Multi File Batch Linting With Aggregated Results

via “batch prompt execution with result aggregation”

A CLI utility and Python library for interacting with Large Language Models, remote and local. [#opensource](https://github.com/simonw/llm)

Unique: Implements batching as a CLI-native feature using standard Unix input/output patterns (stdin/stdout, pipes) rather than requiring a separate batch API or job queue system. Results include full metadata (model, timestamp, tokens) for auditability.

vs others: More accessible than building custom batch processing scripts or using cloud provider batch APIs, while maintaining Unix philosophy of composability with other tools

via “batch evaluation of multiple tool calls with aggregated scoring”

GitHub Action for evaluating MCP server tool calls using LLM-based scoring

Unique: Batch evaluation with per-tool aggregation that groups results by tool type, enabling teams to see not just overall pass rates but also which specific tools are underperforming without separate evaluation runs per tool

vs others: More efficient than evaluating tool calls individually because it batches LLM API calls and aggregates results in one pass, whereas naive approaches evaluate each call separately with redundant API overhead

via “multi-file batch linting with aggregated results”

MCP server for ESLint

Unique: Batches ESLint invocations to analyze multiple files in a single MCP request, reducing overhead vs. individual file requests. Aggregates results with file-level grouping and summary statistics for efficient bulk analysis.

vs others: More efficient than making separate MCP requests per file (reduces network round-trips and server startup overhead), while providing structured aggregation suitable for dashboards or bulk refactoring workflows.

via “multi-file batch linting with parallel execution”

MCP server for ESLint

Unique: Implements parallel linting using Node.js async I/O within the MCP server's event loop, avoiding the overhead of spawning separate ESLint CLI processes. Integrates ESLint's built-in caching to skip re-analysis of unchanged files.

vs others: Faster than running ESLint CLI multiple times because it keeps the linting engine warm in memory and parallelizes file processing, while still respecting ESLint's cache invalidation logic.

via “batch processing and file aggregation”

Generate LLM-friendly llms.txt files from markdown and MDX content files

Unique: Designed specifically for documentation aggregation with awareness of file boundaries and logical organization; maintains context about source files unlike naive concatenation

vs others: More efficient than processing files individually; preserves file-level context better than simple text concatenation

via “batch tool definition linting with aggregated reporting”

Static linter for MCP tool definitions — catch quality defects before deployment

Unique: Designed for suite-wide linting with aggregated reporting rather than single-tool validation, enabling consistency checking and quality metrics across entire MCP tool collections

vs others: More efficient than running individual linters on each tool because it processes the entire suite in one pass and provides cross-tool consistency analysis

via “batch directory processing with recursive traversal”

Condense source code for LLM analysis by extracting essential highlights, utilizing a simplified version of Paul Gauthier's repomap technique from Aider Chat.

Unique: Provides recursive directory processing with glob-based filtering and structured metadata output, designed specifically for monorepo scenarios where developers need to condense multiple modules or packages in a single operation

vs others: More efficient than processing files individually because it batches operations and generates a unified metadata manifest, while remaining simpler than full-featured build system integrations

via “batch file processing with llm transformation”

Agent that converses with your files

Unique: Implements a file-level pipeline abstraction that chains LLM calls with filesystem I/O, allowing developers to define reusable transformation templates that apply consistently across multiple files without writing custom scripts for each operation

vs others: Faster than running individual LLM queries for each file because it batches API calls and reuses prompt templates, and more flexible than static linters because the transformation logic is defined in natural language rather than code

via “batch file and directory linting”

** - Clojure linter

Unique: Wraps clj-kondo's batch analysis capability in MCP, allowing single tool calls to lint entire directories. Aggregates results with file-level grouping, enabling efficient codebase-wide analysis without per-file MCP overhead.

vs others: More efficient than invoking linting separately for each file; provides codebase-wide analysis in a single MCP call, reducing latency and simplifying client logic compared to manual file enumeration and sequential linting.

via “batch schema linting across multiple files”

CLI linter for MCP tool/resource schemas

Unique: Implements directory-aware batch validation with aggregated reporting specifically for MCP schema collections, rather than validating schemas individually

vs others: More efficient than running single-file validation in a loop because it aggregates results and can potentially parallelize validation across files

via “batch evaluation of llm outputs”