Claude Skills by jscraik

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 tech...

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 paneli...

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...

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 t...

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 applyin...

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 age...

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),...

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 app...

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 ...

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, avoi...

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 ne...

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 ...

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.

Explains Jack Wotherspoon's Humans vs Slop talk and helps create quality gates for AI-heavy software work: review-cost analysis, slop detection heuristics, durable-value metrics, and human-judgment checkpoints. Use when the user asks about AI-generated maintenance burden, review economics, or preserving taste in agentic development.

Explore Harness Engineering options, filter ornate or weak ideas, recover dropped leverage, and select survivor routes before commitment. Use when intent, stage choice, tradeoffs, idea quality, or possible solution shapes are still unsettled before spec, plan, Linear, or implementation work.

Review Harness Engineering diffs, PRs, commits, and readiness claims for introduced risk. Use when correctness, validation proof, security posture, traceability, closure safety, or review-thread resolution must be assessed before merge or handoff.

Debug and repair validated Harness Engineering defects with bounded scope, reproduction evidence, root-cause notes, regression protection, and validation proof. Use when a bug is already evidenced and the fix should not expand into broad improvement work.

Improve existing Harness Engineering skills, references, contracts, and evals from concrete evidence such as failed evals, repeated review findings, usage traces, or documented regressions. Use when a bounded hardening pass is required; do not use for speculative redesign.

Create bounded Harness Engineering execution plans from approved specs or issue slices. Use when work needs ordered implementation units, explicit scope boundaries, rollback posture, traceability, and validation gates before code changes.

Create bounded, evidence-backed Harness Engineering specs from approved intent. Use when a selected issue, milestone, reframe phase, or execution slice needs acceptance criteria, traceability, risk gates, and validation boundaries before planning or implementation.

Executes bounded implementation work from approved specs, plans, issues, or small fixes by editing files, running validation, preserving unrelated work, and recording rollback and handoff evidence. Use when asked to implement, execute a plan, apply changes, or build a scoped feature.

Scaffold Codex plugin packages with deterministic manifests, marketplace metadata, and traceability or evidence contracts for non-trivial adoption. Use when creating plugin roots or adopting existing skills into plugin ownership.

Analyze broad, mixed, or unclear Plugin Factory follow-up requests and select the correct plugin lane. Use when plugin intent lacks a clear lane owner.

{{What it does}} Use when {{primary trigger conditions}}. Don't use when {{common near-miss / out-of-scope cases}}. Outputs: {{artifact paths + formats}}. Success: {{what 'done' means}}.

<objective> Build a comprehensive execution skill that does real work in a specific domain. Domain expertise skills are full-featured build skills with exhaustive domain knowledge in references, complete workflows for the full lifecycle (build → debug → optimize → ship), and can be both invoked directly by users AND loaded by other skills (like create-plans) for domain knowledge. </objective> <critical_distinction> **Regular skill:** "Do one specific task" **Domain expertise skill:** "Do EVER...

<required_reading> **Read these reference files NOW:** 1. Infrastructure/references/recommended-structure.md 2. Infrastructure/references/skill-structure.md 3. Infrastructure/references/core-principles.md 4. Infrastructure/references/cso-description-writing.md (frontmatter description quality) </required_reading> <process>

<required_reading> **Read these reference files NOW:** 1. Infrastructure/references/skill-structure.md </required_reading> <purpose> Audit checks structure. **Verify checks truth.** Skills contain claims about external things: APIs, CLI tools, frameworks, services. These change over time. This workflow checks if a skill's content is still accurate. </purpose> <process>

Analyzes bounded skill evidence, classifies root causes, and recommends a lifecycle lane such as keep, observe, improve through Skill Factory hardening, merge with approval, or retire with approval. Use when a skill is not working, a skill is not triggering correctly, evals or Tessl disagree, repeated failures need debugging, or skill performance issues need evidence-backed repair handoff items.

Creates release-ready skill packages from proven workflows by drafting SKILL.md frontmatter, trigger-rich instructions, references/contract.yaml, eval scenarios, task profile, and validation commands. Use when the user asks to create a skill, skillify a workflow, package a process, write SKILL.md, make a reusable skill template, or prepare a skill for release checks.

Analyze options and trade-offs before traceable work is ready, without pretending exploration is a plan. Use when the user wants options, alternatives, decision matrices, or a bounded ideation pass before trace planning.