How do I install the Token Guard skill?

Run npx skillmds@latest add johnalbertini14-glitch/token-guard in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Token Guard skill do?

TokenGuard — LLM API 429 Prevention Engine It is listed under AI & ML on SkillMD.

Is Token Guard safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Token Guard?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Token Guard free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Token Guard?

johnalbertini14-glitch (@johnalbertini14-glitch) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Token Guard by johnalbertini14-glitch

Name: Token Guard
Rating: 4.5 (4 reviews)
Author: johnalbertini14-glitch

File contents

TokenGuard — 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 → death spiral
No built-in way to detect runaway/duplicate requests

Features

Feature	Description
Pre-flight Token Estimation	Estimates token count before API call (CJK-aware, no tiktoken dependency)
Real-time Quota Tracking	Tracks per-model per-minute token usage with sliding window
Smart Throttle	Auto-waits when quota > 80%, blocks at > 95%
Duplicate Detection	Blocks identical requests within 60s window (3+ = runaway)
Response Caching	Caches successful responses for duplicate requests
Auto Model Fallback	Switches to cheaper/available model when primary is exhausted
429 Error Parser	Extracts exact retry delay from Google/Anthropic error responses
Batch vs Mistake Detection	Distinguishes 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/
├── SKILL.md          # This file
└── scripts/
    └── 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": "🟢 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 — every byte counts.

---name: token-guarddescription: TokenGuard — LLM API 429 Prevention Engine---# TokenGuard — 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## DescriptionPrevents 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."*## ProblemWhen 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 → death spiral- No built-in way to detect runaway/duplicate requests## Features| Feature | Description ||---------|-------------|| **Pre-flight Token Estimation** | Estimates token count before API call (CJK-aware, no tiktoken dependency) || **Real-time Quota Tracking** | Tracks per-model per-minute token usage with sliding window || **Smart Throttle** | Auto-waits when quota > 80%, blocks at > 95% || **Duplicate Detection** | Blocks identical requests within 60s window (3+ = runaway) || **Response Caching** | Caches successful responses for duplicate requests || **Auto Model Fallback** | Switches to cheaper/available model when primary is exhausted || **429 Error Parser** | Extracts exact retry delay from Google/Anthropic error responses || **Batch vs Mistake Detection** | Distinguishes intentional bulk processing from error loops |## Supported ModelsPre-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```pythonfrom token_guard import TokenGuardguard = 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) # retryelif 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 OpenClawAdd to your agent's config or use as a middleware:```yamlskills: - token-guard```The agent can invoke TokenGuard before any LLM API call to prevent quota exhaustion.## File Structure```token-guard/├── SKILL.md # This file└── scripts/ └── token_guard.py # Main engine (zero external dependencies)```## Status Output Example```json{ "models": { "gemini-3-flash": { "tpm_limit": 1000000, "used_this_minute": 750000, "remaining": 250000, "usage_pct": "75.0%", "status": "🟢 OK" } }, "stats": { "total_checks": 42, "tokens_saved": 128000, "blocks": 3, "fallbacks": 2 }}```## Zero DependenciesPure Python 3.10+. No pip install needed. No tiktoken, no external API calls.Designed for the $7 Bootstrap Protocol — every byte counts.

Frequently asked questions

Token Guard