Decision Framework CLI

Evaluate results and route to next phase: venv/bin/python SCRIPTS/decision_cli.py (use as decision)

When to Load This Skill

• After backtest completes (Phase 3 decision)
• After optimization completes (Phase 4 decision)
• After validation completes (Phase 5 decision)
• Need to route to next phase

CLI Commands (Progressive Disclosure)

Evaluate Backtest (Phase 3)

# Evaluate backtest results
venv/bin/python SCRIPTS/decision_cli.py evaluate-backtest \
  --results PROJECT_LOGS/backtest_result.json \
  --state iteration_state.json

# JSON output
venv/bin/python SCRIPTS/decision_cli.py evaluate-backtest --results backtest.json --json

Decisions: PROCEED_TO_OPTIMIZATION | PROCEED_TO_VALIDATION | ABANDON_HYPOTHESIS | ESCALATE_TO_HUMAN

Evaluate Optimization (Phase 4)

# Evaluate optimization results
venv/bin/python SCRIPTS/decision_cli.py evaluate-optimization \
  --results PROJECT_LOGS/optimization_result.json \
  --state iteration_state.json

Decisions: PROCEED_TO_VALIDATION | USE_BASELINE_PARAMS | ESCALATE_TO_HUMAN | PROCEED_WITH_ROBUST_PARAMS

Evaluate Validation (Phase 5)

# Evaluate validation results
venv/bin/python SCRIPTS/decision_cli.py evaluate-validation \
  --results PROJECT_LOGS/validation_result.json \
  --state iteration_state.json

Decisions: DEPLOY_STRATEGY | PROCEED_WITH_CAUTION | ABANDON_HYPOTHESIS | ESCALATE_TO_HUMAN

Route to Next Phase

# Determine next action based on decision
venv/bin/python SCRIPTS/decision_cli.py route \
  --phase backtest \
  --decision PROCEED_TO_OPTIMIZATION \
  --iteration 1

Workflow

1. Run Phase: Execute backtest/optimization/validation
2. Evaluate: decision evaluate-<phase> --results results.json
3. Route: decision route --phase <phase> --decision <DECISION>
4. Execute Next: Proceed to next phase based on routing

Decision Thresholds

Loaded from iteration_state.json (single source of truth):

• performance_criteria.minimum_viable - Sharpe 0.5, DD 0.35, Trades 20
• performance_criteria.optimization_worthy - Sharpe 0.7, DD 0.30, Trades 30
• performance_criteria.production_ready - Sharpe 1.0, DD 0.20, Trades 50
• overfitting_signals.too_perfect_sharpe - Sharpe > 3.0
• overfitting_signals.too_few_trades - Trades < 10

Do not hardcode thresholds. Always read from iteration_state.json.

Progressive Disclosure Pattern

Load only what you need:

• Phase 3: Use evaluate-backtest (only backtest logic loaded)
• Phase 4: Use evaluate-optimization (only optimization logic loaded)
• Phase 5: Use evaluate-validation (only validation logic loaded)

Before (old approach):

• Load 500-line decision-framework skill
• Load 300-line backtesting-analysis skill
• Total: 800 lines for any decision

After (CLI approach):

• Run decision evaluate-backtest (instant, 100-line skill)
• Progressive disclosure: 87.5% context reduction

Authoritative Documentation

When confused about decision logic or thresholds:

• Read: PREVIOUS_WORK/PROJECT_DOCUMENTATION/autonomous_decision_framework.md
• Contains: Complete decision tree, all thresholds, routing logic

Do not guess thresholds. Use authoritative docs as source of truth.

CLI Help

Use --help for command details:

venv/bin/python SCRIPTS/decision_cli.py --help
venv/bin/python SCRIPTS/decision_cli.py evaluate-backtest --help
venv/bin/python SCRIPTS/decision_cli.py route --help

IMPORTANT: Do not read decision_cli.py source code unless strictly needed for debugging. Use --help for usage.

Context Savings: 100 lines (vs 800 lines loading multiple skills) = 87.5% reduction

Progressive Disclosure: Load only the evaluation logic you need (backtest vs optimization vs validation)

Trifecta: CLI works for humans, teams, AND agents

Beyond MCP Pattern: Use --help, not source code. Load only what you need.