# Show HN: AIQualityHQ – Browser-native prompt QA and security guardrails > Source: > Published: 2026-10-22 08:55:23+00:00 # Automated QA & Security Guardrails for Prompts and AI Agents This prompt has a strong structure. Ship it to any model. All checks passed. Zero structural, privacy, or security issues detected. Developers and AI teams need to catch prompt defects, red-team injection vectors, and optimize token costs before shipping to users. AIQualityHQ provides deterministic quality linting, adversarial security scanners, and subword token intelligence today — with multi-model testing workflows as the platform evolves. Built for AI developers, prompt engineers, and AppSec teams — validating output contracts, enforcing role boundaries, red-teaming adversarial inputs, and eliminating token bloat. Need to measure token bloat or model headroom?[Analyze AI token usage](https://aiqualityhq.com/ai-token-analyzer) 38 Rules Deterministic prompt linter 15 Detectors OWASP LLM-01 security checks 30+ Models Context & token intelligence registry Browser-Native Local-first, reproducible & fast Ranked by product-hunting communities for speed, privacy & depth. AIQualityHQ is independently listed across LaunchTry, Shipstry, Launch List, Verified Tools, IndieHunt, Toolfio and EarlyHunt — no artificially inflated ratings or reviews. ## AI Quality Engineering Tools for Every Stage of the Prompt Lifecycle From initial prompt design to adversarial defense, token optimization, and automated refactoring — purpose-built browser tools that help teams **Check → Secure → Fix → Optimize → Audit**. ### [Prompt Linter & Contract Checker](https://aiqualityhq.com/checker) Evaluate prompt structure, constraints, clarity, and output schemas against 38 deterministic rules running locally in your browser. [Open Linter](https://aiqualityhq.com/checker) ### [Prompt Injection Security Scanner](https://aiqualityhq.com/prompt-injection-scanner) Red-team prompts and RAG contexts against 15 deterministic detector modules covering direct hijacking, exfiltration, and context poisoning. [Open Scanner](https://aiqualityhq.com/prompt-injection-scanner) ### [Prompt Rewriter & Target Compiler](https://aiqualityhq.com/prompt-rewriter) Automated prompt refactoring for detected defects. Resolves constraint conflicts, adds delimiters, and provides side-by-side Git visual diffs. [Open Rewriter](https://aiqualityhq.com/prompt-rewriter) ### [AI Token Analyzer](https://aiqualityhq.com/ai-token-analyzer) Decompose prompts into architectural layers (System, User, Context, Tools) with exact subword BPE precision and multi-model cost tables. [Analyze Tokens](https://aiqualityhq.com/ai-token-analyzer) ## The AIQualityHQ Suite by Product Domain Eleven browser-native applications organized across five core domains. Deterministic tools run locally in your browser with zero network calls; AI-assisted features are clearly labeled. ### Prompt Quality & Linting Static analysis for prompt templates, system instructions, and RAG conditioning. Catches conflicting constraints, role ambiguity, missing output delimiters, and formatting issues before code execution. [Prompt Linter & Contract Checker →38 deterministic rules evaluating structure, negative constraints, and output schemas](https://aiqualityhq.com/checker) [Quality Report Viewer →](https://aiqualityhq.com/report) ### Prompt Security & Guardrails Pre-deployment red-teaming and exploit detection. Evaluates inputs against direct prompt overrides, RAG context poisoning, behavioral jailbreaks, and system prompt exfiltration vectors. ### Token & Cost Intelligence Comprehensive token counting, subword fragmentation, cost estimation, and context capacity engineering. Built with exact BPE tokenizer logic and calibrated cross-provider models running directly in your browser with zero cloud storage. ### Prompt Engineering & Synthesis Active instruction authoring and automated defect fixing. Synthesize production-ready system prompts with role parameters or compile defective drafts into optimized model-specific prompts. ### AI Visibility & LLM Auditing Diagnose domain discoverability and brand grounding across frontier AI engines. Validate your `llms.txt` file and evaluate generative engine optimization (GEO) metrics. [AI Prompt Quality & LLM Auditor →Audit domain LLM readiness, llms.txt