mcpField
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guard

guard

github.com/sharpdeveyeSecurity592345 installsUpdated 26w ago
Repository

About guard

guard is a Maestro workflow skill for adding safety controls to AI workflows. It covers input and output checks, cost limits, permissions, and audit logging.

Key features

  1. 01Input validation and sanitization
  2. 02Output and PII checks
  3. 03Budget and rate controls
  4. 04Permission and confirmation checks
  5. 05Audit logs and circuit breakers

Use cases

  1. 01Screening input for injection attempts
  2. 02Preventing sensitive data in outputs
  3. 03Limiting model-call spending
  4. 04Requiring approval for destructive tools

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sharpdeveye/maestro --skill guard

After installing, type/guardin Claude Code to confirm it loaded.

Details

From the project README

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:

ThreatRisk LevelGuard Type
Prompt injectionHighInput sanitization, instruction hierarchy
PII leakageHighOutput filtering, data masking
Cost explosionHighToken budgets, rate limits
Unauthorized actionsMediumPermission scoping, confirmation gates
HallucinationMediumSource attribution, fact checking
Service abuseMediumRate 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

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