Agent-Skills

Use when the user asks about Justin Cormack's AI Native DevCon talk on tests, observability, AI-generated behavior, evidence, instrumentation, and keeping AI systems honest when test signals are incomplete.

Use when the user asks about Patrick Debois's talk "Coding Agents Don't Scale Themselves. Neither Do Your Teams. The Rise of Agent Enablement." — including questions about agent enablement teams, the three pillars (Enablement, Platform, Governance), the Context Development Lifecycle applied to org charts, AI product engineers, agent KPIs like turns-per-task, harnesses and shared context libraries, fixing the system vs. fixing the code, the barrel mental model, continuous learning as the next CI/CD, or how VPs / team leads / platform teams should scale AI coding agents across an org.

Answers questions about Brian Douglas's talk on training AI on your own code. Use when a user asks about Brian Douglas's pipeline for capturing agent sessions, extracting skills from traces, fine-tuning small local models, tapes/steros tooling, SFT vs DPO decisions, or wants to apply his agent telemetry and training data approach to their own work with Claude Code, QLoRA, or parallel agents.

Answers questions about, applies frameworks from, and generates artifacts based on Tammuz Dubnov's talk "When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption." Grounds every response in the bundled transcript and outline files. Use when a user asks about AI-native org design, merge rate measurement, non-technical contributors shipping PRs, harness engineering, zero-dev-touch rate, AI adoption ROI, or wants to audit their team against Tammuz's framework, apply his concepts to their situation, draft a measurement dashboard or checklist, or understand terms like Calamarous Coding and the PM-to-engineer authority collapse.

Summarizes, explains, and answers questions about Dave Farley's talk 'Vibe Coding — Is this really the best we can do?', including key arguments, frameworks, and recommendations. Provides verbatim-cited explanations of Farley's three properties of programming languages, audits AI-coding setups against his three-problems framework, drafts BDD-style executable specifications, and outlines his continuous-delivery approach to AI-driven development. Use when the user asks about Dave Farley's talk on vibe coding, agentic programming, AI-generated tests, BDD-style executable specifications, problem-specific DSLs, why natural language is insufficient as a programming language, the three properties of programming languages (formal grammar / unambiguous intent / deterministic execution), the three problems AI programming creates (precise specification, verification, incrementalism), fifth-generation programming, AI as compiler, or applying Farley's continuous-delivery approach to AI coding agents.

Use when the user asks about Maximiliano Firtman's ("Maxi") AI Native DevCon talk on Web MCP and the agentic web — including questions about how Web MCP differs from MCP, exposing frontend tools to AI agents, the imperative vs declarative Web MCP APIs, tool design guidance (name/description/input schema/execute), the Chrome 149 origin trial, using Chrome DevTools MCP or Puppeteer as a bridge today, the Doom demo, or applying Firtman's "pick one high-value page state" adoption approach to current work.

Assists with questions about Hannah Foxwell's talk 'The Reinvention of the Dev Team'. Use when a user asks about Foxwell's arguments on agentic software development, engineering team composition, AI-driven velocity, dev-to-PM ratios, the three anchors (build something worth building, speed requires safety, people matter), the Keep/Trash/Try inventory, on-call sustainability, broken-comb skills, or wants to audit their own team against Foxwell's framework.

Explains Kevin Groetzinger's Skills Everywhere talk and helps teams operationalize reusable skills: trigger design, ownership, discoverability, maintenance, quality review, and adoption loops. Use when the user asks about skill design, skill rollout, skill sprawl, shared agent instructions, or making skills reliable across a team.

Use when the user asks about Daniel Jones (Deejay) and Tomasz's talk "More software, faster — Odevo's AI Native transformation" — including questions about how Odevo (Sweden's third-largest private tech company, residential property management) rolled out agentic coding to its developers, the discovery → workshops → pilot → training → train-the-trainer playbook, prerequisites for adopting agentic coding (CI/CD, platform, tests, coding standards), liberating structures and TRIZ workshop techniques, context window management, the 94% AI adoption metric, the 8-years-to-3-weeks platform rewrite, shifting bottlenecks to product, the "everyone a builder" vision, or applying re-cinq's AI-native transformation approach to their own organisation.

