Skip to content
cccskills
FreePublished on GitHub

guard

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

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591 stars(GitHub 591 + cccskills 0)

Run score Not tested

See how to install

Files included (1)

  • SKILL.md3.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591Updated Apr 29, 2026

Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591Updated Apr 29, 2026

Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591Updated Apr 29, 2026

Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591Updated Apr 29, 2026

Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591Updated Apr 29, 2026

Capture a session summary — what was done, what decisions were made, and what to do next.

The English summary is being prepared. Showing the original description.

sharpdeveye/maestro591Updated Apr 29, 2026

See all skills by sharpdeveye

Report a problem with this skill