Prompt Engineering

Prompt Engineering for Claude Code

The artifacts in this toolkit — agents, skills, commands, instructions — are prompts. They only help if the model (a) loads them at the right moment and (b) can act on them. These two failure modes drive every guideline below.

Descriptions decide whether you get loaded

For agents and skills, the description is the only thing the model sees when deciding whether to invoke. Write it for triggering, not for humans:

• Start with "Use when…" and name the concrete situations, symptoms, and keywords a

user would actually say.

• Include 1–2 example triggers. Specificity beats elegance.
• State what it's for and what it's not if there's a near neighbor (e.g. "for

design, not implementation").

• A vague description ("helps with code") never fires. A precise one fires reliably.

System prompts: role, method, output, boundaries

Structure the body of an agent/skill as:

1. Role — who it is and what it optimizes for, in one or two sentences.
2. Method — the concrete steps or checklist it follows. Numbered, specific.
3. Output format — the exact shape you want back. Models follow a template well.
4. Boundaries — what not to do, and what to do when uncertain (ask vs. assume).

Write positive instructions ("do X") over long lists of prohibitions. Show the format with a small example rather than describing it abstractly.

Scope tools to the job

Give an agent only the tools it needs. A reviewer or auditor is read-only (Read, Grep, Glob, Bash) — no Edit. A refactorer needs Edit. Narrow tools reduce mistakes and make the agent's contract obvious.

Progressive disclosure (skills)

Keep SKILL.md lean — the high-frequency guidance. Push exhaustive references (checklists, catalogs, schemas) into sibling files the model loads only when needed, and point to them by name. This keeps the always-loaded surface small while making depth available on demand. (See code-review-rubric and refactoring-catalog here for the pattern.)

Calibrate, don't overclaim

Tell the model to express uncertainty, prefer evidence over assertion, and stop and ask when a decision is genuinely the user's. An agent that fabricates confidence is worse than one that says "I'm not sure — here's what would confirm it."

Operating-discipline patterns

The sections above cover structure — how to make an agent load and be actionable. For the behavioral techniques that separate a sharp agent from a vague one (parallel tool use, read-before-edit, verify-with-evidence, autonomy and stop conditions, output contracts, delegation briefing), load PATTERNS.md. It distills what production coding agents converge on, so you can state the right discipline in each agent's Method/Boundaries.

Test it

After writing, sanity-check the trigger: would the description fire on a realistic request? Would it over-fire on unrelated ones? Tune the description until it's sharp.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: AlisinaDevelo
• Source: AlisinaDevelo/md-files
• License: MIT

Install and usage instructions live in the source repository linked above.