gateguard

Overview of gateguard skill

gateguard is a fact-forcing pre-action gate for Claude workflows that blocks the first Edit, Write, or Bash attempt, then requires concrete investigation before the action is allowed. The gateguard skill is best for codebases where changes can ripple across modules, schemas, or team conventions, and where a generic prompt would be likely to guess instead of inspect.

What users usually want from gateguard is not “more AI control” in the abstract; they want fewer wrong edits, better first-pass implementation quality, and a workflow that makes the model prove it has read the right files before it writes. Its main differentiator is the three-step loop: deny the action, force fact gathering, then allow retry with evidence.

What gateguard is for

Use gateguard for Workflow Automation when you want an agent to slow down before touching code and gather specifics first: importers, schemas, file ownership, user instructions, and existing patterns. It is especially relevant when one edit can affect several files or when the repo contains structured data that needs exact handling.

Why this skill changes outcomes

gateguard is not just a reminder to “be careful.” It converts caution into a required workflow, so the model has to inspect the repository before it can proceed. That matters most when the failure mode is confident guessing, not lack of instructions.

Best-fit readers

This gateguard guide is for people deciding whether to install the skill into a Claude-based coding workflow, especially if they manage larger repos, team conventions, or AI-assisted edits that must stay aligned with existing code. If you mainly want a lightweight prompting trick, this may be more process than you need.

How to Use gateguard skill

Install and activate it

Install gateguard with:

npx skills add affaan-m/everything-claude-code --skill gateguard

After install, make sure the skill is available in the Claude workflow before you rely on it for edits. The gateguard install is most useful when it is part of the normal path to making changes, not a one-off experiment.

Read the right files first

Start with SKILL.md, then inspect any repository instructions that shape how the skill behaves in your environment. In this repo, the main file is the skill itself, so the first read should focus on its activation rules, gate logic, and evidence requirements.

A practical reading order for gateguard usage is:

1. SKILL.md for the gate behavior and trigger conditions
2. Any surrounding repo instructions such as README.md or AGENTS.md if present in your environment
3. Files that define the target feature, schema, or module you plan to change

Turn a vague goal into a usable prompt

gateguard works best when your request names the task, the suspected files, and the facts the agent should prove before editing. A weak request is “fix the bug.” A stronger one is:

• “Investigate which files import analytics.ts, confirm the data format used in the webhook validator, then propose the minimal edit.”
• “Before writing, identify the schema fields, the user-facing instruction source, and any tests that cover this path.”
• “Use gateguard behavior: gather evidence first, then patch only the affected module.”

This matters because gateguard is designed to force discovery, not just restraint.

Practical workflow for better output

The most reliable gateguard usage pattern is: ask for investigation, review the gathered facts, then authorize the edit. If the model surfaces missing importers, schema constraints, or conflicting instructions, use that as the decision point before allowing changes.

Good inputs usually include:

• the target file or subsystem
• the expected behavior
• the data shape or interface involved
• any known constraints, such as formatting or compatibility requirements

gateguard skill FAQ

Is gateguard only for large repositories?

No. The gateguard skill is most valuable in larger or more interconnected repos, but it can also help on smaller projects when the main risk is the model skipping investigation and making a premature edit.

How is this different from just prompting “think carefully”?

A normal prompt relies on self-checking. gateguard changes the workflow so the model must gather facts before it can proceed. That is the core advantage of gateguard usage: evidence comes first, not after the mistake.

Is gateguard beginner-friendly?

Yes, if you are comfortable giving the agent a specific task and then reviewing the evidence it collects. It is less suitable if you want the model to act immediately without interruption.

When should I not use gateguard?

Skip it when you need a fast throwaway edit, a trivial single-file change, or exploratory work where forcing investigation would add more friction than value. gateguard is strongest when the cost of a wrong first edit is high.

How to Improve gateguard skill

Give it concrete evidence targets

The biggest quality gain comes from telling the model what facts must be verified before editing. For example, ask for importer lists, schema definitions, file ownership, or the source of user instructions. That makes gateguard more effective than a generic “analyze first” request.

Watch for common failure modes

The main failure mode is shallow investigation: the model reads one file, then acts as if it has enough context. Another failure mode is over-broad searching that produces facts but not decision-ready evidence. If that happens, tighten the request to specific files, symbols, or behaviors.

Iterate after the first response

Use the first pass to confirm scope, then refine. If the evidence is incomplete, ask for the missing dependency chain, the exact data format, or the tests that define expected behavior. If the proposed edit is too broad, narrow the target and rerun the gateguard workflow.

Shape prompts for the repo you have

The best gateguard guide inputs reflect your actual repository structure, not a generic template. Mention the module name, the likely callers, and the constraint that matters most, such as compatibility, schema accuracy, or matching existing patterns. That keeps gateguard focused on facts that change the patch, not on trivia.