agent-qa

agent-qa is a skill for Claude Code, Codex from Artesiana/agent2. It costs 70 tokens per session (1,061 once invoked), scanned A, original, MIT.

An end-to-end testing skill for Agent2 agents, which are software agents that run as services and answer API requests. It starts the service, sends test requests, checks the response format, and creates a health report.

In plain words
What is it for?
Testing newly created or changed agents, using evaluation datasets, generating missing test cases, validating response schemas, and reporting health and confidence results.
Why use it?
It checks whether an agent works in realistic use instead of relying only on unit tests or code inspection.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/artesiana/agent2/agent-qa
Any agent
npx skills add Artesiana/agent2 --skill agent-qa
Clone the repo
git clone --depth 1 https://github.com/Artesiana/agent2

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-qa

README.md
[![agentmods](https://agentmods.dev/badge/skills/artesiana/agent2/agent-qa.svg)](https://agentmods.dev/skills/artesiana/agent2/agent-qa)
Your own site
<a href="https://agentmods.dev/skills/artesiana/agent2/agent-qa"><img src="https://agentmods.dev/badge/skills/artesiana/agent2/agent-qa.svg" alt="Measured on agentmods" height="20"></a>
Per session 70 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,061 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00070 $0.01061
Opus 5 $0.00035 $0.00531
Sonnet 5 $0.00014 $0.00212
Haiku 4.5 $0.00007 $0.00106

Measured 5d ago against content hash e3ad95cb9e91, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-qa scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s "http://localhost:$AGENT_PORT/health" 2>/dev/null && echo "ALREADY_RUNNING" || echo "NEEDS_START"
.agents/skills/agent-qa/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent QA

End-to-end testing for a deployed or locally running Agent2 agent.

When to Use

  • After generating a new agent with /brain-clone or /creating-agents
  • After changing an agent's prompt, tools, or schema
  • Before shipping an agent to production
  • When debugging unexpected agent behavior

Workflow

Step 1: Identify the agent

Ask which agent to test if not obvious from context:

ls agents/*/config.yaml 2>/dev/null | sed 's|agents/||;s|/config.yaml||'

Step 2: Load test cases

Check for existing eval datasets:

AGENT_NAME="<agent-name>"
EVAL_FILE="tests/promptfoo/$AGENT_NAME/dataset.json"
if [ -f "$EVAL_FILE" ]; then
  echo "EVAL_FOUND: $EVAL_FILE"
  cat "$EVAL_FILE"
else
  echo "NO_EVAL_DATASET"
fi

If NO_EVAL_DATASET: generate test cases from the agent's example cases in config.yaml and the prompt in agent.py. Create at least 3 cases:

  1. A case that should return the primary complete outcome
  2. An empty/incomplete case that should trigger needs_clarification
  3. A case with a defect that should trigger rejected (if applicable)

Step 3: Start the agent

# Check if already running
AGENT_PORT=$(grep "port:" "agents/$AGENT_NAME/config.yaml" | awk '{print $2}')
curl -s "http://localhost:$AGENT_PORT/health" 2>/dev/null && echo "ALREADY_RUNNING" || echo "NEEDS_START"

If NEEDS_START:

uv run agent2 serve "$AGENT_NAME" &
AGENT_PID=$!
sleep 3
curl -s "http://localhost:$AGENT_PORT/health"

Step 4: Run test cases

For each test case, send a real POST /tasks request:

curl -s -X POST "http://localhost:$AGENT_PORT/tasks?mode=sync" \
  -H "Authorization: Bearer dev-token-change-me" \
  -H "Content-Type: application/json" \
  -d '{"input": <test-case-input>}'

For each response, validate:

  1. HTTP status is 200
  2. Response contains "status": "completed"
  3. result is present and contains the expected schema fields
  4. result.status matches expected outcome (if specified in test case)
  5. result.confidence is a number between 0 and 1
  6. result.reasoning is non-empty
  7. result.review_steps is a non-empty list
  8. Schema consistency: no contradictory fields (e.g., complete + rejection_reason)

Read the full file on GitHub · 141 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 141 lines · 70 tokens per session scan A e3ad95cb9e91

Subscribe to this mod's changes

agent-qa is a skill published in the GitHub repository Artesiana/agent2 (36 stars, last pushed 4mo ago), licensed MIT. It adds 70 tokens to every session and 1,061 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.