compliance, and response accuracy (supports BYOK keys)](https://aiqualityhq.com/ai-audit) [GEO Engineering Guide →](https://aiqualityhq.com/generative-engine-optimization-ai-visibility) ## Eliminate Token Bloat & Optimize Model Context Windows Every uncompressed prompt burns context headroom and drives unnecessary API expenditure. Follow the complete token engineering cycle: **Measure → Detect Waste → Compare Tokenizers → Verify Headroom → Optimize**. ### Calculate Your Token & API Cost Savings See how much static prompt pre-filtering & token optimization saves your team vs cloud evals. Estimates assume an average 18% token-bloat reduction from optimization and failed-injection filtering, and a 350ms cloud evaluation latency. Actual results vary by prompt and provider. ### [Deconstruct BPE Tokens](https://aiqualityhq.com/ai-token-analyzer) Break down prompts into system, context, user, and schema layers with subword precision. [Open Token Analyzer →](https://aiqualityhq.com/ai-token-analyzer) ### [Detect Conversational Fluff](https://aiqualityhq.com/ai-token-waste-analyzer) Automatically identify polite filler and redundant formatting that waste token bandwidth. [Open Waste Analyzer →](https://aiqualityhq.com/ai-token-waste-analyzer) ### [Context Window Headroom](https://aiqualityhq.com/ai-context-window-calculator) Monitor utilization gauges and avoid "Lost in the Middle" attention degradation risks. [Open Calculator →](https://aiqualityhq.com/ai-context-window-calculator) ### [Cross-Model Tokenizer BPE](https://aiqualityhq.com/ai-tokenizer-comparison) Compare token efficiency and subword chunking across OpenAI, Anthropic, Google & Meta. [Open Comparison →](https://aiqualityhq.com/ai-tokenizer-comparison) ## Red-Team Prompts Against Injections & Adversarial Jailbreaks Adversarial inputs compromise AI behavior in two distinct ways: single-turn instruction hijacking (prompt injections) and persona modulation (jailbreaks). AIQualityHQ provides dedicated scanners for both threat vectors: - **Prompt Injection Vectors:** Malicious user input or tainted RAG documents overriding system constraints or extracting sensitive system instructions. - **Behavioral Jailbreaks:** DAN personas, hypothetical scenarios, and multi-turn roleplay tactics designed to bypass safety filters. ### Prompt Injection Scanner Evaluates prompts against 15 deterministic detection modules. Scans for direct override syntax, typoglycemia, and exfiltration probes. [Open Injection Scanner →](https://aiqualityhq.com/prompt-injection-scanner) ### AI Jailbreak Detector Tests guardrail resilience against adversarial persona modulation, developer mode exploits, and simulated framing attacks. [Open Jailbreak Detector →](https://aiqualityhq.com/jailbreak-detector) ## Deterministic. Reproducible. Private. By Design. Most prompt evaluation tools are just another LLM call — spending tokens to grade tokens with non-reproducible outputs. AIQualityHQ provides a deterministic static analysis and BPE token engine running directly in your browser. | Capability | AIQualityHQ Local Engine | Asking an LLM to Grade | Generic Cloud Tools | |---|---|---|---| | Deterministic & reproducible scores | ✓ | ✗ varies run to run | ✗ LLM-backed | | Local browser execution (prompt text remains on device) | ✓ | ✗ sent to provider | ✗ cloud processing | | Instant evaluation (< 10ms, zero rate limits) | ✓ | ✗ seconds + queues | ✗ network latency | | Free browser tools without required API keys | ✓ | ✗ API token cost | ✗ subscriptions | | Exact subword BPE token & context analytics | ✓ | ✗ rough estimate | ± basic counter | | Prompt injection & jailbreak security red-teaming | ✓ | ✗ | — rarely included | Local analysis tools (Prompt Linter, Token Analyzer, Context Calculator, Injection Scanner) execute fully locally. AI-assisted features (Prompt Rewriter, AI Audit) connect to external providers and are clearly demarcated. ## Six Core Dimensions of Prompt Quality Every prompt evaluated in AIQualityHQ is graded against six deterministic dimensions to isolate vulnerabilities and ensure output precision. ### 1. Prompt Structure Validates role assignments, task demarcation, formatting delimiters, explicit output specifications, and structural clarity. [Read Structure Guide →](https://aiqualityhq.com/ai-prompt-structure-better-responses) ### 