agentsclimarketplace

Os architect

Skill richfrem/agent-plugins-skills/plugins/agent-agentic-os/skills/os-architect

repo for reusable plugins and skills

Install
npx -y skills add richfrem/agent-plugins-skills --skill os-architect

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

SME-facing front-door skill for Agentic OS ecosystem evolution. Invokes the os-architect interview flow: classifies intent, audits existing capabilities, proposes evolution path (orchestrate / update / create), and dispatches work. Use when evolving plugins, skills, or agents — whether applying a new pattern, setting up an improvement lab, filling a capability gap, or coordinating multiple loops.

SKILL.md

3.6 KB, as published. Nobody here has run it

Role

os-architect is the single entry point to the Agentic OS evolution ecosystem. The user invokes it when they want to evolve or build anything in the agent/skill/plugin ecosystem. It interviews, audits, and routes — never implements directly. The full behavior spec lives in agents/os-architect-agent.md.

How to Invoke

/os-architect

No arguments needed. Start with a plain-language description of what you want to evolve.

What It Does

  • Phase 1 — Intent Interview: classifies the request into one of 5 evolution categories
  • Phase 2 — Ecosystem Audit: verifies what capabilities exist vs what's missing
  • Phase 3 — Proposal + Dispatch: proposes Path A/B/C and dispatches via the user's CLI tools

Dispatch Paths

PathWhenMechanism
A+ — No Actionaudit shows full match + all patterns presenttell user, no dispatch
A — Orchestratecapability exists, currentroute to existing agent/skill + run_agent.py
B — Updatecapability exists, outdated/incompleteos-evolution-planner writes plan + prompt → dispatch + optional improvement loop
C — Creategap confirmedcreate-sub-agent scaffold → os-evolution-planner plan + prompt → eval lab → evals HARD-GATE → os-architect-tester validates

Related Capabilities

Skill / AgentPurpose
os-evolution-planner skillCalled for Path B/C: writes structured task plan + Copilot CLI delegation prompt
os-architect-tester agentValidates os-architect correctness via scenario simulation — run after any changes
improvement-intake-agentCalled for Category 3 (Lab Setup): configures the skill improvement run
create-sub-agentCalled for Path C (Gap Fill): scaffolds the new agent/skill file

Gotchas

  • Invoked without clear intent: If the user says only "help me" or "I don't know where to start", run Phase 1 open question first — do not assume intent category.
  • PATH detection gives false positive: which gh returns a result but Copilot CLI is not configured. Always confirm tool availability with the user before using a detected tool for dispatch.
  • Path C evals HARD-GATE is non-negotiable: The evals review gate before the first improvement loop must not be skipped even if the user says "just run it." Bad evals waste improvement loop compute.

Smoke Test

  1. Given "I found a new browser harness pattern and want to apply it to my skills" → classifies as Category 1 (Pattern Abstraction), proposes Path A or B, within 2 turns
  2. Given "I need an agent that audits plugin evals for staleness, it doesn't exist yet" → classifies as Category 4 (Gap Fill), proposes Path C, surfaces eval HARD-GATE
  3. Given "run 50 iterations on my os-eval-runner skill" → classifies as Category 3 (Lab Setup), routes to improvement-intake-agent, asks about CLI tools before proposing dispatch strategy
  4. Given "I want to explore improving how we generate agents and maybe create one" → agent identifies Low confidence (Cat 3 + Cat 4 overlap), asks clarifying question before proceeding to Phase 2

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.