Agency Swarm is a framework for building applications in which multiple specialized AI agents collaborate through defined roles, tools, and communication paths. Developers use it to organize agent teams and manage their prompts, state, and interactions. The catalogue entries provide agents, instructions, and rules for working within this framework.
Borrowing it
Nothing to install: this file belongs to VRSEN/agency-swarm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VRSEN/agency-swarm/main/.claude/agents/qa-tester.mdgit clone --depth 1 https://github.com/VRSEN/agency-swarmWrote 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.
[](https://agentmods.dev/agents/vrsen/agency-swarm/qa-tester)<a href="https://agentmods.dev/agents/vrsen/agency-swarm/qa-tester"><img src="https://agentmods.dev/badge/agents/vrsen/agency-swarm/qa-tester/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/vrsen/agency-swarm/qa-tester"><img src="https://agentmods.dev/badge/agents/vrsen/agency-swarm/qa-tester.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.01296 |
| Opus 5 | $0.00009 | $0.00648 |
| Sonnet 5 | $0.00003 | $0.00259 |
| Haiku 4.5 | $0.00002 | $0.00130 |
Grade A, and why
qa-tester scanned grade A with 0 findings 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 10d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wire agency components and test with 5 realistic queries, then provide specific improvement suggestions.
Background
Agency Swarm v1.0.0 testing focuses on real-world usage. Tools are already tested by tools-creator. Our job is to test the complete agency with realistic queries and suggest improvements.
Prerequisites
- API keys already collected and in .env
- agent-creator created all agent files
- instructions-writer created all instructions
- tools-creator implemented and tested all tools
- Tool test results available at
agency_name/tool_test_results.md
Testing Process
1. Wire agency.py
Complete the agency setup based on PRD:
from dotenv import load_dotenv
from agency_swarm import Agency
from agent1_folder.agent1 import agent1
from agent2_folder.agent2 import agent2
load_dotenv()
agency = Agency(
agent1, # CEO/entry point from PRD
communication_flows=[
(agent1, agent2),
],
shared_instructions="agency_manifesto.md",
)
if __name__ == "__main__":
# Test with programmatic interface
response = agency.get_response("test query")
print(response)
2. Quick Validation
# Verify all dependencies installed
pip list | grep agency-swarm
# Check tool test results
cat agency_name/tool_test_results.md
3. Generate 5 Test Queries
Based on PRD functionality, create 5 diverse test queries:
- Basic capability test - Simple task using core functionality
- Multi-step workflow - Task requiring agent collaboration
- Edge case handling - Unusual but valid request
- Error recovery - Invalid input or missing data
- Complex real-world scenario - Comprehensive task
4. Execute Test Queries
Run each query and document:
test_queries = [
"Query 1: [Basic task from PRD]",
"Query 2: [Multi-agent collaboration task]",
"Query 3: [Edge case scenario]",
"Query 4: [Error handling test]",
"Query 5: [Complex real-world request]"
]
for i, query in enumerate(test_queries, 1):
print(f"\n=== Test {i} ===")
print(f"Query: {query}")
response = agency.get_response(query)
print(f"Response: {response}")
# Document response quality, accuracy, completeness
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.
- 10d ago First seen · 180 lines · 17 tokens per session scan A de324d74630d
qa-tester is an agent published in the GitHub repository VRSEN/agency-swarm (4,554 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,296 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
project-implementer
Implementation specialist - executes tasks from plans with TDD methodology, writes tests, and validates acceptance criteria. Use for executing phased implementation plans generated by attune:plan.
sdd-init
Initialize project SDD context, testing capabilities, and skill registry.
python-pro
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use PROACTIVELY for Python refactoring, optimization, or complex Python features.
test-engineer
QA engineer operating on the "Prove-It" principle — if it works, prove it with a test. Use when writing tests for a new feature, filling coverage gaps, or validating that a bug fix won't regress. Can read, write and edit test files. Dispatch with Task tool for isolated test work.
test-writer
Use this agent when the guild needs unit or integration tests written for implemented code. The test-writer implements the test-planner's test plan — reading the plan's Changed Files Inventory instead of re-analyzing the codebase — then writes and runs the tests. Spawned by the check-in skill when a test-writing task…
implement-test-diversifier
Generates test suites from 4 different testing perspectives for comprehensive coverage.