The future of AI coding: where agents, skills, and SKILL.md go next

In 2024, AI coding meant pasting code into ChatGPT and copying back the result. In 2025, agents like Claude Code and Cursor closed the loop — the AI ran in your terminal, edited your files, and ran your tests. In 2026, skills (SKILL.md) made those agents portable: you could carry your team's standards from project to project as installable artifacts. So what's next? Here's what I'd bet on for the next 18 months. 1. Skills become the unit of AI knowledge work ------------------------------------------------ Today, when you hire a senior engineer, you onboard them with docs, pair sessions, and code review until they internalise your team's patterns. That investment evaporates when they leave. Skills make that knowledge transferable. The ramp-up artefact for "frontend at our company" becomes a SKILL.md file that any engineer (or any agent) can install. New hires productive in days, not months. The implication: companies will increasingly hire for "skill-writing skill" — the ability to encode your team's hard-won patterns into reusable SKILL.md files — alongside traditional engineering skill. 2. Agents will run as background processes, not foreground assistants ----------------------------------------------------------------------- Claude Code today is "I type, it responds". The next phase is "it watches, it suggests". Background agents that: - Detect when you're stuck (no commits in 20 minutes, lots of red diagnostics) and offer help - Flag a regression risk on the PR you're about to push, before you push - Write documentation for the function you just wrote, in your style - Notice when a teammate's PR pattern matches a known anti-pattern in your codebase The model already has the capability. What's missing is the ergonomics — the difference between "agent runs in a tab" and "agent is part of the IDE in a way that respects flow state". 3. Tools become first-class, prompts become second-class --------------------------------------------------------- Tool use (function calling) is the most underrated capability shift of 2025-2026. Two years ago, "AI for X" meant prompt engineering for X. Today, "AI for X" means giving Claude tools that operate on X — query a DB, run a test, hit an API — and letting it compose them. The implication: prompt engineering as a discipline shrinks. Tool design — what tools you expose, what their schemas look like, what they return — grows in importance. Skills that wrap tools (and not just prompts) will increasingly dominate the marketplace. 4. The coding agent ecosystem consolidates -------------------------------------------- There were 30+ "AI coding tools" in 2025. By end of 2026, the serious ones are: Claude Code, Cursor, Windsurf (now Codeium AI), GitHub Copilot Workspaces, and Replit Agent. The rest get acquired or pivot. The differentiator across the survivors will be **skill ecosystem size**. Cursor's Pro plan today bundles a lot of functionality. ClaudeSkill's open SKILL.md ecosystem (now over 2,800 skills) is on track to dwarf it by 2027 simply because the standard is open and the long tail is huge. 5. Multi-agent coordination becomes practical ----------------------------------------------- Today, multi-agent setups (agent A reviews agent B's code, agent C runs the tests) are flaky — agents step on each other, miss context, or make contradictory recommendations. By 2027, the orchestration layer matures. Think: - A planning agent that breaks a feature into tasks - N implementation agents (one per file or module) running in parallel - A review agent that checks integration - A test agent that runs and reports This already partially works for narrow tasks (e.g., language-specific refactors). It needs another year of scaffolding before it works for arbitrary features. 6. Evals become standard infrastructure ----------------------------------------- The biggest gap between "AI demo" and "AI product" today is evals. Most teams ship without them. By 2027, evals become as standard as unit tests: - Every prompt change runs a regression suite before merging - Every model upgrade ships with an eval delta - Skills include built-in evals so you can verify the rules actually produce the claimed outputs ClaudeSkill is already piloting eval-rich skills with measurable pass rates. This will become the norm. 7. Verticalised agents win in narrow domains ---------------------------------------------- General-purpose agents (Claude Code, Cursor) will dominate generic software work. But verticalised agents — "the SOC 2 compliance agent", "the medical-device firmware agent", "the gaming engine agent" — will own their niches because they ship with deep tool integrations and domain-specific skills. If you're building an AI product in 2026-2027, betting on a vertical with deep workflow integration is more defensible than building a generalised assistant. 8. The "AI engineer" role solidifies -------------------------------------- In 2024, "AI engineer" was a fuzzy title that mostly meant "ML researcher who ships". In 2026, it's converged on a specific shape: - Knows the LLM APIs (Anthropic, OpenAI, etc.) deeply - Can wire up RAG, evals, observability, and async pipelines - Can write good prompts AND good tools - Reads SKILL.md files fluently - Treats prompts and skills as code: versioned, reviewed, tested By 2027, AI engineer is a standard role at every product company, separate from ML engineer (who builds models) and software engineer (who builds the rest). What to do now --------------- If you're an engineer: - Get fluent in skill-writing. Pick one repetitive task in your week and turn it into a SKILL.md. - Build a tool. Pick something you do daily that has a clear schema (CRUD an API) and wire it into Claude as a tool. - Read the SKILL.md spec at claudeskil.com/docs. Submit your first skill — the bar is "useful and works", not "perfect". If you're a founder: - Audit your team's recurring patterns. Anything you've explained 5+ times to new hires deserves a SKILL.md. - Pick one workflow (PR review, customer-interview synthesis, support ticket triage) and ship it as an internal skill. Measure time saved. - Plan for the AI engineer role as a 2026-2027 hire. If you're an investor: - The infrastructure layer for skills + agents is still wide open. Tool registries, skill marketplaces, eval frameworks — all underbuilt. - Verticalised AI products with skill-based moats look much stronger than generic chatbots. The next 18 months will look more like the early App Store than the early web. The standard (SKILL.md) is settling. The runtimes (Claude Code, Cursor) are mature. What's missing is the long tail of high-quality skills that turn a generic agent into a domain expert. That's the work. The marketplace at claudeskil.com is the clearest place I know of to see it happening live — over 2,800 skills, growing weekly. Worth a bookmark even if you only build, never publish.