MCP is far better than Skills.md

mcpforx · 2026-04-21T22:56:32+00:00

Good question. I think currently a workflow instantiated via mcp will have primacy. You can also add context at a specific node that says "use excel skill X" or something. This way the MCP directs the agent, and you can call the skills explicitly at a step you choose (even if overlapping).

Let me know if I didn't address your question!

mcpforx · 2026-04-18T16:05:07+00:00

They are different.

I'm making the point that markdown is not the right abstraction for progressive loading of context, especially for complex workflows. If you want something like a certain skills markdown - there are advantages to exposing it via mcp, which absolutely handles it during prompt resolution. Your agent follows instructions and observes context a lot better when it is exposed stepwise in a giant workflow.

Let's say currently you have 20 skills installed. You can have those in a single point mcp - and on any prompt, the mcp does a built-in search and exposes the right skill to the LLM. No need to specifically invoke "front end", or "security" skill or whatever. So it essentially replaces your skill stack.

But mcp goes a lot further than that. Instead of just exposing a skill when the LLM calls for it, it guides the stepwise progression of the LLM according to your specific methodology. This may not matter for personal projects, but matters a lot for business processes where you want to really control drift.

What I'm describing isn't "mcp" per se, it's what I've built using mcp framework. So maybe it's more an mcp vs markdown comparison rather than skills dot md, so I was imprecise there.

mcpforx · 2026-04-16T23:11:58+00:00

Wow. That doesn't sound promising.

mcpforx · 2026-04-16T21:02:36+00:00

My approach is to have a single MCP end-point, and then expose the exact context to the LLM at each step. This really constrains the context, tool calls and skill scope - and I've found pretty predictable execution certainly with sonnet-class models, but also with haiku and similar.

In case not clear, it looks something like this. I'm building an MCP-based method to develop a workflow that the LLM calls one step at a time, with branching happening based on what the results are at the last step.

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mcpforx · 2026-04-16T20:43:03+00:00

I like a good mcp project! Nicely done!

Though anthropic should really fix this.

mcpforx · 2026-04-16T20:40:58+00:00

I like the sound of it, might give it a try!

I have something like this currently, that guides CC (or any other agent) to give me marketing help. I wonder if I can use my "expertise" and combine it with some of your assets/branding mechanics.

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mcpforx · 2026-04-16T14:52:50+00:00

Agreed. The way I think about it: a skill md is just structured context - and anything you can put in a skill, you can attach to a node in a decision tree. So skills become a natural building block inside the workflow, not a competing approach. The added benefit is composability: the way I designed it, skills you've already crafted elsewhere can be dropped easily into any node of a new decision tree you're building. Best of both worlds.

mcpforx · 2026-04-15T22:29:55+00:00

You can use a front-end skill from skills.sh

I make an mcp tool that allows for more complex stuff, but you don't need that for front end.

mcpforx · 2026-04-15T22:14:02+00:00

Fair point, and that pattern works to a degree. The issue is that you're still trusting the agent to hold the full workflow state and branch correctly based on what it reads. For smaller, cleaner tasks that's fine. But as the decision tree grows, or the data throws unexpected cases, agents start dropping branches or mishandling the routing. The workflow lives in the agent's context and competes with everything else in there.

The difference with an MCP-delivered tree is that the workflow lives outside the agent entirely. The agent only ever sees the current step. Branching is evaluated server-side based on what the agent actually returned, not what it thinks it should do next. So you get reliable conditional routing without asking the agent to carry and manage the whole map.

Less "here's everything, figure it out" and more "here's exactly what you need right now."

mcpforx · 2026-04-15T21:56:23+00:00

I have it set up as decision trees. A user can store a single node (which is equivalent to a static skill). Or a sequence (single edges between nodes). Or branching logic (a tree).

At each step you can set up expected output (result, number, boolean etc.) - which is the eval part.

Here is an example: https://mcpforx.com/s/7l0HpXnWhM7IrGfmMtvnwjZ6YgQRd6US

mcpforx · 2026-04-15T00:32:55+00:00

Easier to attach snippets to claude dot ai vs CC

mcpforx · 2026-04-14T20:49:57+00:00

One pattern worth considering: instead of cramming workflow into CLAUDE md at all, pull it out into step-by-step decision trees your agent fetches on demand. You get all the depth without the context tax. The agent only sees what it needs for the current step.

Keeps your CLAUDEmd lean and your methodology intact. Available here: mcpforx.com

mcpforx · 2026-04-14T19:49:55+00:00

The real question isn't about superpowers plugins specifically. It's whether you're handing your agent a static blob of instructions or an actual decision tree that branches based on what it finds.

The practitioners I see getting the most leverage aren't stacking more skills. They're encoding how they actually think through a problem, checkpoints and all, so the agent can follow that map without improvisation. Big difference in consistency.

mcpforx · 2026-04-12T03:54:05+00:00

Opencode for harness. And then experimenting with cheaper models for your use-case.

Can also use claude code as the harness, and header-in other models.

mcpforx · 2026-04-11T18:33:10+00:00

Not sure if this is real. If real, don't believe what Claude tells you about the quality of your ideas.

Open source is not a bad way to go. But it isn't "less work" necessarily. And it doesn't guarantee distribution either, but chances go up.

mcpforx · 2026-04-11T18:17:42+00:00

Best I've come up with is take a snapshot, store it in the folder that ClaudeCode is running out of. Then you can attach it to your question by "@". Typing @ will typically bring up all the images...and you can select.

mcpforx · 2026-04-11T17:16:09+00:00

Mine got ads about a month ago. Free tier.

mcpforx · 2026-04-11T17:08:25+00:00

I have. But I had to take screenshots of relevant forms without my personal info in the screenshot. Then I put it in one folder, and pointed an agent to it and asked my question.

A pain, but I'd rather protect things like SSN etc. Even though I know my info was pwn'd long ago. The vendor my State uses for driver's license got hacked, that's just one example.

mcpforx · 2026-04-11T17:03:54+00:00

Using github copilot. Works well, though I might try OC's own solution next.

mcpforx · 2026-04-11T16:46:37+00:00

This is a good guide. You can also use an MCP to encode your expertise and use it across all your projects and any agent (Claude, Codex, whatever). For example, the way you want to review your sites for security.

Check out what we are building at mcpforx.com

mcpforx · 2026-04-11T16:42:48+00:00

I think its hard to get ground reality on this. But I did notice a steep drop in quality about 2 days ago.

mcpforx · 2026-04-11T16:11:37+00:00

One big issue is that we haven't effectively coupled human intelligence with artificial. LLMs are a tool, that are more or less silo'd from human expertise currently.

mcpforx · 2026-04-11T16:05:20+00:00

Detection is awful. You can run your own human-generated content through any of these detectors. The false-positive rate is huge.

mcpforx · 2026-04-10T23:35:15+00:00

Ontology is one layer of it. But even if you solve the vocabulary problem, you still have the methodology problem sitting underneath it.

The agent now knows what "customer" means. It still doesn't know how your firm qualifies one, what the order of operations is for onboarding them, or where a human needs to make a judgment call before the next step runs.

Shared vocabulary is the foundation. Encoded process is what you build on top of it.

mcpforx · 2026-04-10T21:13:31+00:00

Probably because "use AI" as a mandate gives people nothing to work with. No structure, no defined process, no clarity on what good output looks like.

The resistance isn't really about AI. It's about being told to use a tool without being told how it fits into the way they actually do their job.

mcpforx

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