{"slug": "show-hn-aiqualityhq-browser-native-prompt-qa-and-security-guardrails", "title": "Show HN: AIQualityHQ – Browser-native prompt QA and security guardrails", "summary": "AIQualityHQ launched as a browser-native suite of eleven applications for prompt quality assurance and security guardrails, running 38 deterministic linting rules and 15 OWASP LLM-01-aligned injection detectors locally with zero network calls. The tool set also includes a prompt rewriter with Git visual diffs, a BPE-based token analyzer covering 30+ models with cross-provider cost tables, and an LLM auditor that validates llms.txt files and generative engine optimization metrics. AIQualityHQ said multi-model testing workflows are still to come as the platform evolves.", "body_md": "# Automated QA & Security Guardrails for Prompts and AI Agents\n\nThis prompt has a strong structure. Ship it to any model.\n\nAll checks passed. Zero structural, privacy, or security issues detected.\n\nDevelopers 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.\n\nBuilt for AI developers, prompt engineers, and AppSec teams — validating output contracts, enforcing role boundaries, red-teaming adversarial inputs, and eliminating token bloat.\n\nNeed to measure token bloat or model headroom?[Analyze AI token usage](https://aiqualityhq.com/ai-token-analyzer) \n\n38 Rules\n\nDeterministic prompt linter\n\n15 Detectors\n\nOWASP LLM-01 security checks\n\n30+ Models\n\nContext & token intelligence registry\n\nBrowser-Native\n\nLocal-first, reproducible & fast\n\nRanked by product-hunting communities for speed, privacy & depth.\n\nAIQualityHQ is independently listed across LaunchTry, Shipstry, Launch List, Verified Tools, IndieHunt, Toolfio and EarlyHunt — no artificially inflated ratings or reviews.\n\n## AI Quality Engineering Tools for Every Stage of the Prompt Lifecycle\n\nFrom initial prompt design to adversarial defense, token optimization, and automated refactoring — purpose-built browser tools that help teams **Check → Secure → Fix → Optimize → Audit**.\n\n### [Prompt Linter & Contract Checker](https://aiqualityhq.com/checker)\n\nEvaluate prompt structure, constraints, clarity, and output schemas against 38 deterministic rules running locally in your browser.\n\n[Open Linter](https://aiqualityhq.com/checker)\n\n### [Prompt Injection Security Scanner](https://aiqualityhq.com/prompt-injection-scanner)\n\nRed-team prompts and RAG contexts against 15 deterministic detector modules covering direct hijacking, exfiltration, and context poisoning.\n\n[Open Scanner](https://aiqualityhq.com/prompt-injection-scanner)\n\n### [Prompt Rewriter & Target Compiler](https://aiqualityhq.com/prompt-rewriter)\n\nAutomated prompt refactoring for detected defects. Resolves constraint conflicts, adds delimiters, and provides side-by-side Git visual diffs.\n\n[Open Rewriter](https://aiqualityhq.com/prompt-rewriter)\n\n### [AI Token Analyzer](https://aiqualityhq.com/ai-token-analyzer)\n\nDecompose prompts into architectural layers (System, User, Context, Tools) with exact subword BPE precision and multi-model cost tables.\n\n[Analyze Tokens](https://aiqualityhq.com/ai-token-analyzer)\n\n## The AIQualityHQ Suite by Product Domain\n\nEleven 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.\n\n### Prompt Quality & Linting\n\nStatic analysis for prompt templates, system instructions, and RAG conditioning. Catches conflicting constraints, role ambiguity, missing output delimiters, and formatting issues before code execution.\n\n[Prompt Linter & Contract Checker →38 deterministic rules evaluating structure, negative constraints, and output schemas](https://aiqualityhq.com/checker)\n\n[Quality Report Viewer →](https://aiqualityhq.com/report)\n\n### Prompt Security & Guardrails\n\nPre-deployment red-teaming and exploit detection. Evaluates inputs against direct prompt overrides, RAG context poisoning, behavioral jailbreaks, and system prompt exfiltration vectors.\n\n### Token & Cost Intelligence\n\nComprehensive 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.\n\n### Prompt Engineering & Synthesis\n\nActive instruction authoring and automated defect fixing. Synthesize production-ready system prompts with role parameters or compile defective drafts into optimized model-specific prompts.\n\n### AI Visibility & LLM Auditing\n\nDiagnose domain discoverability and brand grounding across frontier AI engines. Validate your `llms.txt` file and evaluate generative engine optimization (GEO) metrics.