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).

Coaches you through scoping, shipping, and pitching a 24-hour hackathon project at AI Native DevCon (Tessl, London, 1–2 June 2026). Spec-first, track-aware, demo-obsessed. Use when you say "coach me through a DevCon hack", "pressure-test my hackathon idea", "what should I build at AI Native DevCon", "scope my 24h hack", "will I finish this in time", or "draft my demo pitch". Refuses to let you write code before a one-page spec exists.

Define module boundaries, structure infrastructure layers, implement dependency inversion, and create sociable unit tests using MIM (Module-Infrastructure-Module) architecture and modular design principles. Use this skill when the user mentions "MIM", "Module-Infrastructure-Module", "Screaming Architecture", or asks for advice on modular design, high cohesion, low coupling, or sociable unit testing.