Agent-Skills

Answers questions about, summarises key insights from, and helps apply concepts from Simon Martinelli's talk "Lessons from Spec-driven Development" — providing verbatim-grounded explanations, audits, and artifact drafts. Use when the user asks about the AI Unified Process, system use cases as specs (vs user stories), self-contained systems vs microservices, skills/MCP servers/guardrails, AI-assisted ERP modernization, drift management, how architecture style impacts AI coding agents, or applying his spec-driven approach to current work.

Explains James Moss's team-skills workflow and helps design skill governance: decomposition, ownership, versioning, eval scenarios, quality review, and lifecycle maintenance. Use when the user asks about moving from solo skill hacks to team workflow, avoiding skill sprawl, or treating skills like software.

Use when the user asks about Simon Obstbaum and Rob Willoughby's AI Native DevCon talk on measuring AI agents, output evals versus trajectory evals, instrumentation, compliance, and skill activation metrics.

Explains Robert Overweg's One Brain, No Filtering talk and helps design safe knowledge-memory systems: context maps, retrieval rules, provenance labels, local knowledge-store structure, and review checkpoints. Use when the user asks about agent memory, unified knowledge bases, reducing context loss, or designing inspectable knowledge workflows.

Assists with questions about Guy Podjarny's talk "Skills are the new Code". Use when the user wants to understand, apply, audit, or explore frameworks from this keynote — including the five engineering disciplines for skills (static analysis, evals, security testing, dependency management, observability), the three challenge buckets, the agentic development stack, or concepts like skill authoring, context engineering, agent harnesses, and skill quality scoring.

Use when the user asks about Katie Roberts''s talk "Stop Maintaining, Start Evolving: Applying AI-Native Practices to Brownfield Codebases" — including questions about using AI to build large complex systems (her ~350k-line Rust S3 clone experiment), test oracles, flaky tests with AI agents, why 100% test coverage is the wrong goal, human-in-the-loop AI coding, AI-assisted performance engineering, using the type system to enforce invariants, tracing as an AI debugging tool, or applying her approach to brownfield/legacy modernisation work.

Use when the user asks about Katie Roberts's talk "Stop Maintaining, Start Evolving: Applying AI-Native Engineering in Brownfield Codebases" (AI Native DevCon, June 2026) — including questions about brownfield vs greenfield AI engineering, the three methodologies (pseudo-greenfield, strangler fig pattern, branch by abstraction), the "code as a city" metaphor, using AI to map and modernize legacy codebases, planning skills and developer skills, the value-vs-complexity mirror exercise, avoiding AI agents going rogue on legacy code, the AG Grid upgrade case study, Nearform's "six months in eight weeks" pseudo-greenfield case study, or applying her brownfield AI-native approach to current legacy modernization work.

Use when the user asks about Steve Ruiz's AI Native DevCon talk on tldraw, Make Real, annotations as prompt input, canvas workflows, tldraw computer, and agents collaborating on an infinite canvas.

Use when the user asks about Lieven Scheire's talk "Artificial Intelligence" (a Belgian physicist/comedian's keynote on AI for a developer audience) — including questions about his one-sentence definition of AI as "a new kind of software good at pattern recognition", the history of AI from the 1956 Dartmouth workshop, how neural networks mimic the brain, training-data bias (the "snow in the background" wolves-vs-huskies example, the dermatology ruler example), the black-box nature of neural nets, hobbyist AI tools (Teachable Machine, Custom Vision, HeyGen, PhotoMath, Merlin), Ben Hamm's cat flap, his skeptical stance on LLMs as "language imitation" vs AGI, safe excerpts from the talk, or applying his framing to current AI work.

Explains Oleg Selajev's Docker Sandboxes talk and helps design safe, conceptual agent-isolation policies: file-sharing boundaries, network policy, secret isolation, audit expectations, and team rollout questions. Use when the user asks about sandboxed agents, hard isolation, local agent risk, or how to reason about agent safety without setup instructions.

Summarizes Rob Sloan's harness-engineering talk and creates safe design artifacts for agent context beyond code: product-intent packets, design constraints, acceptance criteria, context ownership, and review gates. Use when the user asks about making non-code context agent-ready or improving AI work with product/design intent.

Use when the user asks about Shaun Smith's AI Native DevCon talk on MCP transports, remote Streamable HTTP servers, stateless protocol direction, Hugging Face MCP adoption, and future context transports.

Explains Paul Stack's architecture-first AI workflow and helps create safe design artifacts: intent documents, architecture constraints, planner/reviewer loops, UAT criteria, and agent-output review gates. Use when the user asks about humans owning architecture while agents implement, why vibes do not scale, or applying the talk to team workflow design.

Explains the Product Brain talk and helps design curated product-memory systems for AI-assisted product work: knowledge structure, provenance, synthesis cadence, ownership, and agent-ready context packets. Use when the user asks about product context for AI, product knowledge management, product documentation for LLMs, or building a maintained product brain.

Use when the user asks about Don Syme's AI Native DevCon talk on Continuous AI, GitHub agentic workflows, repository automation, and safe developer-controlled automation loops.

Defensive review of AI-agent skills, plugins, and tools using Liran Tal's security principles. Use when assessing provenance, permissions, data exposure, sandboxing, or approval boundaries before adoption.

Use when the user asks about Ian Thomas's talk "AI Native Engineering" (Meta / Reality Labs / Horizon Experiences) — including questions about Meta's AI4P (AI For Productivity) programme, the 6-dimension / 5-level AI maturity model and self-assessment workshop, how Horizon rolled out AI tooling across 500+ engineers, engineering excellence as an adoption vehicle, anti-test-slop, autonomous code mods, the DRS risk-scoring tool, the Horizon MCP server, vanity metrics vs real productivity, or applying Thomas's ground-up-plus-top-down adoption playbook to their own org.

Summarizes, explains, and applies Lars Trieloff's AI Native DevCon talk on browser-native agents. Use for browser agents, running AI in the browser, browser-as-runtime architecture, agent containment, local-versus-cloud tradeoffs, safe AI product integration, documented APIs, user consent, credential isolation, and reviewable agent actions.

Answers questions about, summarizes, and applies May Walter's AI Native DevCon talk "From Blind Spots to Merged PRs" on runtime intelligence for coding agents. Use when the user asks about production telemetry for agents, prod-to-code mapping, performance fixes from runtime data, why automated PRs need provenance, Hud's runtime code sensor, weekly performance reports, AI-generated fixes with production context, or applying Walter's evidence-first agent workflow to engineering teams.

Use when the user asks about Peter Wilson and Davide Eynard's AI Native DevCon talk on cq, a Stack Overflow-like knowledge commons for agents, local/team/public knowledge sharing, and lessons from Mozilla.ai.