\n\n[AI Prompt Quality & LLM Auditor →Audit domain LLM readiness, llms.txt compliance, and response accuracy (supports BYOK keys)](https://aiqualityhq.com/ai-audit)\n\n[GEO Engineering Guide →](https://aiqualityhq.com/generative-engine-optimization-ai-visibility)\n\n## Eliminate Token Bloat & Optimize Model Context Windows\n\nEvery uncompressed prompt burns context headroom and drives unnecessary API expenditure. Follow the complete token engineering cycle: **Measure → Detect Waste → Compare Tokenizers → Verify Headroom → Optimize**.\n\n### Calculate Your Token & API Cost Savings\n\nSee how much static prompt pre-filtering & token optimization saves your team vs cloud evals.\n\nEstimates 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.\n\n### [Deconstruct BPE Tokens](https://aiqualityhq.com/ai-token-analyzer)\n\nBreak down prompts into system, context, user, and schema layers with subword precision.\n\n[Open Token Analyzer →](https://aiqualityhq.com/ai-token-analyzer)\n\n### [Detect Conversational Fluff](https://aiqualityhq.com/ai-token-waste-analyzer)\n\nAutomatically identify polite filler and redundant formatting that waste token bandwidth.\n\n[Open Waste Analyzer →](https://aiqualityhq.com/ai-token-waste-analyzer)\n\n### [Context Window Headroom](https://aiqualityhq.com/ai-context-window-calculator)\n\nMonitor utilization gauges and avoid \"Lost in the Middle\" attention degradation risks.\n\n[Open Calculator →](https://aiqualityhq.com/ai-context-window-calculator)\n\n### [Cross-Model Tokenizer BPE](https://aiqualityhq.com/ai-tokenizer-comparison)\n\nCompare token efficiency and subword chunking across OpenAI, Anthropic, Google & Meta.\n\n[Open Comparison →](https://aiqualityhq.com/ai-tokenizer-comparison)\n\n## Red-Team Prompts Against Injections & Adversarial Jailbreaks\n\nAdversarial 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:\n\n- **Prompt Injection Vectors:** Malicious user input or tainted RAG documents overriding system constraints or extracting sensitive system instructions.\n- **Behavioral Jailbreaks:** DAN personas, hypothetical scenarios, and multi-turn roleplay tactics designed to bypass safety filters.\n\n### Prompt Injection Scanner\n\nEvaluates prompts against 15 deterministic detection modules. Scans for direct override syntax, typoglycemia, and exfiltration probes.\n\n[Open Injection Scanner →](https://aiqualityhq.com/prompt-injection-scanner)\n\n### AI Jailbreak Detector\n\nTests guardrail resilience against adversarial persona modulation, developer mode exploits, and simulated framing attacks.\n\n[Open Jailbreak Detector →](https://aiqualityhq.com/jailbreak-detector)\n\n## Deterministic. Reproducible. Private. By Design.\n\nMost 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.\n\n| Capability | AIQualityHQ Local Engine | Asking an LLM to Grade | Generic Cloud Tools | \n|---|---|---|---|\n| Deterministic & reproducible scores | ✓ | ✗ varies run to run | ✗ LLM-backed | \n| Local browser execution (prompt text remains on device) | ✓ | ✗ sent to provider | ✗ cloud processing | \n| Instant evaluation (< 10ms, zero rate limits) | ✓ | ✗ seconds + queues | ✗ network latency | \n| Free browser tools without required API keys | ✓ | ✗ API token cost | ✗ subscriptions | \n| Exact subword BPE token & context analytics | ✓ | ✗ rough estimate | ± basic counter | \n| Prompt injection & jailbreak security red-teaming | ✓ | ✗ | — rarely included | \n\nLocal 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.\n\n## Six Core Dimensions of Prompt Quality\n\nEvery prompt evaluated in AIQualityHQ is graded against six deterministic dimensions to isolate vulnerabilities and ensure output precision.\n\n### 1. Prompt Structure\n\nValidates role assignments, task demarcation, formatting delimiters, explicit output specifications, and structural clarity.\n\n[Read Structure Guide →](https://aiqualityhq.com/ai-prompt-structure-better-responses)\n\n### 2. Memory & State\n\nEvaluates how effectively conversational history and variable context are referenced without overloading token windows.\n\n[Calculate Context Headroom →](https://aiqualityhq.com/ai-context-window-calculator)\n\n### 3. Context Grounding\n\nMeasures document retrieval parameters, citation requirements, and grounding constraints to prevent out-of-bounds answers.\n\n[Read Grounding Guide →](https://aiqualityhq.com/context-grounding-ai-prompts-accuracy)\n\n### 4. Trust & Accuracy\n\nFlags ambiguous instructions, unverified assumptions, and factual grounding gaps that contribute to hallucinated LLM responses.