eaa accessibility auditor
WCAG 2.1 AA auditor with auto-fixing and Dutch accessibility statement generation for EAA compliance.
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WCAG 2.1 AA auditor with auto-fixing and Dutch accessibility statement generation for EAA compliance.
Ship production-grade, block-first WordPress themes and plugins with CI/CD and accessibility baked in.
A professional security-ops audit and hardening suite for WordPress installs, including incident recovery runbooks.
A senior WordPress security auditor that reasons about WP-API taint flow — not regex hits — to find the 8 real plugin/theme vulnerability classes a generic scanner misses, and returns scored findings with ready-to-merge before→after patches.
Plans safe dependency upgrades and framework migrations for legacy applications with risk analysis, compatibility mapping, test planning, rollback strategy, and phased AI coding prompts.
Senior-level Python code auditor for PEP 8, type safety, security vulnerabilities, and 3.10+ modernizations.
Architect designed system degradation and choreographed failure sequences to prevent chaotic breakdowns.
Find the LLM integration code that will not survive a provider being pulled or going down. Flags single-provider lock-in with no alternative, calls with no failover branch, missing timeouts, retries with no limit or backoff, no degraded-mode default, and hardcoded endpoints with no alternate. This is about the model going away, not the model declining.
Inventory every LLM model and provider your code depends on, the AI bill of materials, and flag the dependency risk. It lists each provider, model, and where it's used, then flags hardcoded model ids, single-provider dependency with no alternative, the same model referenced by different ids, model ids with no config or env indirection, and providers pinned in your manifests. Recognizes OpenAI, Anthropic, Google Gemini, and more from an editable list.
Architect production-grade multi-agent systems with explicit contracts, idempotency, and self-healing reliability.
Detect and analyze flaky tests across multiple frameworks with automated repeated execution and severity reporting.
Design and write the eval suite for your LLM-powered feature — the metrics that match your failure modes, a golden dataset plan with starter cases, anchored rubrics, LLM-as-judge prompts with the known bias mitigations, and pass/fail gates wired for CI.
Build production-grade React Native apps with native primitives, optimized lists, and type-safe navigation.
Rewrite dense legal, medical, technical, policy, or financial text into clear plain language at a target reading level — with the meaning fully preserved. A professional plain-language rewrite, not a summary or a list of flags.
Turns your agent into a psychometrician that builds, validates, and troubleshoots measures — reliability, validity, factor analysis, IRT, and measurement invariance.
Turn timeline fragments, Slack logs, and pager history into a complete blameless postmortem — impact summary, clean timeline, contributing factors instead of a scapegoat, action items with owners and due dates, and lessons that survive the week.
Find the code that quietly runs up your LLM bill: uncapped generations, model calls looping with no limit, an expensive model doing a trivial job, and whole files stuffed into prompts. Read-only, every finding explained in plain English.
Teaches AI coding agents to implement production-grade structured logging, error handling, and observability from the first line of code. For developers deploying agent-built services who are tired of debugging black boxes.
Generate accessible, token-ready form components with full validation and interactive states.
Generate accessible, token-aware CSS animations and stateful UI micro-interactions for any frontend.
Scan your OpenClaw config for the settings that quietly hand your agent too much power: unrestricted exec, open inbound DMs, secrets committed in config, the deny-write bypass, sandbox turned off, dangerous Docker binds, and elevated tools. Read-only, plain-English findings, grounded in the OpenClaw docs.
Red-team your own AI agent for prompt-injection and tool-misuse vulnerabilities before it ships — then fix them. Maps your attack surface, generates a defensive test plan with the safe behavior expected for each case, and gives a prioritized mitigations list. Defensive use only.
Router + 10 specialists — SQL, metrics, dashboards, experiments — with a no-fabricated-numbers analysis gate.
Map your real cloud dependency tree — data plane and control plane — find the single points of failure ranked by blast radius, catch the classic traps like monitoring that dies with the region it watches, and get a prioritized resilience backlog. The audit every outage post-mortem says to do first.