The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Papergraph MCP
PaperGraph MCP turns math papers into evidence-grounded reading maps for AI agents: extract results, trace proof evidence, plan reading order, and review external dependencies…
Install
git clone https://github.com/lotchuazzz-crypto/papergraph-mcpInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →PaperGraph MCP
Read math papers with evidence, not guesses.
PaperGraph v1.2.1 preserves source evidence in its reading workflow for math papers: discover public full-text candidates for DOI papers, inspect source-backed proof and citation evidence, review direct and recursive dependencies, then export Markdown Reading Reports or Cross-Paper Reading Plans.
v1.1.6 fixed source re-import data loss and resolver input boundaries; v1.1.7 is a maintenance cleanup without workflow changes. See the v1.1.6 bugfix notes and v1.1.7 release.
It helps AI agents turn arXiv papers, local LaTeX projects, and born-digital PDFs into a local theorem-centered workspace so a researcher can inspect where every claim came from.
v1.2.1: first-use evidence and workflow repairs
This patch preserves ordinary TeX proof citations and BibTeX identity with their original source spans, maps nested input files without confusing merged offsets, and links uniquely named PDF proof headings. It also corrects shell-quoted CLI instructions, read-only starter planning, file preflight and result-page schemas. See the v1.2.1 notes for compatibility.
Installation commands below target fixed version 1.2.1. Package-version output alone does not establish publication: check the official index and recorded public-index acceptance before treating this version as a verified install. The repository checker keeps that launch gate closed until acceptance is recorded.
v1.2.0: DOI inputs and dependency reading
v1.2.0 is released
and available on PyPI.
The previous version has a Git-free, clean-cache consumer check
that passed CLI, stdio MCP and PDF reading for 1.2.0. Its immutable Git tag remains available. To test subsequent checkout changes in isolation, use
uv sync --locked --dev, then uv run papergraph-mcp --version and
uv run papergraph-mcp doctor. For a wheel, build with uv build, install it in a
separate environment, and launch that environment's papergraph-mcp.
See the v1.2.0 notes for compatibility and release verification boundaries.
CLI, diagnostics and MCP initialization report the same package version.
Diagnostics build_identity records the build's full source commit when available,
tracked changes and a source-tree SHA-256; an installed wheel keeps its build-time
identity even when launched from another checkout. A dirty build's commit identifies
its base, not all its contents. Unknown source provenance stays unknown. Release
diagnostics name the version's tag; this field does not check publication or tag
availability. Development builds have a null release_tag and explicitly label
their stable installation recommendation. There is no v1.2.0.dev0 release/tag.
Similar plugs
Reverse engineer anything with agents, from app behavior down to native binaries.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype…
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
A cross-platform desktop All-in-One assistant for Claude Code, Codex, OpenCode, OpenClaw, Grok Build & Hermes Agent. Only official website: ccswitch.io