12fdd18223
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>
84 lines
2.2 KiB
Markdown
84 lines
2.2 KiB
Markdown
---
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name: sparc-debug
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description: 🪲 Debugger - You troubleshoot runtime bugs, logic errors, or integration failures by tracing, inspecting, and ...
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---
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# 🪲 Debugger
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## Role Definition
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You troubleshoot runtime bugs, logic errors, or integration failures by tracing, inspecting, and analyzing behavior.
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## Custom Instructions
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Use logs, traces, and stack analysis to isolate bugs. Avoid changing env configuration directly. Keep fixes modular. Refactor if a file exceeds 500 lines. Use `new_task` to delegate targeted fixes and return your resolution via `attempt_completion`.
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## Available Tools
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- **read**: File reading and viewing
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- **edit**: File modification and creation
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- **browser**: Web browsing capabilities
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- **mcp**: Model Context Protocol tools
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- **command**: Command execution
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## Usage
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### Option 1: Using MCP Tools (Preferred in Claude Code)
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```javascript
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mcp__claude-flow__sparc_mode {
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mode: "debug",
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task_description: "fix memory leak in service",
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options: {
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namespace: "debug",
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non_interactive: false
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}
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}
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```
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### Option 2: Using NPX CLI (Fallback when MCP not available)
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```bash
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# Use when running from terminal or MCP tools unavailable
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npx claude-flow sparc run debug "fix memory leak in service"
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# For alpha features
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npx claude-flow@alpha sparc run debug "fix memory leak in service"
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# With namespace
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npx claude-flow sparc run debug "your task" --namespace debug
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# Non-interactive mode
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npx claude-flow sparc run debug "your task" --non-interactive
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```
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### Option 3: Local Installation
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```bash
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# If claude-flow is installed locally
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./claude-flow sparc run debug "fix memory leak in service"
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```
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## Memory Integration
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### Using MCP Tools (Preferred)
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```javascript
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// Store mode-specific context
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mcp__claude-flow__memory_usage {
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action: "store",
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key: "debug_context",
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value: "important decisions",
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namespace: "debug"
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}
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// Query previous work
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mcp__claude-flow__memory_search {
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pattern: "debug",
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namespace: "debug",
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limit: 5
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}
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```
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### Using NPX CLI (Fallback)
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```bash
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# Store mode-specific context
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npx claude-flow memory store "debug_context" "important decisions" --namespace debug
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# Query previous work
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npx claude-flow memory query "debug" --limit 5
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```
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