{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hn-47030354","slug":"openslimedit-cut-ai-coding-token-usage-by-21-45-wi","name":"OpenSlimedit – Cut AI coding token usage by 21-45% with zero config","type":"repo","url":"https://github.com/ASidorenkoCode/openslimedit","page_url":"https://unfragile.ai/openslimedit-cut-ai-coding-token-usage-by-21-45-wi","categories":["automation"],"tags":["hackernews","show-hn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hn-47030354__cap_0","uri":"capability://code.generation.editing.intelligent.code.context.pruning.for.llm.prompts","name":"intelligent code context pruning for llm prompts","description":"Analyzes source code files and automatically removes redundant, boilerplate, or semantically irrelevant code segments before sending to LLM APIs, reducing token consumption by 21-45%. Uses AST-aware or heuristic-based filtering to identify and strip comments, unused imports, test fixtures, and low-information-density patterns while preserving syntactic validity and semantic meaning required for code understanding tasks.","intents":["Reduce API costs when sending large codebases to Claude, GPT-4, or other token-metered LLM services","Speed up LLM response latency by shrinking context window requirements","Maintain code quality and correctness while minimizing token overhead in AI-assisted development workflows","Automatically optimize prompt engineering without manual code selection or refactoring"],"best_for":["Solo developers and small teams using paid LLM APIs for code generation or analysis","Organizations with large codebases seeking to reduce AI tooling costs","CI/CD pipelines integrating LLM-based code review or refactoring tools"],"limitations":["Pruning heuristics may remove context critical for domain-specific or highly abstracted code patterns, requiring manual validation","No semantic understanding of business logic — relies on syntactic patterns, so context removal may be suboptimal for non-standard coding styles","Zero-config approach means no fine-tuning per project; aggressive pruning may degrade LLM output quality in edge cases","Effectiveness varies by language and codebase structure; gains of 21-45% are empirical ranges, not guaranteed"],"requires":["CLI environment (shell/bash/zsh/PowerShell)","Source code files in supported languages (likely Python, JavaScript, TypeScript, Go, Rust, Java based on typical AI coding tools)","Integration point: pre-processing step before sending code to LLM API or IDE plugin"],"input_types":["source code files (single or batch)","directory paths or glob patterns","code snippets or full project structures"],"output_types":["pruned source code (same language, reduced token count)","token count metrics (before/after comparison)","structured report of removed segments (optional)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47030354__cap_1","uri":"capability://code.generation.editing.multi.language.code.analysis.and.filtering","name":"multi-language code analysis and filtering","description":"Detects and processes source code across multiple programming languages, applying language-specific rules to identify and remove redundant constructs (unused variables, dead imports, boilerplate patterns) while preserving functional code. Likely uses regex-based pattern matching, lightweight parsing, or language-specific linters integrated as a preprocessing layer to normalize code before LLM ingestion.","intents":["Process polyglot codebases (Python, JavaScript, Go, Rust, etc.) with a single tool without language-specific configuration","Automatically apply language conventions (e.g., Python import cleanup, JavaScript minification-safe removal) to reduce noise","Ensure pruned code remains syntactically valid and executable in its original language"],"best_for":["Developers working with mixed-language projects or microservices","Teams using LLM tools across heterogeneous tech stacks"],"limitations":["Language detection may fail on ambiguous file types or non-standard extensions","Language-specific rules are heuristic-based; may not handle advanced features (macros, generics, DSLs) correctly","No deep semantic analysis per language — relies on pattern matching, so context removal may miss language-specific idioms"],"requires":["Source files with standard language extensions (.py, .js, .ts, .go, .rs, .java, etc.)","CLI environment with file system access"],"input_types":["source code files in Python, JavaScript, TypeScript, Go, Rust, Java, or other supported languages","mixed-language directory structures"],"output_types":["pruned source code in original language","language detection metadata (optional)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47030354__cap_2","uri":"capability://data.processing.analysis.batch.code.processing.and.token.accounting","name":"batch code processing and token accounting","description":"Processes multiple code files or entire directories in a single CLI invocation, computing token counts before and after pruning to quantify savings. Likely uses a token counter (e.g., tiktoken for OpenAI models, or a generic approximation) to measure compression ratio and provide metrics-driven feedback on pruning effectiveness per file or aggregate.","intents":["Measure token savings across an entire codebase before committing to LLM-based workflows","Generate cost-benefit reports showing API savings for different pruning strategies","Batch-process large projects without manual per-file intervention"],"best_for":["Teams evaluating LLM tooling costs before adoption","CI/CD pipelines that need to track token efficiency metrics","Developers optimizing costs for large-scale code analysis tasks"],"limitations":["Token counting is model-specific (OpenAI vs Anthropic vs Ollama have different tokenizers); results may not