| name | test-team |
| description | Agent team coordination test — research-analyst gathers Kelly Criterion info, python-specialist implements it. Validates sequential agent coordination via tasks/results/ handoff. |
| command | /test-team |
| allowed-tools | ["Bash","Read","Write"] |
| user-invocable | true |
/test-team — Agent Team Coordination Test
Direct coordination test between agents using Agent Teams.
Demonstrates that the output of one agent feeds directly into the next.
Team
Task: Implement Kelly Criterion in Python based on prior research.
Agent 1 — research-analyst (first)
Researches the Kelly Criterion and produces a structured summary with:
- Exact mathematical formula:
f* = (bp - q) / b
where b = net odds, p = win probability, q = 1 - p
- Input parameters and their valid ranges
- Half-Kelly variant (f* / 2) and when to prefer it
- Use cases in systematic trading (position sizing)
- Known limitations (sensitivity to p estimation)
Writes result to: tasks/results/research-kelly-[timestamp].md
Agent 2 — python-specialist (after Agent 1)
Reads the research-analyst result from tasks/results/research-kelly-*.md
and based on it implements:
def kelly_criterion(win_prob: float, win_loss_ratio: float, half_kelly: bool = True) -> float:
"""
Calculates the optimal position size according to Kelly Criterion.
"""
Writes result to: tasks/results/python-kelly-[timestamp].md
Coordination protocol
research-analyst → tasks/results/research-kelly-*.md
↓
python-specialist reads that file → implements function
The python-specialist does NOT start until research-analyst has written its result.
Execution
Launch both agents as a coordinated team. When done, show:
- Research summary (formula + parameters)
- Implemented Python code
- Confirmation:
[TEAM] Kelly Criterion — research + impl complete