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Article · Friday, September 11, 2026

Agentic Coding

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By Marius BongartsTech103 editions
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Agentic Coding
Friday, September 11, 2026
AI Agents - Agentic Coding

Cole's 34-skill PIV loop, Ouroboros interviews your vague ideas into code

1 min read

Cole's Skills Collection

Your agent workflow just got 34 battle-tested skills in one drop.

Cole Medin released a modular skill library built around a prime → plan → implement → validate → review → commit → PR loop that loads just 4,400 tokens of always-on context [Source: GitHub]. You can install it as a Claude Code plugin, copy the markdown files directly, or reference them from your existing setup. Skills cover codebase priming, PRD-driven planning, git worktree parallelism, and meta-skills for building your own rules and hooks. Everything is plain markdown you can read and tweak—no framework lock-in.

Pair this with the Open Agent Hub from earlier this week and your skill stack doubles overnight.

Ouroboros Agent OS

What if your agent interrogated your idea before writing a line of code?

Ouroboros is a new Agent OS that runs a Socratic interview phase to surface hidden assumptions, scoring ambiguity until it drops below a 0.2 threshold—only then does code generation begin [Source: GitHub]. It works across Claude Code, Codex CLI, Gemini CLI, and a dozen other runtimes. You get three-stage automated evaluation (mechanical, semantic, and multi-model consensus) instead of manual QA. The PAL routing feature picks cost tiers automatically so simple tasks stay cheap. One-line install on macOS, Linux, or Windows.

If you've been fighting vague PRDs, this is the forcing function you needed.

Workflow Convergence Pattern

The pattern emerging this week is clear: registry plus orchestration plus interview.

Cole's skills library and Open Agent Hub give you verified, portable capabilities. Ouroboros adds the structured intake that turns fuzzy ideas into tight specs before execution starts. Combined with Paperclip's orchestration from yesterday, you now have a three-layer stack: interview for clarity, registry for skills, harness for coordination. Each piece is modular—swap one without rebuilding the others. The community is converging on this architecture faster than any single tool can ship it.

Worth wiring up one layer this weekend and seeing how your session quality shifts.

Sources
GitHub - coleam00/skills: The agent skills I actually use to build ...
GitHub - coleam00/skills: The agent skills I actually use to build ...
8 hours ago ... These 34 skills are the AI Layer from my Agentic Coding course. They're ... Structural code search by AST pattern instead of text. drive-screen, Real ...
github.com
AI Summary

Cole's AI Skills is a collection of 34 markdown-based skills for agentic coding with Claude Code and other agents, organized around a prime → plan → implement → validate → review → commit → PR loop. Skills are modular procedure files that load efficiently (4,400 tokens of always-on context for descriptions only) and can be installed as a Claude Code plugin, copied as editable files, or referenced directly. The system includes skills for priming codebases, planning with PRDs and architecture docs, executing the PIV loop with implementation and validation, investigating and fixing issues, parallel work via git worktrees, and meta-skills for building custom AI layers like rules, hooks, and opportunity scanning. All skills are plain markdown files designed to be read, edited, and adapted to individual workflows rather than enforced as a framework.

Visit source
GitHub - Q00/ouroboros: Agent OS: the agent gets smarter on its ...
GitHub - Q00/ouroboros: Agent OS: the agent gets smarter on its ...
19 hours ago ... ooo interview is the first workflow command and starts the Socratic interview. After setup, Codex follows its currently selected model and Claude Code starts ...
github.com
AI Summary

Ouroboros is an Agent OS for AI coding that transforms vague ideas into verified, working codebases through a structured workflow: Interview → Seed → Execute → Evaluate → Evolve. The platform uses Socratic questioning to expose hidden assumptions and scores ambiguity (threshold ≤ 0.2) before code generation, eliminating the need for ad-hoc prompting. It works across multiple AI agents including Claude Code, Cursor alternatives like Codex CLI, and others, offering replayable, observable execution contracts with three-stage automated evaluation (Mechanical → Semantic → Multi-Model Consensus) instead of manual QA. The system uses a Double Diamond methodology where the first diamond converges vague goals into ontological clarity through questioning, and the second diamond converts design options into verified delivery. Key features for solo founders include efficient PRD and AGENTS.md workflows, PAL routing for cost-optimized multi-tier execution (1x/10x/30x), brownfield repository detection, and persistent evolutionary loops (Ralph) that iterate until ontology convergence (similarity ≥ 0.95). Installation is one-line across macOS, Linux, Windows, and integrates natively with Claude Code, Codex, GitHub Copilot CLI, and a dozen other runtimes.

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