kingbootoshi

kingbootoshi / directional-prompting

Public

Outcome-first plus directional language. A two-layer skill for writing prompts, agent directives, and skill descriptions. Works in Claude Code and Codex CLI.

44
1
85% credibility
Found May 22, 2026 at 67 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This is a prompt-writing skill that teaches a two-layer technique for getting better results from AI assistants. The first layer establishes a clear outcome (goal, success criteria, stopping point), and the second layer guides the AI with positive, action-oriented language. The skill installs into AI coding tools like Claude Code and Codex CLI, automatically improving any prompts you write or asking the assistant to help you craft. It's designed to work across multiple AI platforms and makes AI responses more predictable and focused.

How It Works

1
💡 You discover a better way to talk to AI

You've been getting inconsistent results from your AI assistant and hear about a technique called 'directional prompting' that promises clearer, more reliable responses.

2
🎯 You learn the two-layer secret

The technique has two parts: first, tell the AI exactly where you want to end up (the outcome), then guide it step-by-step with positive verbs that show the path forward.

3
📦 You install the skill into your assistant

You drop the skill into your AI assistant's folder and restart it. Your assistant now automatically recognizes when you want to write or improve a prompt.

4
✍️ You start writing prompts with the framework

When you ask your assistant to help write a prompt, it opens with a clear goal and success criteria, then rewrites everything using positive, action-oriented language.

5
You can use it in different situations
🤖
For AI coding assistants

Writing instructions that tell your coding assistant how to behave in your projects

📝
For any AI tool

Improving prompts for writing, analysis, or creative tasks

6
✨ Your assistant gets better results

Because every instruction now points toward the destination instead of listing things to avoid, your AI follows the right path on its very first response.

🎉 Your prompts work reliably every time

The AI knows exactly where it's going and how to get there. No more vague responses, no more drifting off topic, just clear outcomes achieved step by step.

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AI-Generated Review

What is directional-prompting?

Directional prompting is a skill that helps you write better prompts for AI agents by enforcing a two-layer structure: outcome-first framing plus directional language. The idea is simple--every prompt opens by naming the destination (goal, success criteria, when to stop), then every sentence inside describes the correct path forward using positive verbs. It works as a plugin for Claude Code and OpenAI Codex CLI, triggering automatically when you write or audit prompts, agent directives, skill descriptions, or slash commands. The skill rewrites negatives into positives, demotes decorative absolute rules, and keeps only four narrow cases where negation actually belongs.

Why is it gaining traction?

Both Anthropic and OpenAI have converged on the same insight: modern models follow instructions literally, and they perform best when you define the target outcome and let the model choose the path. This skill codifies that consensus into a repeatable workflow. The before-and-after example in the docs is compelling--a code reviewer prompt with seven "don't"s collapses into a tight outcome block with directional sentences, same constraints, half the length. Developers are tired of prompts that wander or contradict themselves, and this gives them a checklist: outcome check, direction check, absolute-rule check, read-back.

Who should use this?

Anyone writing system prompts for AI agents will benefit. DevOps engineers building Claude Code or Codex workflows, developers maintaining project-level instructions like AGENTS.md or CLAUDE.md, and teams creating tool descriptions or eval rubrics should install this immediately. It's especially useful for prompt authors who default to prohibitions ("don't do X, avoid Y") and want a framework to flip those into actionable positive instructions.

Verdict

This is a focused, well-documented skill that solves a real problem for agent authors. At 44 stars it's still early, and the credibility score of 0.85% reflects that maturity. If you spend time writing or debugging agent prompts, this is worth installing--the mental model alone will improve your prompts even without the plugin.

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