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.
git clone --depth 1 https://github.com/AlexFischman/mcp-skill-creator-agencyWrote 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/alexfischman/mcp-skill-creator-agency/qa-tester)<a href="https://agentmods.dev/agents/alexfischman/mcp-skill-creator-agency/qa-tester"><img src="https://agentmods.dev/badge/agents/alexfischman/mcp-skill-creator-agency/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/alexfischman/mcp-skill-creator-agency/qa-tester"><img src="https://agentmods.dev/badge/agents/alexfischman/mcp-skill-creator-agency/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 5.5 | $0.00007 | $0.00518 |
| Sonnet 5.5 | $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 1mo 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.
This is a copy
100% identical to qa-tester — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 179 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_completion("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_completion(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.
- 1mo ago First seen · 179 lines · 17 tokens per session scan A 1d3427ad6689
qa-tester is an agent published in the GitHub repository AlexFischman/mcp-skill-creator-agency (2 stars, last pushed 11mo ago), licensed MIT. It adds 17 tokens to every session and 1,296 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to qa-tester, differing in 7 lines, and is treated as a copy.
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