The AMD Ross product page reads interestingly. Ross is not one LLM but an agent layer combining MCP Servers, AMD Knowledge Base, Agent Skills, and Design Examples. It plugs into VS Code, Cursor, Claude Code, Codex CLI, and friends.

So MCP plus Skills structure has reached FPGA and embedded work. With air-gapped setups supported, no less.

4 layers: a layer, not a model

Layer Role
MCP Servers Sessions into AMD embedded tools like Vivado plus doc access
Knowledge Base Search over trusted AMD knowledge
Agent Skills Expert-authored steps guiding LLMs (timing closure, RTL analysis, HLS optimization)
Design Examples Tested reference designs showing skills applied

It runs like this. Ask in plain language and Ross searches knowledge, talks to tool sessions, and runs proven workflows pushing design, debug, and optimization toward QoR targets.

Environment terms are set. Vivado in all versions, Vitis HLS from 2025.2 on. Air-gapped builds work off a local knowledge base. No client lock-in. Any MCP-speaking IDE or CLI joins.

The core: SKILL.md turns expert steps into assets

Agent Skills are defined plainly. Expert-authored, structured, shareable workflows. Procedures like timing closure, RTL analysis, and HLS optimization ship as SKILL.md for AI reuse.

Bones of 1 skill (standard Agent Skills shape):
SKILL.md (YAML frontmatter: name plus when-to-fire description)
|- instructions (the steps)
|- scripts (helper scripts)
+- references (reading material)

Agents load it when tasks match
→ an executable asset, not spoken advice

AMD ships this as a public catalog too. amd/skills (MIT) serves Cursor, Claude Code, Codex, and Gemini CLI installs via npx skills add amd/skills. Ross FPGA skills live apart (Xilinx/ross-ai-assistant) with SKILL.md files, docs, and examples.

AMD demos report an HLS workflow cutting FPGA logic resources about 35% at equal throughput. Caveat attached: that is one AMD demo result, never read it as a general benchmark. The number is reference. The structure is the point.

Why it matters: skills outgrew coding convenience

Old skills: coding convenience (command sets, snippets)
New skills: executable domain procedure (expert know-how in AI hands)
→ even high-barrier fields like FPGA go "hand it the steps and it runs"

It proves the SKILLS.md idea (the tech worldview) from the three-layer memory post in a hardware domain. And "ship tools as a standard" from the MCP comparison extends into "ship procedures as a standard."

CodeBridge Mini Lab: turn 1 procedure into a skill

1. Pick 1 repeated debug, build, or analysis procedure
   (example: the post-synthesis timing report walk)
2. Write 1 SKILL.md:
   - name plus 2 firing lines (when to read it)
   - 5 to 10 steps (commands plus checkpoints)
   - 2 failure branches (common failures and responses)
3. Run it through an agent 3 times:
   [ ] Did it follow the order
   [ ] Did it skip no checkpoints
   [ ] Do results reproduce
4. Call it: 3 of 3 reproductions promotes it to team asset

Software teams play the same game. A "7 pre-deploy checks" skill turns rookie agents onto senior steps. Same seat as SKILLS.md in the agent-native docs post.

Conclusion: teams that file know-how win

One line to close.

The moment an expert's head-steps become SKILL.md, every team AI turns senior.

Ross is not really an FPGA story. It is an assetizing method. Write 1 repeated step as a Markdown skill and let AI reuse it. One task for today: turn 1 step your team always explains by mouth into 1 SKILL.md page. That page starts domain skills.

Further reading

References

Go deeper with a course

To practice structuring steps for agent reuse, this course builds CLAUDE.md, skills, hooks, subagents, and MCP in real projects, exactly like the skill assetizing here.