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Before an AI coding assistant edits files or runs commands, ask it to identify consequential unknowns, question you about them, and wait for your explicit approval of a plan. A prompt sets that expectation; a product’s permission mode may add a separate technical checkpoint. Use both when you need stronger control.
Use this prompt to surface ambiguity before implementation
Adapt this reusable instruction to your task:
Before implementing this task, inspect the relevant project context and requirements. Identify any ambiguity, missing information, or assumptions that could materially change the solution. Ask me concise, specific questions about those points and wait for my answers. Once the requirements are clear, summarize your understanding and propose a short implementation plan. Do not edit files or run commands until I explicitly approve the plan. If you discover a new material ambiguity while implementing, stop and ask before proceeding.
To make the approval checkpoint unambiguous, specify the exact response that counts as approval—for example, “wait until I say ‘approved.’” Ask the assistant to include the files it expects to touch, the intended behavior, and significant risks in its plan. That gives you something concrete to review rather than a vague promise to proceed carefully.
What to check before you approve
Review the plan against the behavior you want, not just whether its steps sound plausible. In particular, check:
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- Scope: Does the plan name the relevant files or areas and avoid unrelated changes?
- Requirements: Did the assistant resolve questions that could materially alter the implementation?
- Actions: Does the plan distinguish code edits from commands it intends to run?
- Risks: Are there assumptions or meaningful consequences you need to accept first?
If something is still unclear, answer the open question or ask for a revised plan. A new consequential uncertainty during implementation is also a reason to pause and clarify, rather than letting an early approval silently cover a different task.
A prompt is not the same as a permission control
The instruction asks the assistant to behave in a particular way; it does not guarantee that the software will technically prevent edits or command execution. A plan or approval mode can provide an additional boundary, but the boundary differs by product and sometimes by action. Check whether the setting blocks file changes, terminal commands, both, or neither, and whether approval applies to a plan or to each action. Your IDE, account, and organization policy may affect what is available.
How documented approval modes differ
| Product and mode | What the cited documentation says | What to keep in mind |
|---|---|---|
| OpenAI Codex Suggest | OpenAI describes Suggest mode as proposing edits and shell commands while requiring approval before changes are made or commands are executed. OpenAI Help Center | The same source distinguishes Auto Edit and Full Auto, whose stated permissions differ. Confirm the current labels and behavior in your environment. |
| GitHub Copilot Plan mode | GitHub says the plan agent makes no code changes until you review and approve the plan. GitHub Docs | GitHub labels Plan mode public preview and subject to change. A plan approval is not necessarily a per-command approval. |
| Claude Code plan mode | Anthropic describes plan as read-only: it proposes a plan and waits for approval. Anthropic Help Center |
Review the active permission mode and its current behavior before relying on it. |
| Claude Code manual mode | Anthropic says manual asks before risky edits or commands; the FAQ also says Shift+Tab cycles permission modes. The FAQ describes acceptEdits as allowing file edits while still asking before commands. Anthropic Help Center |
These modes separate file-edit and command permissions; do not assume one approval covers both. |
| GitHub Copilot agent mode | GitHub describes agent mode as handling a high-level task by choosing files, making edits, and running commands as needed. Its cited IDE documentation says you can confirm or reject each proposed terminal command. GitHub Docs | Agent mode is intended to carry out implementation, unlike a plan-first checkpoint. Command confirmation does not, by itself, establish that every file edit requires separate approval. |
These are vendor-specific descriptions, not interchangeable guarantees. Product labels, preview status, and available controls can change; check the current documentation and the settings that apply to your account or organization.
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Choose the checkpoint that matches the risk
For a small, reversible task, a clear prompt and a reviewed plan may be enough for your workflow. For changes where unintended edits or commands would be costly, use a mode that independently blocks the actions you want to control, then verify its exact permissions before starting. In either case, keep approval specific: approving a plan should not be treated as approval for newly discovered work outside that plan.
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