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Skill

/guard

Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries.

From plugin
maestro
41125 skills
Install
$ npx -y skills add sharpdeveye/maestro --skill guard --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/guard

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries.

SKILL.md

guard.SKILL.md
name: guard
description: "Use when deploying to production, handling sensitive data, or the workflow needs safety constraints, input validation, and security boundaries."
argument-hint: "[threat or area]"
category: enhancement
version: 2.0.0
user-invocable: true

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first.

Consult the guardrails-safety reference in the agent-workflow skill for the full defense-in-depth framework.

---

Add safety boundaries to a workflow. Guards protect against malicious inputs, unintended outputs, data leakage, cost explosion, and all the ways an autonomous system can go wrong in the real world.

Threat Assessment

Before adding guards, understand what you're protecting against:

| Threat | Risk Level | Guard Type | |--------|-----------|-----------| | Prompt injection | High | Input sanitization, instruction hierarchy | | PII leakage | High | Output filtering, data masking | | Cost explosion | High | Token budgets, rate limits | | Unauthorized actions | Medium | Permission scoping, confirmation gates | | Hallucination | Medium | Source attribution, fact checking | | Service abuse | Medium | Rate limiting, authentication |

Guard Implementation

**Input Guards**

Before processing any input:
1. Validate against schema (reject malformed)
2. Check size limits (reject oversized)
3. Sanitize for injection patterns
4. Rate limit check (reject if exceeded)
5. Authentication/authorization check

**Output Guards**

Before returning any output:
1. Schema validation (format correct?)
2. PII scan (names, emails, SSNs, etc.)
3. Content policy check
4. Confidence threshold check
5. Source attribution present?

**Cost Guards**

Before every model/API call:
1. Check remaining budget
2. Estimate request cost
3. If estimate > remaining budget → reject or use cheaper alternative
4. After call → update spent amount
5. Circuit breaker check (too many failures?)

**Permission Guards**

For every tool call:
1. Is this tool allowed for this user/context?
2. Is this a destructive operation? → require confirmation
3. Is this accessing data the user is authorized for?
4. Log the access for audit trail

Guard Checklist

  • [ ] All inputs validated before processing
  • [ ] PII detection on all outputs
  • [ ] Cost ceiling set with enforcement
  • [ ] Prompt injection defenses active
  • [ ] Destructive operations require confirmation
  • [ ] All access logged for audit
  • [ ] Circuit breakers on external services
  • [ ] Rate limits on all endpoints

Recommended Next Step

After adding guards, run `/evaluate` with adversarial test scenarios to verify guards hold under attack.

**NEVER**:

  • Deploy without input validation
  • Trust model output for high-stakes decisions without verification
  • Run without cost controls
  • Skip logging (you need the audit trail)
  • Assume the model will follow safety instructions 100% of the time
Read more
Ships withmaestro

Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and 6 more.

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TypeScript
Language
MIT
License
3mo ago
Last commit
4mo ago
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Repo: sharpdeveye/maestro