Custom Prompt Templates for AI Coding Agents

Custom prompt templates for AI coding agents are useful when the same workflow keeps showing up with slightly different inputs.

Maybe every bug fix should start with a failing test. Maybe every docs change needs a link check. Maybe every dependency update should include a package-manager constraint, a lockfile check, and a short risk summary. You can type that guidance by hand every time, but repeated prompts eventually become inconsistent prompts.

Junction supports per-repository slash commands and reusable prompt templates so common agent tasks can start from a known shape. The goal is not to hide judgment. The goal is to make the repeatable parts of Claude Code and Codex work explicit before the agent starts editing.

Prompt templates are not magic prompts

A useful template is not a spell that makes an agent correct. It is a compact operating agreement for a task class.

Good templates answer practical questions:

• What kind of work is this?
• Which files or areas are in scope?
• What should the agent avoid?
• What evidence should it collect before editing?
• Which command should verify the result?
• What should the final summary include?

Those questions are boring in the best way. They reduce ambiguity before it becomes a messy diff.

How this relates to provider-native guidance

Claude Code and Codex both have native ways to make repeated instructions easier.

Claude Code's current docs describe skills and slash-command style workflows. A skill can be invoked directly with a slash command, and Claude Code can also load it automatically when the description matches the request. The Claude Code skills and slash commands docs explain that project and personal skill locations can define reusable instructions, while the SDK docs describe legacy .claude/commands/ Markdown files and dynamic arguments.

Codex has a different but related model. OpenAI's Codex AGENTS.md docs explain how Codex reads global and project instruction files before starting work, including nested overrides and fallback filenames. The Codex CLI docs describe Codex as a local terminal agent that can read, edit, and run code in the selected directory.

Junction does not replace those provider-native systems. It adds a control surface around the local sessions where your repeated workflows can be easier to start, monitor, and review from the same place.

Think of the layers this way:

The strongest workflow uses these layers together instead of asking one prompt to carry everything.

What belongs in a reusable template

Start with a task that happens often and has a predictable shape.

Good candidates:

• add a regression test for a named bug,
• update copy in one product surface,
• investigate a failing command without editing yet,
• summarize the current diff before review,
• prepare a pull request description,
• run a dependency update check,
• audit a feature for missing empty states,
• or compare two implementation options before choosing one.

Poor candidates:

• redesign the onboarding flow,
• make the app better,
• fix all flaky tests,
• improve performance everywhere,
• or refactor this package.

Those can be real tasks, but they are not good templates until someone breaks them into smaller repeatable workflows.

A practical template shape

Here is a reusable structure that works well for local agent work:

Task type:
Bug fix with regression test.
 
Inputs:
- Issue or bug summary:
- Expected behavior:
- Current behavior:
- Suspected files:
 
Instructions:
1. Inspect the relevant files before editing.
2. Identify the smallest failing test or add one if no focused test exists.
3. Keep the fix scoped to the named behavior.
4. Do not change public copy, pricing, provider setup, or unrelated styles.
5. Run the narrow test first, then the package-level check if needed.
 
Final response:
- Root cause:
- Files changed:
- Verification run:
- Remaining risk:

That template does not decide the fix. It sets the rails for a fix.

If the agent discovers the scope is wrong, the right behavior is to stop and explain the mismatch, not quietly expand the change.

Why templates matter more on mobile

When you start or steer an agent from a phone, prompt quality matters more because you have less room for long clarification loops.

A template lets you fill in the variable parts without retyping the whole workflow. That is especially useful when you are away from your desk and want to start a bounded task, not write a miniature project brief on a small keyboard.

For example, a mobile prompt can be as simple as:

Use the regression-test template.
Bug: setup page shows the wrong prerequisite copy for GitHub CLI.
Expected: copy says GitHub CLI is needed for Git and PR workflows.
Suspected files: packages/site/src/pages/setup.tsx and related tests.

The template carries the testing, scope, and summary requirements. The mobile input carries the case-specific facts.

Junction's role in the workflow

Junction is useful here because the template is only the start of the run.

After the agent begins, you still need to know:

• whether it is reading the right files,
• whether it asks for a risky approval,
• whether the diff is staying in scope,
• whether the test command passed,
• and whether the result is ready for a pull request or needs another turn.

That is why templates belong near the control surface. A prompt template can start the workflow. Junction's daemon and web app help supervise the local session after that.

The code stays on the daemon machine. The template does not move the repository into a hosted workspace. Claude Code or Codex still runs where the project already lives.

Template examples worth keeping

Narrow bug fix

Use this when the expected behavior is clear.

Investigate and fix the named bug. Add or update the smallest regression test that proves the behavior. Keep the change inside the named package unless the investigation shows a shared helper is responsible. Summarize the root cause and verification command.

Diff summary before review

Use this when an agent already changed files and you want a review-oriented handoff.

Summarize the current diff for human review. Group changes by behavior, call out risk, list tests already run, and name anything that still needs desktop review. Do not edit files.

Docs or copy update

Use this when the work should not drift into product behavior.

Update only the requested docs or user-facing copy. Preserve product facts and pricing exactly. Do not change runtime behavior. Check nearby tests or snapshots if they assert the copy.

Investigation only

Use this when implementation should wait.

Investigate the issue and report findings before editing. Identify likely files, relevant tests, and the smallest safe fix path. Do not modify files unless explicitly approved in a later turn.

What to avoid

Avoid templates that hide too much judgment.

Do not write:

Fix this however you think best.

Do not write:

Refactor the whole area and make it cleaner.

Do not write:

Ship this with full autonomy.

Those instructions are not reusable workflows. They are invitations for scope drift.

Also avoid packing every team norm into every template. Stable project-wide rules belong in provider-native guidance such as Claude skills or Codex AGENTS.md. Templates should stay focused on the task type.

A simple adoption path

Start with three templates:

1. Investigation only.
2. Bug fix with regression test.
3. Diff summary before review.

Those cover the most common moments where agent runs either become useful or start to sprawl. Once the team trusts those patterns, add more specialized templates for docs, dependency updates, UI copy, or release notes.

If you are using Junction for Claude Code or Codex today, pair the daemon first with the setup guide. When you need more open chats or more daemon connections for parallel template-driven work, compare the limits on pricing.