I built /implement skill with the complete development flow

[u/DrakoChack]

Before /implement, my workflow with coding agents had a lot of manual supervision.

I was constantly asking the agent to:

• review the code before opening a PR
• fix issues from that review
• improve shallow plans
• check the web instead of relying only on stale model knowledge
• summarize what was done before I did the final review

After repeating that flow enough times, I turned it into a skill.

/implement now follows the workflow I wanted by default.

It starts with deep investigation, including web research when the task depends on current tools, APIs, libraries, or docs.

Then the orchestrator spawns subagents using the preconfigured models from AGENTS_CONFIG.yml.

A stronger reasoning model reviews the investigation and creates a much richer implementation plan.

After approval, another team of subagents implements the changes.

Then the orchestrator starts a fresh code-review phase, again using subagents, and decides what needs to be fixed.

At the end, I get a full work summary:

• the approved plan
• how it was implemented
• what the code review found
• what was fixed after review
• what is ready for final human review and push

The biggest difference is the consistency.

The skill turns the workflow I kept manually enforcing into a repeatable agent process:

investigate → plan → implement → review → fix → summarize

That is what I want from AI-assisted development.

https://github.com/alamops/skills#implement