Assists with questions about a practitioner panel talk titled 'From Pipelines to Prompts: Surviving the Shift to AI' featuring Stephane Jourdan, Simon (Saxo Bank), and Samantha. Use when a user asks about what panelists said, argued, or disagreed on regarding AI-native transformation, harness engineering, observability, developer cognitive load, feedback loops, reflector agents, or co-driving vs. self-driving analogies. Answers factual questions with verbatim transcript quotes, applies panelist frameworks to user situations, surfaces relevant panel insights during related discussions, and explains concepts like harness engineering, self-learning production agents, and explainability tooling.

Answers questions about, summarises key insights from, and applies the security guidance of Joseph Katsioloudes's talk 'Code Security Reinvented: Navigating the era of AI'. Use when the user asks about AI-assisted secure coding, MCP servers, skills, agentic workflows, the 1-to-100 security-to-developer gap, start left vs shift left, task flows, LLM-as-judge, supply-chain decisions, AI-assisted fuzzing, hallucinations and non-determinism in AI security review, GitHub Security Lab resources, or applying the talk's security framework to AI-assisted development.

Use when the user asks about Dave Kerr's AI Native DevCon talk on bipolar disorder, dysregulation, and AI, including responsible interpretation of personal and clinical themes from the transcript.

Use when the user asks about Amit Kushwaha's AI Native DevCon talk on benchmarking agent-era systems, measuring performance beyond single LLM calls, inference, workflow complexity, tool use, and real-world workloads.

Answers questions about Lamis's (Anthropic) AI Native DevCon talk on context engineering, agent memory systems, and dreaming — an asynchronous, out-of-band memory-curation process. Supports factual Q&A, framework application, system auditing, artifact drafting, and concept explanation based on the talk's content. Use when the user asks about context engineering, CLAUDE.md files, agent memory persistence, skills, multi-session memory, the dreaming process, hashing-based concurrency, or wants to apply, audit against, or draft artifacts from the frameworks described in this talk.

Use when the user asks about Dana Lawson's talk "Built for Humans. Now Agents Are Here." (Netlify CTO, 2026) — including questions about Agent Experience (AX), the AX paradox, redesigning CLIs/build logs/deploy previews for agents, moving from APIs to capabilities, event-driven agent architectures, blueprints (skills/recipes/context/ADRs), software factories, autonomous development loops, sandbox + human-in-loop + audit/rollback trust principles, the expanded "builder persona," or applying Netlify's AX approach to the user's own platform.

Use when the user asks about Ryan Lopopolo's AI Native DevCon talk on harness engineering, steering coding agents with goals, constraints, context, tool scope, eval loops, and verification systems.

Explains, summarizes, and turns Matthias Luebken's talk on embedding Pi-style coding agents into safe product-design artifacts: tool-contract sketches, guardrail checklists, session-record models, and malleable-software review plans. Use when the user asks about OpenClaw, Pi-style product agents, lifecycle guardrails, agent sessions, or design-level application of these primitives.

Provides detailed answers, conceptual explanations, workflow guidance, and framework-based analysis about Edouard Maleix's talk "How AI-First Dev Teams Build Collective Intelligence — One Attributed Mistake at a Time." Use when the user asks about giving coding agents their own identity and signed commits, the diary/entry/pack/render workflow, turning agent mistakes into reusable team knowledge, evaluating knowledge packs for fidelity and usefulness, voluntary task picking by autonomous agents, the MoltNet open-source project, compound engineering, or applying his approach to make agent lessons compound across a team instead of evaporating into chat history.

Summarizes Simon Maple's AI Native DevCon welcome and explains the conference framing: context window, latent space, tool pool, hallway track, attendee goals, and practical learning themes. Use when the user asks about the event opening, conference themes, AI-native development framing, or how to orient work around the talks.

Use when the user asks about Luke Marsden's talk "Giving Every Agent Its Own Desktop: Lessons from Dogfooding HelixML" — including questions about HelixML, giving each agent its own GPU-accelerated desktop, spec-driven development with plan/implement phases, scaling agents by task vs by org-shape, centralized vs per-developer agent infrastructure, forking Zed for remote control, ZFS-cloned Docker-in-Docker dev environments, mixing local models (Llama 3.1) with frontier models (Claude Opus), the "snake eating its own tail" dogfooding approach, self-improving companies, or applying his design opinions to your own agent platform.