📦 @goodandready/dsh-context-lens

Semantic AST Code Skeletonizer & Test Log Condenser for DeepSeek Harness

🇬🇧 English • 🇷🇺 Русский • 🇨🇳 中文说明

⚡ Overview & Semantic Compression Focus

Note on DSH Core vs Plugin Responsibility:
Context budgeting, token counting, tool result pruning (head/middle/tail), and spilling long results to disk (dsh-spill-policy, dsh-compaction-tool-result-pruner, dsh-token-meter) are natively managed by the DSH core (>= 0.1.5).
dsh-context-lens focuses strictly on what the core does not do — semantic compression:

1. AST Code Skeletons: Signatures, interfaces, exported types, and doc comments instead of whole function/method bodies across 9+ languages.
2. Path-Based Focus: Full code for active editing targets while surrounding workspace files remain skeletonized.
3. Test & Build Log Compression: Failures, errors, stack traces, and test summary instead of raw multi-megabyte terminal streams.

graph LR
    subgraph RawContext [Raw Context Streams]
        Code[📁 Source Code: Lengthy File Bodies] --> Lens[dsh-context-lens Semantic Engine]
        Logs[📋 Test/Build Output: Verbose Stream] --> Lens
    end

    subgraph Lens [Semantic Processing]
        Lens --> Focus{Path Focus Check}
        Focus -->|File in Focus| FullCode[Retain Full Implementation]
        Focus -->|Surrounding Files| AST[AST Skeletonizer: Signatures & Types]
        Lens --> LogCompress[Log Condenser: Errors, Stack & Summary]
    end

    subgraph Output [Optimized Context]
        FullCode --> Agent[🤖 AI Agent Context]
        AST --> Agent
        LogCompress --> Agent
        Agent --> Core[⚙️ DSH Core Pruner & Spill Policy]
    end

    style RawContext fill:#1e1e2e,stroke:#89b4fa,stroke-width:2px,color:#cdd6f4
    style Lens fill:#181825,stroke:#cba6f7,stroke-width:2px,color:#cdd6f4
    style Output fill:#11111b,stroke:#a6e3a1,stroke-width:2px,color:#cdd6f4

✨ Features

1. 🧬 Multi-Language AST Structural Skeletonizer

• Supported Languages: TypeScript, JavaScript, Python, Go, C/C++, Rust, Java, and SQL DDL.
• Structural Preservation: Retains imports, classes, structs, interfaces, exported types, function signatures, and doc comments while discarding inner implementation bodies.
• Multiline Signature Support: Seamlessly accumulates complex multiline generic arguments, return types, and parameter lists up to block delimiters.
• Pure Regex Implementation: Zero heavy native dependencies or binary parser overhead; runs lightning-fast across any platform.

2. 📋 Intelligent Test & Build Log Condenser

• Supported Test Runners & Tools: Jest, Vitest, Pytest, node --test, Go test, Cargo, Webpack, Vite, TSC, Maven, Gradle.
• Targeted Extraction: Identifies and preserves critical error messages, stack traces, assertion differences (Expected ... Received ...), and failure context windows.
• Successful Run Summarization: Automatically collapses passing runs (0 failures) into concise summary lines.
• Auto-Mode Discrimination: Evaluates command context and text signatures to ensure normal non-test outputs are preserved untouched for core pruning.
• 3 Aggressiveness Modes:
  • raw: Removes noise lines while keeping general execution order.
  • balanced: Preserves failure sections with surrounding context windows (default).
  • aggressive: Extracts strictly error lines and stack frames.

3. 🎯 Path Focus Management

• Designate specific files or folders as active working targets for the current task.
• Files in focus bypass AST skeletonization, returning complete implementation code.
• Focus state is strictly scoped per session (sessionId).

🛠️ Agent Tools Reference (Consolidated to 2 Tools)

All tools strictly conform to the DeepSeek Harness tool specification by providing output.render returning structured ContentBlock[] arrays ([{ type: 'text', text: ... }]), ensuring 100% session stability.

⚙️ Configuration Schema

Configured via the DSH Settings page (plugins.row.config / plugins.item):

📄 License

MIT © GooDAnDReaDY