A reusable AI prompt library stores durable instructions—goals, constraints, required questions, acceptance criteria, and review checklists—separately from project-specific context. Use guided fields to fill the framework, files, risks, and desired outcome each time, then review both the prompt and the generated code.
Key takeaways
- Save repeatable reasoning structures, not giant prompts tied to one project.
- Separate context, task, constraints, evidence, and acceptance criteria.
- Create distinct prompts for planning, implementation, review, and verification.
- Never paste secrets or proprietary code into an AI provider without authorization.
Treat prompts as a small engineering system
A useful prompt library is closer to a set of reusable test fixtures than a collection of clever phrases. Each prompt has an intended stage, required inputs, expected output shape, and a review step. That makes it easier to improve when the result is weak.
Start with five jobs: clarify a request, produce a plan, implement a bounded change, review a diff, and verify the complete user story. Keeping these separate reduces the chance that a model plans, changes, and approves its own work in one unexamined step.
Build one durable implementation brief
The brief should ask for the repository context, user-visible outcome, constraints, files or systems in scope, acceptance criteria, and checks that must pass. It should also tell the agent to surface ambiguity before taking a materially different action.
Store stable rules in the snippet and collect changing values with guided fields. The resulting prompt remains consistent without carrying the last project's framework, path, or customer requirement into the next one.
;xbriefGoal: {ask:User-visible outcome}
Context: {ask:Repository or product context}
In scope: {ask:Files or systems}
Constraints: {ask:Constraints}
Acceptance criteria: {ask:Observable success}
Before changing code, list assumptions and the verification plan.Make review prompts specific enough to find real defects
A generic request to review code often produces a generic answer. Ask for concrete failure modes: broken user flows, unsafe input handling, privacy leaks, accessibility regressions, stale loading states, race conditions, incomplete error handling, and missing tests. Require file and line references when the tool has them.
Separate correctness review from style review. A naming suggestion should not obscure a data-loss bug. Prioritize findings by user impact and confidence, then verify the claimed issue directly.
- Behavior: does the change satisfy the observable acceptance criteria?
- Boundaries: what happens with empty, malformed, slow, or repeated input?
- Security and privacy: does data cross a new trust boundary?
- Operations: can the change be monitored, reversed, and supported?
Use clipboard transforms carefully
A clipboard workflow can wrap selected code or logs in a review prompt, but copied content may contain tokens, customer data, internal URLs, or proprietary code. Review the payload before an AI transform and understand whether the selected provider is local, bring-your-own-key, or hosted.
Redact secrets and minimize context. More tokens are not automatically better context; irrelevant files can distract the model and increase exposure. Include the smallest evidence set that lets the task be understood and verified.
Improve the library from failures, not vibes
When an AI result misses the mark, identify which input or constraint was absent. Add a required field, a clearer boundary, or an explicit check. Do not keep appending defensive paragraphs without understanding the failure; oversized prompts become hard to maintain and may contain conflicting instructions.
Version the five core prompts mentally or in names, retire duplicates, and keep a short change note. The goal is a compact system that captures your standards while keeping the developer responsible for the code that ships.
- 01
Save five stage-specific prompts.
- 02
Use each on three real tasks.
- 03
Record recurring omissions or weak outputs.
- 04
Change one instruction at a time and compare the result.
- 05
Retire prompts that do not produce a consistent advantage.
Common questions
01Is ExpandCaptain an AI coding agent?+
No. It can store and insert prompts or run explicit clipboard transforms and Mac actions. The coding agent, model provider, code review, and shipping decision remain separate.
02Should I save one enormous master prompt?+
Usually no. Smaller prompts for planning, implementation, review, and verification are easier to maintain, test, and combine with the right project context.
03Can I store API keys in a prompt snippet?+
Do not. Use a secrets manager or approved environment-variable system. Keep credentials out of snippet libraries, chat histories, and copied context.
Sources
Product features and prices were checked on July 16, 2026. They can change. Comparisons describe published scope and are not claims of complete feature parity.
- ExpandCaptain guide for vibe coders Product-specific prompt, trigger, review, and clipboard-workflow examples.
- ExpandCaptain on the App Store Current product platform and feature description.
