--- title: Skills vs MCP vs Agents: What Each One Does category: guide canonical: https://forgehouse.ai/guides/skills-vs-mcp-vs-agents/ lang: en hreflang_alt: https://forgehouse.ai/tr/rehberler/skill-mcp-agent-farki/ last_updated: 2026-07-04 --- # Skills vs MCP vs Agents: What Each One Does > Skills, MCP, and agents solve three different problems: a skill gives the model method, an MCP connector gives it reach, and an agent owns the outcome. Every week our agency runs the same handful of jobs: a technical SEO audit for one client, schema markup for another, an ad account review, a batch of content briefs, and the monthly report that ties it together. None of the work is mysterious, and that was the frustrating part. We knew the procedures cold, yet every time we handed one to a general-purpose AI we started from an empty prompt and re-explained the whole thing. Getting past that meant getting three ideas straight, skills, MCP, and agents, because they are easy to confuse and each solves a different part of the problem. ## What is the difference between skills, MCP, and agents? The one-line version: a skill gives the model method, an MCP connector gives it reach, and an agent owns the outcome. A skill is packaged judgment for a single job, with the steps and the known pitfalls written down so the model works like a specialist instead of guessing. An MCP connector is the wiring to an external service, the thing that lets the model actually read your analytics or touch your ad account. An agent is an expert worker that runs a whole job end to end, composing skills and connectors, making decisions between steps, and handing back a finished result. Knowledge, reach, and ownership. Most of the confusion clears up the moment you stop treating them as three names for one idea and start asking which of the three a given gap actually needs. ## Why a prompt library was the wrong shape We tried the obvious fix first: a big library of prompts. It did not hold up. A prompt captures the words you happened to type that day, not the procedure underneath them, and it forgets everything the moment the chat ends. Paste the same prompt next week and the model has no memory of the fifty edge cases you learned the hard way. The real value in an agency is not the wording, it is the accumulated judgment: which schema type actually earns a rich result, the order to run an ad check so you do not waste the first hour, the exact way a report has to read before a non-technical owner trusts it. A prompt is a thin snapshot of that. What we wanted was a way to package the judgment itself, and that packaging turned out to have three distinct shapes. ## What is a Claude skill? A skill is a ready-to-run capability that does one job well. In practice it is a folder the model loads when the task matches: plain instructions, plus any small scripts or reference files the procedure needs. It carries the judgment, not just the phrasing, so the model runs the job the way someone who has done it a hundred times would. Think "run a backlink profile analysis" or "write the robots policy for AI crawlers", one job, done the same way every time. The signal that you have found a skill is simple: the first time you catch yourself re-typing the same careful instructions, that procedure wants to be packaged. For a fuller treatment, see [what a Claude skill is](/guides/what-are-claude-skills/). ## What is an MCP connector? An MCP connector is the plumbing. MCP, the Model Context Protocol, is how a model reaches an external service: a real, production-hardened integration to an API. A skill can teach the model how to think about Search Console data; the connector is what lets it read that data at all. Skills are knowledge, MCP is reach, and keeping the two separate is what makes each easy to fix. A rough rule tells you which one a gap needs. If the problem is "the AI can't see my data," you need a connector. If it is "the AI sees the data but works on it poorly," you need a skill. The longer explanation, including why the protocol is not tied to a single vendor, is in [what MCP is](/guides/what-is-mcp/), and the direct head-to-head is [Claude skills vs MCP](/compare/skills-vs-mcp/). ## What is an agent? An agent is an expert worker that runs a whole job end to end. It composes skills and connectors, makes decisions between steps, and hands back a finished result instead of a single answer. Where a skill does one thing, an agent owns the outcome: it might pull live data through a connector, apply a skill to interpret it, decide what to do next, and only then report. The early mistake we made was cramming all three, method, reach, and orchestration, into one giant prompt. Splitting them made each piece simpler to trust: a skill you can read and audit, a connector you can test in isolation, an agent you can watch make decisions. If you build on Claude Code, [agents and subagents](/guides/claude-code-agents-subagents/) covers how this layer works in practice. ## Two skills, concretely Abstract taxonomy is easy to nod along to, so here are two real skills from what we ended up with. The first controls AI crawler traffic: it decides which crawlers to allow, GPTBot, ClaudeBot, PerplexityBot, Google-Extended and others, generates the matching `robots.txt` and `ai.txt`, and writes the corresponding Cloudflare WAF or nginx rules. The detail that makes it a skill rather than a snippet is that it verifies bots by reverse DNS, so a scraper that simply sets its User-Agent to "GPTBot" does not get waved through. That reverse-DNS check is exactly the kind of hard-won step a plain prompt drops. The second is answer-engine optimization: getting a page cited by LLMs and surfaced in AI-generated answers, not only in the classic blue links. It is not a trick, it is a checklist of structure and evidence that makes a page easy for a model to quote correctly, applied the same way every time. Boring on purpose, which is the point. We are deliberately not attaching before-and-after numbers to either one. What they honestly do is encode a repeatable procedure; what they do not do is guarantee a ranking, and we would distrust anyone who claimed otherwise. ## What we learned building this Four things held up across every skill we wrote. **A skill is a procedure, not a personality.** The prompts that read the best were the least reusable. The ones that worked were plain, almost dull, and described steps rather than vibes. Write for the second time you will run it, not the first. **Split knowledge from reach.** Keeping "how to think about the data" (the skill) apart from "how to fetch the data" (the connector) made both easier to fix. When something broke, we knew which half to look at. **The judgment is the moat, and it only comes from real work.** We could only write these because we had done the jobs on real client sites first. A skill distilled from actual work carries the edge cases; a skill written from imagination carries confidence and little else. **Name the boundary out loud.** Every skill got an explicit line for what it does not do. That single sentence prevented more bad output than any clever instruction, because it stopped the model from wandering past its competence. ## Where to start If you want to see how a skill is actually built before committing to anything, the [free SEO starter](/free/seo-starter/) is a working set you can read and reuse, and the same files are public on [GitHub](https://github.com/development-candavarci/seo-starter). Clone it, open a skill, and take whatever is useful. From there, the fastest way to get the three-part model into your own hands is to browse the [skills](/skills/), [connectors](/mcps/), and [agents](/agents/) and pick the one job you most want to stop re-explaining. The short version, if you take one thing away: stop writing prompts you will paste again next week, and start writing down the procedure. The model does not need your best wording. It needs your judgment, in a shape it can load. --- Maker: Can Davarcı, https://candavarci.com.tr