Bootstraps and uses the canonical LoadComponent host service for Module Federation (src/core/api/services/module-federation/moduleFederationService.tsx). Use whenever the user mentions Module Federation, remoteEntry, LoadComponent, createRemoteComponent, remotes API, bridge components, host shell, dynamic or static remotes, or scaffolding a new host — even if the service file does not exist yet.

Review Java stream performance advice, especially slow stream mappings, external collection mutation with forEach/add, and whether parallelStream is safe; clean up mutation and write or refactor Java Stream and Collector code. Avoid common stream antipatterns such as materializing just to inspect, sorting before min/max, counting for existence, nested stream collections, unsafe null sorting, multi-line lambdas, and careless findFirst/findAny changes. Use whenever writing, reviewing, or refactoring Java code that uses Java streams, collectors, stream pipelines, grouping, joining strings, first/any element lookup, sorting, limiting, distinct values, primitive totals, Optional values in streams, or parallel streams, including review prompts asking whether a lookup should use findFirst or findAny.

Write, review, and refactor Java Optional code using best practices, improving readability, and preventing common Optional antipatterns such as null-style control flow and readability regressions. Use whenever writing, reviewing, or refactoring Java code that introduces, changes, or reasons about Optional; handles absent, missing, nullable, fallback, or default values where Optional may be appropriate; or touches isPresent/isEmpty, get/orElseThrow, orElse(null), optional.stream(), findFirst/findAny, checked exceptions inside Optional chains, or nullable control flow.

Use when a professional article task needs evidence-bound editorial control: source packet, claim ledger, verification queue, publication notes, anti-slop revision, article audit, or series plan. Trigger on write, rewrite, revise, humanize, outline, audit, or plan for an article, essay, blog post, longform, thought leadership, or source-backed copy. Do not use for generic copy, short posts, email, docs, or fiction. Topic-only => Editorial Brief plus source plan only, no article body. Source packet => article plus Publication Notes. Draft rewrite/revision => first output # Revised Article, then rewritten article body, then Change Notes.

Use when the user wants to design, size, audit, or choose a self-hosted speech recognition or streaming ASR stack, including Whisper, Parakeet, Canary, Riva, NIM, Triton ASR, faster-whisper, sherpa-onnx, voice-agent transcription, Romanian or Moldovan ASR, contact-center transcription, GPU sizing, latency budgets, multilingual routing, VAD, diarization, or production evaluation.

Use when the user wants to review, audit, or check safety for an AI memory system, agent learning pipeline, prompt-tuning workflow, skill builder, trace-mining tool, or eval/feedback loop. Produces an evidence-led audit report with learning-loop map, evidence inventory, maturity scorecard, severity-ranked findings, privacy/provenance gaps, counterfactual/eval coverage, and Stabilize/Standardize/Scale roadmap.

Use when the user wants a landing page audit, CRO review, marketing conversion review, copy audit, funnel alignment check, brand positioning critique, technical performance marketing audit, or prioritized issue report for a landing page, pricing page, signup page, lead magnet, or product page. Runs evidence-first context gathering, parallel specialist analysis, synthesis, and severity-ranked recommendations.

Use when the user wants a test suite audit, test quality or reliability review, regression-protection review, unit/integration/e2e test review, coverage or CI signal assessment, flaky CI investigation, fixture-realism review, spec-drift review, or generated-test validation for AI/LLM/agent-written code. Produces severity-ranked findings for weak assertions, oracle gaps, brittle fixtures, over-mocking, CI trust, and generated-code test risks.

Write or audit AI agent system prompts component-by-component across identity, instruction architecture, behavioral constraints, tools, examples, context strategy, output format, and error handling. Use when the user wants to design a new agent prompt, write a system prompt, review an existing agent prompt, fix tool-use instructions, audit prompt structure, improve context strategy, tune output formats, or define error handling for single-agent or multi-agent systems.

Design, build, or audit professional UI design systems across strategy, product language, foundations, tokens, components, patterns, accessibility, content, Figma/code libraries, documentation, QA, governance, adoption, measurement, theming, releases, and migration. Use when the user wants to create a design-system blueprint, review an existing design system, fix design-system drift, plan Figma/code parity, define token or component architecture, evaluate accessibility and governance maturity, or sequence design-system adoption and migration work.

Run an evidence-grounded software architecture audit workflow that builds a repo brief, selects single-auditor or specialist-panel mode, inspects boundary, layering, dependency, composition, cohesion, and testability risks, writes required finding blocks, and sequences incremental refactors. Use when asked for an architecture audit, architecture review, repo-structure review, software architecture report, audit_report.md, structural issue findings, or specialist-panel synthesis across multi-module systems.

Use when migrating, restructuring, publishing, or auditing an existing Claude skill into a Tessl tile; converting flat .md files or SKILL.md bundles; fixing Tessl Quality, Impact, Uplift, frontmatter, metadata, tile.json summary, README, markdown reference links, registry-vs-local Quality gaps, artifact anchors, auto-eval wait discipline, or pushing tile scores from 88-99% to 100%.

Design, build, or audit a coding agent, agentic loop, tool-use harness, or autonomous coding system — covering loop architecture, action space, context strategy, observation formatting, evaluation, error handling, prompt engineering, and task decomposition. Use when the user wants to design an agent, build a coding agent, scaffold an agentic system, architect a tool-use loop, review an existing agent harness for improvements, fix context bloat or compaction problems, tune observation formatting or tool output handling, debug agent loop or termination issues, design a system prompt or evaluator prompt for an agent, set up or redesign an agent evaluation pipeline, plan multi-agent orchestration, or specify how an agent should manage context, tools, prompts, evaluation, or recovery (greenfield design or audit mode).