SkillHub

agent-qa-gates

v1.0.0

Output validation gates for AI agent systems. Prevents hallucinated data, leaked internal context, wrong formats, duplicate sends, and post-compaction drift. Use when building or operating an agent that delivers output to humans or external systems. Provides a tiered gate system (internal → user-fac...

Sourced from ClawHub, Authored by Don Zurbrick

Installation

Please help me install the skill `agent-qa-gates` from SkillHub official store. npx skills add zurbrick/agent-qa-gates

Agent QA Gates

A field-tested validation system for AI agent output. Born from production failures, not theory.

Quick Start

Before any agent delivers output, run the Pre-Ship Checklist:

  1. Accurate? — every number/date/metric has a source. Unsourced → prefix "estimated"
  2. Complete? — no missing pieces, no "I'll do that next"
  3. Actionable? — ends with clear next step or decision point
  4. Fits the channel? — check character limits for your delivery surface
  5. No leaks? — no internal context, private data, or secrets
  6. Not a duplicate? — verify no recent identical send
  7. Would the human be embarrassed? — if yes, don't ship

Gate Tiers

Four ascending tiers by risk level:

Gate Scope Key Checks
Gate 0 Internal (files, config, memory) Mechanism changed not just text, no placeholders, file exists
Gate 1 Human-facing (briefings, summaries) Key info in first 2 lines, ≤3-line paragraphs, channel length limits
Gate 2 External (email, public content, client materials) No internal context leaked, recipient-appropriate tone, dedup check
Gate 3 Code & technical Builds clean, no secrets in code, error handling, tests pass

See references/gates-detail.md for full gate checklists.

Severity Classification

Not all failures are equal:

  • 🔴 BLOCK — cannot ship (secrets, privacy, hallucinated data, wrong recipient)
  • 🟡 FIX — fix before shipping, <2 min (formatting, too long, missing citation)
  • 🟢 NOTE — log and ship (style preference, minor optimization)

Protocol Gates

Recurring failure modes need dedicated gates. These are the most common:

Heartbeat / Periodic Check Output

  • Binary output: alert text ONLY or status-OK ONLY. Never mixed.
  • Every data point verified by current-session tool call. No hallucinated metrics.
  • No stale data from previous cycles or pre-compaction sessions.

Post-Compaction / Context Reset

  • Do not trust facts from the pre-reset session — verify from files and tools.
  • Rerun pending checks from scratch.
  • Zero carryover for periodic checks.

Scheduled Job / Cron Changes

  • Explicit timeout set
  • Explicit model set
  • Verify schedule after creation
  • Output fits destination channel limits

Sub-Agent Output Review

  • Does output match the brief's success criteria?
  • Any uncertainty flags unresolved?
  • Is the reasoning (not just the conclusion) sound?

Gate Evolution

Gates should evolve based on real failures, not imagination:

  1. When a failure occurs → log it with root cause
  2. Same failure class occurs 2+ times → add a gate item
  3. Monthly: prune gates that haven't caught anything in 60 days

Anti-Patterns

  • Gates that sound good but never catch anything → kill them
  • Per-agent checklists that duplicate general gates → merge or reference
  • "ADHD-friendly" or "high-quality" as gate items → not testable, replace with mechanical checks
  • Aspirational gates nobody runs → either automate or cut

Adapting to Your System

This skill provides the pattern. Adapt it:

  1. Start with the Pre-Ship Checklist — it works for any agent system
  2. Add Protocol Gates for your top 3 recurring failure modes
  3. Set channel limits for your delivery surfaces
  4. Map real failures to gates — if a failure isn't gated, add the gate
  5. Kill gates that never fire — a shorter, sharper checklist wins

For the full reference implementation, see references/gates-detail.md. For automation scripts, see scripts/qa-check.sh.