Files

jgrusewski 12fdd18223 refactor: remove entire CPU training path — 5,307 lines of dead code

Deleted:
- DQN::compute_loss_internal (280 lines) — old Candle forward+loss
- DQN::train_step (55 lines) — old Candle training step
- DQN::compute_gradients (47 lines) — old gradient accumulation
- ComputeLossResult struct — only used by deleted functions
- RegimeConditionalDQN::train_step (65 lines) — old dispatch
- RegimeConditionalDQN::train_step_gpu_regime (100 lines) — old GPU path
- RegimeConditionalDQN::compute_gradients_gpu (130 lines) — old regime gradients
- RegimeConditionalDQN::compute_gradients (92 lines) — old dispatch
- DQNAgentType::train_step dispatch — dead
- DQNAgentType::compute_gradients dispatch — dead
- GpuDqnTrainer::upload_batch (71 lines) — old CPU→GPU upload
- train_step.rs (500 lines) — entire module including ensure_fused_ctx
- dqn_benchmark.rs — used old train_step
- examples.rs — used old train_step
- validation/adapters.rs (289 lines) — used old train_step
- dqn/trainable_adapter.rs — used old train_step
- gpu_smoketest.rs — tested old train_step
- Gradient accumulation path in training_loop.rs (144 lines)
- IQN d_h_s2().clone() → raw pointer (zero alloc)
- Causal intervention format! string alloc removed
- Dead HER relabel functions (320 lines)

Kept:
- ensure_fused_ctx logic inlined into training_loop.rs
- set_noise_sigma_scale re-added to RegimeConditionalDQN

Fixed:
- GpuReplayBuffer max_batch_size wired from batch_size parameter
  (was hardcoded 1024, blocking batch_size=8192)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-02 09:06:23 +02:00

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name, description

name	description
sparc-sparc	⚡️ SPARC Orchestrator - You are SPARC, the orchestrator of complex workflows. You break down large objectives into delega...

⚡️ SPARC Orchestrator

Role Definition

You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.

Custom Instructions

Follow SPARC:

Specification: Clarify objectives and scope. Never allow hard-coded env vars.
Pseudocode: Request high-level logic with TDD anchors.
Architecture: Ensure extensible system diagrams and service boundaries.
Refinement: Use TDD, debugging, security, and optimization flows.
Completion: Integrate, document, and monitor for continuous improvement.

Use new_task to assign:

spec-pseudocode
architect
code
tdd
debug
security-review
docs-writer
integration
post-deployment-monitoring-mode
refinement-optimization-mode
supabase-admin

Tool Usage Guidelines:

Always use apply_diff for code modifications with complete search and replace blocks
Use insert_content for documentation and adding new content
Only use search_and_replace when absolutely necessary and always include both search and replace parameters
Verify all required parameters are included before executing any tool

Validate: ✅ Files < 500 lines ✅ No hard-coded env vars ✅ Modular, testable outputs ✅ All subtasks end with attempt_completion Initialize when any request is received with a brief welcome mesage. Use emojis to make it fun and engaging. Always remind users to keep their requests modular, avoid hardcoding secrets, and use attempt_completion to finalize tasks. use new_task for each new task as a sub-task.

Available Tools

Usage

Option 1: Using MCP Tools (Preferred in Claude Code)

mcp__claude-flow__sparc_mode {
  mode: "sparc",
  task_description: "orchestrate authentication system",
  options: {
    namespace: "sparc",
    non_interactive: false
  }
}

Option 2: Using NPX CLI (Fallback when MCP not available)

# Use when running from terminal or MCP tools unavailable
npx claude-flow sparc run sparc "orchestrate authentication system"

# For alpha features
npx claude-flow@alpha sparc run sparc "orchestrate authentication system"

# With namespace
npx claude-flow sparc run sparc "your task" --namespace sparc

# Non-interactive mode
npx claude-flow sparc run sparc "your task" --non-interactive

Option 3: Local Installation

# If claude-flow is installed locally
./claude-flow sparc run sparc "orchestrate authentication system"

Memory Integration

Using MCP Tools (Preferred)

// Store mode-specific context
mcp__claude-flow__memory_usage {
  action: "store",
  key: "sparc_context",
  value: "important decisions",
  namespace: "sparc"
}

// Query previous work
mcp__claude-flow__memory_search {
  pattern: "sparc",
  namespace: "sparc",
  limit: 5
}

Using NPX CLI (Fallback)

# Store mode-specific context
npx claude-flow memory store "sparc_context" "important decisions" --namespace sparc

# Query previous work
npx claude-flow memory query "sparc" --limit 5

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