generalize across LLM providers","Batch processing may be memory-intensive for very large codebases (100k+ files)","Token metrics are estimates; actual API usage may vary based on prompt structure and model behavior"],"requires":["CLI environment with file system access","Optional: API key or local tokenizer for accurate token counting (may default to approximation)"],"input_types":["file paths (single or multiple)","directory paths with recursive traversal","glob patterns for selective file matching"],"output_types":["token count summary (before/after per file and aggregate)","cost savings estimate (if pricing data provided)","pruning effectiveness report (percentage reduction)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47030354__cap_3","uri":"capability://automation.workflow.zero.configuration.automatic.pruning.with.sensible.defaults","name":"zero-configuration automatic pruning with sensible defaults","description":"Operates without requiring configuration files, language-specific settings, or manual tuning — applies a single set of heuristic rules to all code automatically. Likely uses conservative defaults (e.g., remove comments, unused imports, test files) that work across most codebases without degrading code quality, allowing developers to invoke the tool with a single command and immediately see token savings.","intents":["Get started with token optimization in seconds without learning tool-specific configuration syntax","Apply best-practice pruning rules automatically without manual code review or selection","Integrate into existing workflows (IDE, CI/CD, pre-commit hooks) with minimal setup friction"],"best_for":["Individual developers seeking quick wins on LLM API costs","Teams with limited DevOps resources who need plug-and-play optimization","Rapid prototyping scenarios where setup overhead is a blocker"],"limitations":["One-size-fits-all defaults may be suboptimal for specialized codebases (e.g., highly abstracted frameworks, domain-specific languages)","No customization means developers cannot fine-tune pruning aggressiveness per project or file type","Conservative defaults may leave token savings on the table compared to aggressive, tuned strategies","No per-project configuration means all codebases are treated identically regardless of structure or context requirements"],"requires":["CLI environment (shell/bash/zsh/PowerShell)","No configuration files, API keys, or environment variables required for basic operation"],"input_types":["source code files or directories (passed as CLI arguments)"],"output_types":["pruned code and token metrics (printed to stdout or written to files)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hn-47030354__cap_4","uri":"capability://automation.workflow.cli.native.integration.with.llm.workflows","name":"cli-native integration with llm workflows","description":"Designed as a command-line tool that fits into shell pipelines and development workflows, accepting code input via file arguments or stdin and outputting pruned code to stdout or files. Enables seamless integration with existing LLM tools, IDE plugins, and CI/CD systems through standard Unix pipes and file I/O, without requiring SDK installation or language-specific bindings.","intents":["Pipe pruned code directly to LLM APIs or local models via shell commands","Integrate into pre-commit hooks or CI/CD pipelines to optimize code before sending to AI tools","Chain with other CLI tools (grep, sed, awk) for custom preprocessing workflows"],"best_for":["DevOps engineers and infrastructure teams building AI-assisted CI/CD pipelines","Developers comfortable with shell scripting and Unix tooling philosophy","Teams using headless or server-side LLM integrations without IDE plugins"],"limitations":["CLI-only interface means no GUI or IDE plugin support out-of-the-box","Requires shell environment and file system access; not suitable for browser-based or cloud-only workflows","Piping large files through CLI may incur I/O overhead compared to in-process library calls","No built-in error handling for malformed code; relies on downstream tools to validate output"],"requires":["CLI environment (bash, zsh, PowerShell, or equivalent)","File system access for reading source code","Optional: shell scripting knowledge for advanced pipeline integration"],"input_types":["file paths (command-line arguments)","stdin (piped input from other commands)","directory paths with recursive traversal"],"output_types":["stdout (pruned code, suitable for piping)","file output (written to disk or specified paths)","exit codes (for CI/CD integration)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["CLI environment (shell/bash/zsh/PowerShell)","Source code files in supported languages (likely Python, JavaScript, TypeScript, Go, Rust, Java based on typical AI coding tools)","Integration point: pre-processing step before sending code to LLM API or IDE plugin","Source files with standard language extensions (.py, .js, .ts, .go, .rs, .java, etc.)","CLI environment with file system access","Optional: API key or local tokenizer for accurate token counting (may default to approximation)","No configuration files, API keys, or environment variables required for basic operation","CLI environment (bash, zsh, PowerShell, or equivalent)","File system access for reading source code","Optional: shell scripting knowledge for advanced pipeline integration"],"failure_modes":["Pruning heuristics may remove context critical for domain-specific or highly abstracted code patterns, requiring manual validation","No semantic understanding of business logic — relies on syntactic patterns, so context removal may be suboptimal for non-standard coding styles","Zero-config approach means no fine-tuning per project; 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