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  • A
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    MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.
    50
    107
    MIT
  • A
    license
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    quality
    A
    maintenance
    Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.
    53
    24
    MIT
  • A
    license
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    quality
    B
    maintenance
    Provides git-aware safe file operations, allowing AI agents to delete files while protecting git-tracked source code from accidental deletion.
    MIT
  • A
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    quality
    A
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    A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.
    39
    MIT
  • A
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    quality
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    An MCP server that provides Claude Code with 99 methodology skills, seed patterns, decision logging, and memory tools to improve software development decisions. It acts as a cognitive scaffold layer, enabling better working methods rather than just additional data.
    MIT
  • A
    license
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    quality
    F
    maintenance
    Execute code in 8 programming languages via gVisor-sandboxed Docker containers. Supports Python, JS, TS, Go, Java, C++, C, and Bash.
    2
    MIT
  • A
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    quality
    B
    maintenance
    LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.
    10
    MIT
  • A
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    quality
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    Enables governed multi-agent software factory operations through eight typed, audited tools for Sprint planning, submission, decisions, inspection, cancellation, and resumption.
    Apache 2.0
  • A
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    quality
    A
    maintenance
    Enables AI assistants and IDEs to work with the AGNTCY Agent Directory, providing tools for validating, publishing, searching agent records, and navigating OASF taxonomies.
    2
    Apache 2.0
  • A
    license
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    quality
    C
    maintenance
    Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.
    6
    280
    MIT
  • A
    license
    A
    quality
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    maintenance
    An MCP front-end for AnB. The tool surface is "use-don't-reveal": no tool returns a plaintext secret. Reveal paths require a TTY the server doesn't have, so the no-reveal guarantee is structural, not prompt-based — it holds even against a prompt-injected agent. Runs as a dedicated, narrowly-scoped AnB identity; exec is default-deny (only allowlist rules tagged scope=mcp run); command output
    5
    1
    MIT
  • A
    license
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    quality
    D
    maintenance
    An MCP server that exposes tools for issuing scoped agent credentials, delegating narrower child credentials, handling approvals, revoking task trees, and retrieving audit trails and evidence packets.
    14
    1
    Apache 2.0
  • A
    license
    A
    quality
    A
    maintenance
    Coordination for parallel coding agents: TTL file claims stored in the git common dir (visible across all worktrees), enforcement hooks that block colliding edits, agent presence, handoff notes, and a git-committed lessons knowledge base with BM25 search. Single static Go binary — no server, no database.
    8
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    MCP server for gavel, a code-quality gate for Bazel monorepos. Exposes judge, findings, coverage and architecture tools so a coding agent can check its change against the quality gate (lint, coverage, layer rules) before declaring it done — all off the Bazel build graph, over just what changed.
    8
    8
    Apache 2.0