2. Memory & State Evaluates how effectively conversational history and variable context are referenced without overloading token windows. [Calculate Context Headroom →](https://aiqualityhq.com/ai-context-window-calculator) ### 3. Context Grounding Measures document retrieval parameters, citation requirements, and grounding constraints to prevent out-of-bounds answers. [Read Grounding Guide →](https://aiqualityhq.com/context-grounding-ai-prompts-accuracy) ### 4. Trust & Accuracy Flags ambiguous instructions, unverified assumptions, and factual grounding gaps that contribute to hallucinated LLM responses. [Read Trust & Accuracy Guide →](https://aiqualityhq.com/ai-tools-trustworthy-secure-prompts) ### 5. PII & Privacy Detects exposed email addresses, API tokens, phone numbers, and raw user variables requiring sanitization. [View Privacy Policy →](https://aiqualityhq.com/privacy) ### 6. Security & Safety Scans for prompt injection attacks, DAN jailbreak overrides, missing system instruction locks, and output boundary violations. [Read Security Mitigation Guide →](https://aiqualityhq.com/ai-prompt-security-risks-mitigation) ## CHECK → SECURE → FIX → OPTIMIZE → TEST Deterministic prompt quality checks, adversarial security red-teaming, and token intelligence engineered for pre-production CI/CD pipelines. ### Prompt Linting Validate constraints and schema contracts against 38 deterministic rules. [Open Linter →](https://aiqualityhq.com/checker) ### Security Scan Red-team prompt text and RAG context against 15 injection and jailbreak vectors. [Open Scanner →](https://aiqualityhq.com/prompt-injection-scanner) ### Refactor Prompt Remediate linter violations and compile model-specific candidate instructions. [Open Rewriter →](https://aiqualityhq.com/prompt-rewriter) ### Token Efficiency Eliminate conversational bloat and benchmark cross-model tokenizer costs. [Analyze Tokens →](https://aiqualityhq.com/ai-token-analyzer) ### Automate & Eval Export Promptfoo CLI configurations for automated multi-model regression runs. [View Docs →](https://aiqualityhq.com/docs) ## Engineering Guides & AI Quality Research In-depth technical architecture blueprints, testing methodologies, and governance frameworks from the AIQualityHQ engineering team. [Explore all 26 AI engineering guides](https://aiqualityhq.com/articles) ### [Modern AI Software Testing Framework](https://aiqualityhq.com/artificial-intelligence-testing) The authoritative 3,100-word cornerstone guide exploring the two dimensions of modern AI QA and the four pillars of AI-driven test automation. [Read Full Guide](https://aiqualityhq.com/artificial-intelligence-testing) ### [Autonomous AI Agent Governance](https://aiqualityhq.com/defense-in-depth-autonomous-ai-agents-governance) Defense-in-depth security architectures, human-in-the-loop gates, and deterministic execution boundaries for enterprise autonomous agents. [Read Governance Blueprint](https://aiqualityhq.com/defense-in-depth-autonomous-ai-agents-governance) ### [Generative Engine Optimization (GEO)](https://aiqualityhq.com/generative-engine-optimization-ai-visibility) How to configure `llms.txt`, structure entity documentation, and maximize brand citations in ChatGPT, Perplexity, and Claude. [Read Visibility Guide](https://aiqualityhq.com/generative-engine-optimization-ai-visibility) ### [Wysera — AI Assistant for Client Follow-Up](https://wysera.ai/) One AI assistant that posts to your social, answers your leads, and follows up with clients. You approve before anything ships. Built for solo med spas, CPAs, and insurance agents. ## Frequently Asked Questions Common questions about AIQualityHQ. Visit our [full FAQ page](https://aiqualityhq.com/faq) for detailed technical guides. ## What tools are included in the AIQualityHQ suite? ## Is my prompt data private and secure? ## How is AIQualityHQ different from asking an LLM to review my prompt? ## Can AIQualityHQ protect against prompt injection and jailbreaks? ## How does the Token Intelligence suite help reduce API costs? ## Ready to harden your AI prompts? Catch prompt quality problems, red-team injection vectors, and eliminate token bloat with deterministic QA and security guardrails. Browser-native tools run locally in your browser — no mandatory signup or API key required. AI-powered tools are clearly labeled.