How do I install the Strategy Tournament skill?

Run npx skillmds@latest add knuckles-team/strategy-tournament in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Strategy Tournament safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Strategy Tournament?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Strategy Tournament free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Strategy Tournament?

Knuckles-Team (@knuckles-team) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Strategy Tournament by knuckles-team

Name: Strategy Tournament
Author: Knuckles-Team

File contents

Strategy Tournament Workflow

CONCEPT:EE-011

Performs concurrent backtests across 10 distinct algorithmic strategies, evaluates risk-adjusted metrics, ranks return profiles, and selects the top models.

Steps

Step 1: Backtest Batch Runner

Agent: data-fetcher Tools: graph_query, sx_search

Runs historical backtests concurrently for 10 distinct trading strategies. Expected: batch-backtest-outputs

Step 2: Metric Evaluator [depends_on: backtest-batch-runner]

Agent: compute-engine Tools: graph_analyze

Computes Sharpe, Sortino, Calmar ratios, and max drawdown for each candidate. Expected: strategy-performance-scorecards

Step 3: Ranker Selector [depends_on: metric-evaluator]

Agent: risk-assessor Tools: graph_query, graph_analyze

Ranks strategies and selects the top 3 best-performing models. Expected: winning-strategies-selection

Step 4: Synthesis Compiler [depends_on: ranker-selector]

Agent: report-generator Tools: graph_write, document_tools

Synthesizes results into a unified strategic tearsheet. Expected: tournament-summary-report

Step 5: KG Persistence [depends_on: synthesis-compiler]

Agent: report-generator Tools: graph_write

Persist workflow results as nodes and edges in the Knowledge Graph. Create appropriate typed nodes with metadata and link to existing domain entities.

Output

Strategy Tournament results persisted in KG
Structured report (MD/PDF)
Audit trail with timestamps and agent attributions

Execution

Run this workflow as a dependency-ordered DAG. Steps with no unmet depends_on run in parallel; dependents run after their prerequisites complete.

Run first (in parallel): Step 1 — Backtest Batch Runner
After level 0: Step 2 — Metric Evaluator
After level 1: Step 3 — Ranker Selector
After level 2: Step 4 — Synthesis Compiler
After level 3: Step 5 — KG Persistence

Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.

---name: strategy-tournamentdescription: Performs concurrent backtests across 10 distinct algorithmic strategies, evaluates risk-adjusted metrics, ranks return profiles, and selects the top models.---# Strategy Tournament Workflow**CONCEPT:EE-011**Performs concurrent backtests across 10 distinct algorithmic strategies, evaluates risk-adjusted metrics, ranks return profiles, and selects the top models.## Steps### Step 1: Backtest Batch Runner**Agent**: `data-fetcher`**Tools**: `graph_query, sx_search`Runs historical backtests concurrently for 10 distinct trading strategies.Expected: `batch-backtest-outputs`### Step 2: Metric Evaluator [depends_on: backtest-batch-runner]**Agent**: `compute-engine`**Tools**: `graph_analyze`Computes Sharpe, Sortino, Calmar ratios, and max drawdown for each candidate.Expected: `strategy-performance-scorecards`### Step 3: Ranker Selector [depends_on: metric-evaluator]**Agent**: `risk-assessor`**Tools**: `graph_query, graph_analyze`Ranks strategies and selects the top 3 best-performing models.Expected: `winning-strategies-selection`### Step 4: Synthesis Compiler [depends_on: ranker-selector]**Agent**: `report-generator`**Tools**: `graph_write, document_tools`Synthesizes results into a unified strategic tearsheet.Expected: `tournament-summary-report`### Step 5: KG Persistence [depends_on: synthesis-compiler]**Agent**: `report-generator`**Tools**: `graph_write`Persist workflow results as nodes and edges in the Knowledge Graph.Create appropriate typed nodes with metadata and link to existing domain entities.## Output- Strategy Tournament results persisted in KG- Structured report (MD/PDF)- Audit trail with timestamps and agent attributions## ExecutionRun this workflow as a dependency-ordered DAG. Steps with no unmet `depends_on` run in parallel; dependents run after their prerequisites complete.- **Run first (in parallel):** Step 1 — Backtest Batch Runner- **After level 0:** Step 2 — Metric Evaluator- **After level 1:** Step 3 — Ranker Selector- **After level 2:** Step 4 — Synthesis Compiler- **After level 3:** Step 5 — KG Persistence**Execution:** If graph-os is reachable, offload the whole DAG via `graph_orchestrate action=execute_workflow` (or the `kg-delegate` skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet `depends_on` in parallel, then their dependents.

Frequently asked questions

Strategy Tournament