Directional Prompting
A two-layer skill for writing prompts, agent directives, skill descriptions, slash commands, and anywhere else an LLM reads instructions. Works the same in Claude Code and OpenAI Codex CLI.
Same SKILL.md, same trigger surface, same outcome. Drop it into ~/.claude/skills/directional-prompting/ and ~/.codex/skills/directional-prompting/ and both agents pick it up natively.
The two layers
Layer 1 — Outcome. Every non-trivial prompt opens with a block that names the destination: the goal, what "done" looks like, when to stop, the true invariants. This is the frame.
Layer 2 — Direction. Inside that frame, every sentence names the path forward with positive verbs. "Trace", "build", "use", "read", "return", "ask", "check". The correct behavior is described so clearly and completely that the wrong behavior has no room to exist.
Outcome without direction reads as wishful — the model knows where to go but not how to step. Direction without outcome wanders — the model walks crisp paths to nowhere. Both layers together: a model that knows the destination and walks toward it on every token.
Why both labs converge here
Modern frontier models follow instructions literally. The Claude 4.7 guide: "Positive examples showing how Claude can communicate with the appropriate level of concision tend to be more effective than negative examples or instructions that tell the model what not to do." The GPT-5.5 guide: "GPT-5.5 is strongest when the prompt defines the target outcome, success criteria, constraints, and available context, then lets the model choose the path."
Both labs converge on the same shape. Name the destination. Name the path. Skip the prohibitions.
What this skill does
When the agent sees prompt-authoring language (write a prompt, improve this prompt, audit this system prompt, outcome-first, make this prompt positive, or any new skill, agent, or directive being authored), it:
- Opens the draft with an explicit outcome block:
Goal:, Success means:, Stop when: (plus Constraints: when needed).
- Rewrites the body so every sentence leads with a positive verb naming the correct action.
- Audits the draft for
don't, do not, never, avoid, refrain, instead of, rather than, not allowed, prohibited, forbidden, won't, shouldn't — and rewrites each as the positive replacement.
- Demotes decorative
ALWAYS / NEVER / MUST down to plain prose so the absolute-rule signal stays loud where it matters.
- Keeps negation only in four narrow cases: hard safety boundaries, disambiguating near-identical paths, acceptable spaces too large to enumerate, and specific banned items where the positive form is genuinely ambiguous.
The before-and-after that proves it
Before — no outcome block, mostly negatives, seven don'ts:
You are a code reviewer. Don't be too harsh. Don't nitpick formatting.
Avoid making assumptions about the author's intent. Never approve code
with obvious bugs. Don't suggest changes that aren't actionable. Try
not to be vague. Avoid emojis.
After — outcome on top, directional inside:
Goal: Review the PR diff and decide whether to approve, request changes, or block.
Success means:
- Verdict is one of: APPROVE, REQUEST_CHANGES, BLOCK
- Each comment names the file, line, and replacement code
- Comments cover correctness, security, clarity (skip formatting — the linter handles that)
Stop when: A verdict is issued and every comment is actionable.
Focus on bugs you can reproduce, security boundaries, and unclear logic.
Ask before interpreting intent — quote the line and request clarification.
Block merges on reproducible bugs. Write in plain text.
Same constraints, half the length. The model knows the destination (verdict plus actionable comments), how to stop (verdict issued), and every sentence in the body pulls forward.
Install
Claude Code
Add the marketplace, then install the plugin:
/plugin marketplace add kingbootoshi/directional-prompting
/plugin install directional-prompting
Restart Claude Code if the skill does not appear immediately. After install, the skill triggers automatically on prompt-authoring language, or you can invoke it explicitly.
OpenAI Codex CLI
Codex looks for skills at ~/.codex/skills/<name>/. Drop the skill directory there:
git clone https://github.com/kingbootoshi/directional-prompting.git ~/.directional-prompting
mkdir -p ~/.codex/skills
ln -sfn ~/.directional-prompting/plugins/directional-prompting/skills/directional-prompting ~/.codex/skills/directional-prompting
