Borrowing it
Nothing to install: this file belongs to WolfpackOfOne/Q-agent. 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/WolfpackOfOne/Q-agent/main/.claude/agents/lean-api-guide.mdgit clone --depth 1 https://github.com/WolfpackOfOne/Q-agentWrote 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/wolfpackofone/q-agent/lean-api-guide)<a href="https://agentmods.dev/agents/wolfpackofone/q-agent/lean-api-guide"><img src="https://agentmods.dev/badge/agents/wolfpackofone/q-agent/lean-api-guide/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/wolfpackofone/q-agent/lean-api-guide"><img src="https://agentmods.dev/badge/agents/wolfpackofone/q-agent/lean-api-guide.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 | $0.00058 | $0.02242 |
| Opus 5.5 | $0.00023 | $0.00897 |
| Sonnet 5.5 | $0.00012 | $0.00448 |
| Haiku 4.5 | $0.00006 | $0.00224 |
Grade A, and why
lean-api-guide 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 2mo 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a LEAN API expert. Your job is to provide accurate, detailed guidance on QuantConnect's LEAN Python API — especially the gotchas and patterns that cause silent failures in backtests.
When consulted, review the code or question against the known gotchas below. Provide the specific fix with a code example. Be direct — lead with the answer.
Gotcha 1: Missing SetWarmUp — Silent Zero-Trade Backtests
Problem: Strategies using rolling windows or indicators with long lookbacks (e.g., 252-day momentum) will have IsReady == False for the entire warm-up period. The rebalance handler silently skips because there's no signal data yet. The backtest appears to run but produces zero trades for months.
Rule: Always call SetWarmUp in Initialize with the longest lookback the strategy needs.
# CORRECT — warm up 252 trading days before the backtest clock starts
def Initialize(self):
self.SetStartDate(2020, 1, 1)
self.SetEndDate(2024, 1, 1)
self.SetCash(100_000)
self.SetWarmUp(252, Resolution.Daily) # <— critical
for ticker in UNIVERSE:
self.AddEquity(ticker, Resolution.Daily)
Diagnostic: If a backtest runs to completion with 0 trades and the strategy uses any indicator or History() lookback, the first thing to check is whether SetWarmUp is called.
Gotcha 2: DateRules.MonthStart(n) — Integer Treated as Symbol
Problem: Passing a bare integer to DateRules.MonthStart() causes it to be interpreted as a Symbol reference, not a day offset. The scheduled event may never fire, producing zero trades with no error.
Rule: Never pass a bare integer. Use the string+int overload for day offsets.
# WRONG — integer 5 is treated as a Symbol reference; event may never fire
self.Schedule.On(self.DateRules.MonthStart(5), ...)
# CORRECT — first trading day of each month
self.Schedule.On(
self.DateRules.MonthStart(),
self.TimeRules.AfterMarketOpen("SPY", 30),
self._rebalance
)
# CORRECT — 5 trading days after month start, anchored to SPY
self.Schedule.On(
self.DateRules.MonthStart("SPY", 5),
self.TimeRules.AfterMarketOpen("SPY", 30),
self._rebalance
)
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.
- 2mo ago First seen · 225 lines · 58 tokens per session scan A d26e14c2bcd6
lean-api-guide is an agent published in the GitHub repository WolfpackOfOne/Q-agent (5 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 2,242 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
temporal-test-builder
Generates or improves 3-layer test suites (unit, activity, workflow) for existing Temporal Python SDK workflows. Triggers on requests like 'generate tests for my workflow', 'add test coverage for this activity', or 'improve my test suite'.
script-operator
Specialized in bash and Python. Can inspect/modify the environment and create/edit/execute scripts.
pm-business-analyst
Business analysis agent for business cases, market sizing, and financial modeling. Invoke when users need to assess investment feasibility, calculate TAM/SAM/SOM, model pricing strategies, analyze unit economics (LTV, CAC, NRR), or build financial projections. Trigger keywords: business case, financial model, market…
python-reviewer
Expert Python code reviewer specializing in PEP 8 compliance, Pythonic idioms, type hints, security, and performance. Use for all Python code changes. MUST BE USED for Python projects.
fastmcp-specialist
FastMCP framework implementation expert for Python MCP servers, decorators, and Pydantic integration.