← Back to Skills Marketplace
zurbrick

Agent QA Gates

by Don Zurbrick · GitHub ↗ · v1.2.0 · MIT-0
cross-platform ✓ Security Clean
319
Downloads
0
Stars
1
Active Installs
3
Versions
Install in OpenClaw
/install agent-qa-gates
Description
Output validation gates for AI agent systems. Prevents hallucinated data, leaked internal context, wrong formats, duplicate sends, post-compaction drift, and...
README (SKILL.md)

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, \x3C2 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?

Isolated Agent / Cron Output (real-world data)

For any cron or sub-agent that reports external data without orchestrator review:

  • Did the agent make a verifiable live tool call? Is the raw response traceable?
  • Any names, dates, amounts, or IDs that can't be traced to a tool result? → 🔴 BLOCK
  • If tool call failed: output must be DATA_UNAVAILABLE — [reason], not fabricated data
  • Does the cron prompt include the Real-World Data Verification Rule? Severity: Fabricated real-world data = 🔴 BLOCK. Same as hallucinated metrics.

Delegated Work Acceptance

For any non-trivial delegated task (especially builds, audits, config changes, or external deliverables):

  • Does the handoff include a clear artifact path or proof object?
  • Did the worker report exact commands run rather than vague claims?
  • Did verification actually happen, with results stated?
  • Is the output non-empty and specific, not just "done" or "completed successfully"?
  • Are known gaps / next actions named explicitly?
  • If the handoff is empty, artifact-free, or self-certifying without proof → 🔴 BLOCK
  • Valid dispositions: Done, Revision Needed, Blocked, Failed, Stale

Silent Worker / Stale Task Classification

For delegated work that appears to be running:

  • Was the spawn actually accepted? If not, it is not running.
  • No start signal within 10 minutes after accepted spawn → Stale
  • No materially new output for 30 minutes on active work → Stale unless the task explicitly justifies a longer quiet window
  • Stale work must be investigated, respawned, or escalated — never left as indefinite In Progress

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.

Usage Guidance
This skill is internally consistent and appears safe to review/use, but take these precautions before installing or running it: (1) the qa-check.sh script reads whatever file or stdin you pass it — do not feed it sensitive files you don't want inspected or echoed; (2) the script looks for secret-like patterns and may produce false positives — verify flagged items manually; (3) it uses standard shell tools (bash, grep, awk) so run it in a controlled environment if you are uncertain; (4) the skill does not request credentials or network access, but avoid giving it broad file paths or automating it with elevated privileges until you’ve tested it on non-production data; (5) if you integrate it into autonomous agent pipelines, ensure gate behavior and blocking semantics match your operational safety policies.
Capability Analysis
Type: OpenClaw Skill Name: agent-qa-gates Version: 1.2.0 The 'agent-qa-gates' skill is a defensive utility designed to improve the reliability and security of AI agent outputs. It provides a structured validation framework (SKILL.md and references/gates-detail.md) and an automated bash script (scripts/qa-check.sh) that scans for common issues such as leaked API keys, PII patterns, internal system context leaks, and hallucinated data. The logic is entirely focused on quality control and preventing accidental data exposure, with no evidence of malicious intent or unauthorized capabilities.
Capability Assessment
Purpose & Capability
Name/description match the provided assets: SKILL.md describes QA gate checklists and the repo contains a reference doc and a shell script that implements checks. The skill does not request unrelated credentials, binaries, or configuration paths.
Instruction Scope
SKILL.md limits itself to output-validation guidance and points to an automation script. The script operates on a provided file or stdin and looks for placeholders, secrets patterns, length/format issues, internal-context keywords, and basic code checks. It does not instruct the agent to read arbitrary system state, other skills' configs, or transmit data externally.
Install Mechanism
There is no install spec (instruction-only with a bundled script). That is low-risk. The included Bash script uses standard POSIX utilities (bash, grep, awk) but no downloaded code or external URLs.
Credentials
The skill declares no required environment variables or credentials. The script actively scans content for secret-like patterns (e.g., sk-..., AKIA..., ghp_...) but does not request or store any secrets itself.
Persistence & Privilege
The skill does not request persistent/always-on privileges (always:false), and it does not modify other skills or system-wide settings. Autonomous invocation is allowed by platform default but not elevated by this skill.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install agent-qa-gates
  3. After installation, invoke the skill by name or use /agent-qa-gates
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.2.0
Add delegated-work acceptance gates, empty-success rejection, and stale-task classification for non-trivial delegated work
v1.1.0
Add Isolated Agent/Cron Output gate to gates-detail.md and SKILL.md quick reference. Any cron or sub-agent delivering real-world data (bookings, email, health, finance) without orchestrator review must have a live tool call on record — or output DATA_UNAVAILABLE. Fabricated external data is classified as 🔴 BLOCK severity.
v1.0.0
Initial release: tiered validation gates, protocol gates, severity classification, qa-check.sh automation script
Metadata
Slug agent-qa-gates
Version 1.2.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 3
Frequently Asked Questions

What is Agent QA Gates?

Output validation gates for AI agent systems. Prevents hallucinated data, leaked internal context, wrong formats, duplicate sends, post-compaction drift, and... It is an AI Agent Skill for Claude Code / OpenClaw, with 319 downloads so far.

How do I install Agent QA Gates?

Run "/install agent-qa-gates" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Agent QA Gates free?

Yes, Agent QA Gates is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Agent QA Gates support?

Agent QA Gates is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Agent QA Gates?

It is built and maintained by Don Zurbrick (@zurbrick); the current version is v1.2.0.

💬 Comments