\n\n[Read Trust & Accuracy Guide →](https://aiqualityhq.com/ai-tools-trustworthy-secure-prompts)\n\n### 5. PII & Privacy\n\nDetects exposed email addresses, API tokens, phone numbers, and raw user variables requiring sanitization.\n\n[View Privacy Policy →](https://aiqualityhq.com/privacy)\n\n### 6. Security & Safety\n\nScans for prompt injection attacks, DAN jailbreak overrides, missing system instruction locks, and output boundary violations.\n\n[Read Security Mitigation Guide →](https://aiqualityhq.com/ai-prompt-security-risks-mitigation)\n\n## CHECK → SECURE → FIX → OPTIMIZE → TEST\n\nDeterministic prompt quality checks, adversarial security red-teaming, and token intelligence engineered for pre-production CI/CD pipelines.\n\n### Prompt Linting\n\nValidate constraints and schema contracts against 38 deterministic rules.\n\n[Open Linter →](https://aiqualityhq.com/checker)\n\n### Security Scan\n\nRed-team prompt text and RAG context against 15 injection and jailbreak vectors.\n\n[Open Scanner →](https://aiqualityhq.com/prompt-injection-scanner)\n\n### Refactor Prompt\n\nRemediate linter violations and compile model-specific candidate instructions.\n\n[Open Rewriter →](https://aiqualityhq.com/prompt-rewriter)\n\n### Token Efficiency\n\nEliminate conversational bloat and benchmark cross-model tokenizer costs.\n\n[Analyze Tokens →](https://aiqualityhq.com/ai-token-analyzer)\n\n### Automate & Eval\n\nExport Promptfoo CLI configurations for automated multi-model regression runs.\n\n[View Docs →](https://aiqualityhq.com/docs)\n\n## Engineering Guides & AI Quality Research\n\nIn-depth technical architecture blueprints, testing methodologies, and governance frameworks from the AIQualityHQ engineering team.\n\n[Explore all 26 AI engineering guides](https://aiqualityhq.com/articles)\n\n### [Modern AI Software Testing Framework](https://aiqualityhq.com/artificial-intelligence-testing)\n\nThe authoritative 3,100-word cornerstone guide exploring the two dimensions of modern AI QA and the four pillars of AI-driven test automation.\n\n[Read Full Guide](https://aiqualityhq.com/artificial-intelligence-testing)\n\n### [Autonomous AI Agent Governance](https://aiqualityhq.com/defense-in-depth-autonomous-ai-agents-governance)\n\nDefense-in-depth security architectures, human-in-the-loop gates, and deterministic execution boundaries for enterprise autonomous agents.\n\n[Read Governance Blueprint](https://aiqualityhq.com/defense-in-depth-autonomous-ai-agents-governance)\n\n### [Generative Engine Optimization (GEO)](https://aiqualityhq.com/generative-engine-optimization-ai-visibility)\n\nHow to configure `llms.txt`, structure entity documentation, and maximize brand citations in ChatGPT, Perplexity, and Claude.\n\n[Read Visibility Guide](https://aiqualityhq.com/generative-engine-optimization-ai-visibility)\n\n### [Wysera — AI Assistant for Client Follow-Up](https://wysera.ai/)\n\nOne 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.\n\n## Frequently Asked Questions\n\nCommon questions about AIQualityHQ. Visit our [full FAQ page](https://aiqualityhq.com/faq) for detailed technical guides.\n\n## What tools are included in the AIQualityHQ suite?\n\n## Is my prompt data private and secure?\n\n## How is AIQualityHQ different from asking an LLM to review my prompt?\n\n## Can AIQualityHQ protect against prompt injection and jailbreaks?\n\n## How does the Token Intelligence suite help reduce API costs?\n\n## Ready to harden your AI prompts?\n\nCatch prompt quality problems, red-team injection vectors, and eliminate token bloat with deterministic QA and security guardrails.\n\nBrowser-native tools run locally in your browser — no mandatory signup or API key required. AI-powered tools are clearly labeled.", "url": "https://wpnews.pro/news/show-hn-aiqualityhq-browser-native-prompt-qa-and-security-guardrails", "canonical_source": "https://aiqualityhq.com", "published_at": "2026-10-22 08:55:23+00:00", "updated_at": "2026-10-22 09:11:56.581192+00:00", "lang": "en", "topics": ["ai-safety", "ai-tools", "developer-tools", "large-language-models", "generative-engine-optimization"], "entities": ["AIQualityHQ", "OWASP LLM-01", "LaunchTry", "Shipstry", "IndieHunt", "EarlyHunt"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-aiqualityhq-browser-native-prompt-qa-and-security-guardrails", "markdown": "https://wpnews.pro/news/show-hn-aiqualityhq-browser-native-prompt-qa-and-security-guardrails.md", "text": "https://wpnews.pro/news/show-hn-aiqualityhq-browser-native-prompt-qa-and-security-guardrails.txt", "jsonld": "https://wpnews.pro/news/show-hn-aiqualityhq-browser-native-prompt-qa-and-security-guardrails.jsonld"}}