Research operating kits for AI agents, built by Mark at Mindify AI.
LabKits is the researcher-facing sibling of ideas like gStack or gBrain: a portable stack of skills, prompts, harnesses, and environment conventions that help an AI agent work like a serious research partner instead of a generic chatbot. The first kits include Cothinking for active paper reading, Math Knowledge Graph for dense formal reasoning, Agentic Solution Search for searching solution spaces, LaTeX Paper Writing for venue-ready manuscripts, Fine-Tune Hyperparameter Search for reproducible ML tuning, and Hypothesis Experiment Loop for designing experiments that either meet a hypothesis or force a principled revision.
macOS, Linux, and WSL:
curl -fsSL https://github.com/ghraw/mindify-ai/LabKits/main/scripts/install.sh | bashWindows PowerShell:
irm https://github.com/ghraw/mindify-ai/LabKits/main/scripts/install.ps1 | iexFrom a cloned checkout:
./scripts/install.shThe installer creates ~/.labkits, installs the packaged skill archives, installs the generic agent harness, and, when a Codex environment is present, expands the skills into ~/.codex/skills.
skills/paper-reading/cothinking.skill- packaged Cothinking skill archive.skills/math-understanding/math-knowledge-graph.skill- packaged Math Knowledge Graph skill archive.skills/agentic-simulation/agentic-solution-search.skill- packaged agentic solution-search skill archive.skills/experiment-design/hypothesis-experiment-loop.skill- packaged hypothesis-driven experiment-loop skill archive.skills/paper-writing/latex-paper-writing.skill- packaged LaTeX paper-writing and venue-readiness skill archive.skills/ml-training/fine-tune-hyperparameter-search.skill- packaged ML fine-tuning and hyperparameter-search skill archive.skills/*/*/- editable source for each LabKits skill.agent-harness/LABKITS.md- environment-independent harness instructions for agents that do not natively load.skillfiles.scripts/install.shandscripts/install.ps1- one-command installers for Unix-like shells and Windows PowerShell.scripts/doctor.sh- local environment and package validation.scripts/package-skill.sh- rebuilds the.skillarchive from source.env/labkits.env.example- optional environment variables for predictable installs.CONTRIBUTE.md,SECURITY.md, andCHANGELOG.md- OSS project guidance.
Most AI research help stops at summarization. LabKits pushes the agent into a more useful role:
- Active reading: the agent asks the researcher to predict, critique, and transfer ideas before revealing answers.
- Math decomposition: the agent turns long formal statements into assumptions, definitions, equations, transformations, claims, and reasoning-flow edges.
- Agentic simulation: the agent maps a solution space, keeps a frontier, runs cheap probes, prunes failed paths, and verifies convergence.
- Experiment design: the agent derives predictions, designs discriminating tests, optimizes within a hypothesis, and revises the hypothesis from evidence.
- Paper writing: the agent drafts, revises, audits, and preflights LaTeX manuscripts against current venue instructions.
- Model tuning: the agent designs disciplined fine-tuning sweeps, tracks trials, prunes weak configs, and packages reproducible recipes.
- Visual compression: concepts become Mermaid graphs, ASCII sketches, figure walkthroughs, or small study companions when useful.
- Research taste: the agent separates claims, hypotheses, evidence, and weak links.
- Portable setup: the same kit can be used inside Codex, other agent runtimes, or a plain prompt harness.
Think of it as a small research lab bench you can carry between machines and agent environments.
In Codex or any agent that can load installed skills, ask for Cothinking explicitly:
Use LabKits Cothinking to walk me through this paper: https://arxiv.org/abs/...
Or unpack dense math directly:
Use LabKits Math Knowledge Graph to explain this loss function at undergraduate and expert levels.
Or search an open solution space:
Use LabKits Agentic Solution Search to explore possible approaches before choosing an implementation.
Or prepare a manuscript:
Use LabKits LaTeX Paper Writing to convert these results into an ICML-ready paper draft.
Or tune a model:
Use LabKits Fine-Tune Hyperparameter Search to design a LoRA sweep for this dataset.
Or test a hypothesis:
Use LabKits Hypothesis Experiment Loop to design experiments for this hypothesis and revise it if results contradict it.
For agents without native skill loading, paste or attach agent-harness/LABKITS.md as system or project context, then provide the paper.
| Environment | Status | Notes |
|---|---|---|
| macOS | Supported | Uses bash, curl, and unzip; installs to ~/.labkits and ~/.codex/skills when available. |
| Linux | Supported | Same path and tool requirements as macOS. |
| WSL | Supported | Treated as Linux; recommended for Windows users who run Unix-style agents. |
| Windows PowerShell | Supported | Uses Invoke-RestMethod and Expand-Archive; installs to $HOME\\.labkits. |
| Codex | Supported | Skill source is expanded into the Codex skills directory. |
| Generic agents | Supported | Use agent-harness/LABKITS.md as the portable harness. |
Optional environment variables:
export LABKITS_HOME="$HOME/.labkits"
export CODEX_HOME="$HOME/.codex"
export LABKITS_REPO_RAW="https://github.com/ghraw/mindify-ai/LabKits/main"./scripts/doctor.shThe doctor checks OS support, required commands, archive integrity, source layout, and likely Codex install state.
Edit the source skill under skills/<category>/<skill-name>/, then rebuild the packages:
./scripts/package-skill.sh
./scripts/doctor.shThe package command rewrites each skills/<category>/<skill-name>.skill archive from its source directory.
- More paper-reading kits for review writing, experiment reproduction, and related-work mapping.
- A benchmark harness for testing whether a kit improves researcher comprehension or decision quality.
- Runtime adapters for more agent environments.
- Versioned releases of individual kits.
MIT License. Copyright (c) 2026 Mark Chen.