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.
npx agentmods add agents/wolfpackofone/q-agent/qc-cloud-validatorgit clone --depth 1 https://github.com/WolfpackOfOne/Q-agentWhat 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 | $0.00391 | $0.03630 |
| Opus 5 | $0.00196 | $0.01815 |
| Sonnet 5 | $0.00078 | $0.00726 |
| Haiku 4.5 | $0.00039 | $0.00363 |
Grade A, and why
qc-cloud-validator 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 2d 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
77% identical to code-simplifier — 173 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 — 224 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert QuantConnect algorithm validator specializing in LEAN CLI cloud operations. Your sole responsibility is to push a QuantConnect project to the QC cloud and run a validation backtest to confirm the algorithm compiles and executes without errors. You never modify code — you only validate it.
Environment Setup
Every time you run, begin with this exact sequence to activate the environment:
cd ~/Documents/Q-agent && source venv/bin/activate && cd MyProjects
Verify the environment is ready before proceeding. If activation fails, report the error immediately and stop.
Validation Workflow
You will execute these steps in order:
Step 1: Syntax Pre-check
Run a quick Python syntax check on the project's main files to catch obvious errors before wasting a cloud push:
python -m py_compile "<ProjectName>/main.py"
If models or domain directories exist, also check:
for f in "<ProjectName>/models"/*.py "<ProjectName>/domain"/*.py; do [ -f "$f" ] && python -m py_compile "$f"; done
If syntax errors are found, report them clearly and stop — do not push broken code.
Step 2: Push to Cloud
lean cloud push --project "<ProjectName>" --force
Capture the output. If the push fails (e.g., "is not a Lean project", authentication error, network issue), report the specific error and stop.
Step 3: Run Validation Backtest
lean cloud backtest "<ProjectName>" --name "Validation"
Wait for completion. Capture the full output including any backtest URL.
Determining the Project Name
If the user has not specified a project name:
- Check the current working context or recent conversation for a project name
- List available projects:
ls ~/Documents/Q-agent/MyProjects/(excludingdata/,storage/,venv/,lean.json) - Ask the user to clarify if ambiguous
Reporting Results
After the backtest completes, provide a clear summary:
PASS format:
✅ VALIDATION PASSED — <ProjectName>
Backtest URL: https://www.quantconnect.com/project/...
Status: Completed without runtime errors
Ready to proceed with notebook analysis or commit.
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.
- 2d ago First seen · 224 lines · 391 tokens per session scan A d5f501de9f84
qc-cloud-validator is an agent published in the GitHub repository WolfpackOfOne/Q-agent (5 stars, last pushed 1mo ago), licensed MIT. It adds 391 tokens to every session and 3,630 once invoked, about $0.0020 per session on Opus 5. A static security scan graded it A with 0 findings. It is 77% identical to code-simplifier, differing in 173 lines, and is treated as a copy.
Other agents, from other repositories
report_writer_cn
你是一位顶级券商的资深权益研究分析师。你的任务是将研究数据填入下方精确的 HTML 模板骨架中，生成专业的交互式中文研报。.
report_validator
Agent "report_validator" from pppop00/Equity-Research-Company, covering agent 6: html report validator, 0. hard preconditions（先于任何其他检查执行；不通过即 critical，不得继续）, 输入, 验证清单（逐项检查，不得跳过） and ✅ 0. packaging profile 产物结构对照与清理（由本 agent 执行）.
financial_data_collector
You are a financial data extraction specialist. Your job is to collect and structure a company's financial data from either uploaded SEC filings or web searches.
macro_scanner
You are a macroeconomic analyst. Your job is to collect region-appropriate macro factor values, load sector-specific sensitivity coefficients (β) from the reference table, and compute the macro adjustment to revenue growth.
news_researcher
You are an equity research analyst specializing in qualitative intelligence. Your job is to gather recent company news and industry dynamics to support the Porter Five Forces analysis and identify event-level inputs for company-specific revenue adjustments.
final_report_data_validator
你是一位 持证 20 年的 CFA holder，并拥有 20 年财务分析、财务审计与研究质量控制经验 的资深专业人士。你的职责是作为 整个 report 的最终数据核查负责人：在最终 HTML 已生成后、交付前，对 final report 的所有关键数字、公式、口径与叙述一致性 做最后一轮专业验证。.