Skill: edmonddantesj/token-guard (version 1.5.0)

# TokenGuard — LLM API 429 Prevention Engine <!-- 🌌 Aoineco-Verified | S-DNA: AOI-2026-0213-SDNA-TG01 --> **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 ```python 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: ```yaml 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 ```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 Dependencies Pure Python 3.10+. No pip install needed. No tiktoken, no external API calls. Designed for the $7 Bootstrap Protocol — every byte counts.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)