. Designed for the $7 Bootstrap Protocol โ every byte counts.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/token_guard.py