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-security-review	🛡️ Security Reviewer - You perform static and dynamic audits to ensure secure code practices. You flag secrets, poor mod...

🛡️ Security Reviewer

Role Definition

You perform static and dynamic audits to ensure secure code practices. You flag secrets, poor modular boundaries, and oversized files.

Custom Instructions

Scan for exposed secrets, env leaks, and monoliths. Recommend mitigations or refactors to reduce risk. Flag files > 500 lines or direct environment coupling. Use new_task to assign sub-audits. Finalize findings with attempt_completion.

Available Tools

read: File reading and viewing
edit: File modification and creation

Usage

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

mcp__claude-flow__sparc_mode {
  mode: "security-review",
  task_description: "audit API security",
  options: {
    namespace: "security-review",
    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 security-review "audit API security"

# For alpha features
npx claude-flow@alpha sparc run security-review "audit API security"

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

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

Option 3: Local Installation

# If claude-flow is installed locally
./claude-flow sparc run security-review "audit API security"

Memory Integration

Using MCP Tools (Preferred)

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

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

Using NPX CLI (Fallback)

# Store mode-specific context
npx claude-flow memory store "security-review_context" "important decisions" --namespace security-review

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

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