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MiniMax M3 Cursor Rules

A durable execution spine for repo-scale engineering on M3 + Cursor 3.7 — with frontier-agent coding judgment and reasoning protocols distilled into rules any model can run.

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Tuned for MiniMax M3 (1M-token MSA context, native multimodal input) and Cursor 3.7 (Agents Window, canvases, Design Mode, /worktree, /best-of-n, Await, MCP Apps). Written to stay useful across model changes.

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Quick Start · Non-Dev Guide · Why This Repo · Architecture · Runtime Modes · Solver Loop · Evaluate It · References

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At A Glance

| | What you get | |---|---| | Lean always-on core | Two durable rules carry the execution spine — reasoning protocol, solver loop, scope control, code discipline, M3 long-context discipline, M3 multimodal input discipline, and a strict proof contract. No persona bloat. | | Frontier craft, distilled | The fable5-* craft rules transfer the judgment behind SWE-Bench-class agents — locate-before-write, root-cause method, simplicity taste, test integrity, hypothesis ledgers, stuck-strategy ladder — to M3 and any open model. | | Progressive depth | 18 requestable rules + 7 skill packs load only when the task needs them, so context stays clean. | | M3 long-context discipline | 1M-token MSA context is a real lever, but the failure mode shifts to "kept too much raw output." A dedicated skill (minimax-m3-long-context) teaches the retention and compression cadence. | | M3 multimodal-native | Image and video inputs ground visual claims (multimodal-grounded). A dedicated skill (minimax-m3-multimodal-input) teaches the design-parity and screenshot-triage workflow. | | Cursor 3.7 surface | Explicit guidance for the Agents Window, canvases, Design Mode, /worktree, /best-of-n, Await, MCP Apps structured content, and Composer 2.5. | | Honest tool use | The agent works the current runtime — no invented tools, no stale wrappers, no promises before the path is confirmed. | | Evidence-backed closeouts | Explicit status labels (verified / unverified / blocked / multimodal-grounded), minimum-proof rules per change type, and red → green proof for bug fixes. | | Portable | docs/AGENTS.md carries the same behavior to non-Cursor IDEs and CLIs. | | Model-resilient | Tuned for M3 first, compatible with any Cursor-supported model. |

The bet: MiniMax doesn't get better from persona text. It gets better from cleaner context, smaller proving slices, better tool routing, honest verification — and the same judgment habits frontier agents use: fix the broken invariant, not the symptom; never game a test; update the plan after every tool result. Every rule here optimizes for that.

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Quick Start

Not a developer? You can still use these rules. Read the plain-language Non-Developer Guide — what the rules do for you, how to install them with no terminal, and how to write good prompts in everyday words. Available in English and Bahasa Indonesia.

For Cursor

git clone https://github.com/madebyaris/advance-minimax-m3-cursor-rules.git
cp -r advance-minimax-m3-cursor-rules/.cursor your-project/.cursor

That's it. Two rules are always on:

• .cursor/rules/minimax-m3-core.mdc — reasoning protocol, execution behavior, code discipline, M3 long-context discipline, M3 multimodal input discipline
• .cursor/rules/minimax-m3-status-verification.mdc — status & proof contract (multimodal-grounded visual proof, red → green for bug fixes)

Everything else is requestable and narrower by design — it loads when the task or file globs call for it. The two fable5-* craft rules load for non-trivial coding and reasoning work; the rest attach by runtime or domain.

The official docs recommend Anthropic-compatible access for MiniMax text models, and also support OpenAI-compatible access paths. See MiniMax text generation docs · MiniMax API overview.

For Other IDEs and CLIs

Copy docs/AGENTS.md into the target repo root as AGENTS.md. It lives under docs/ here on purpose, so Cursor does not auto-activate it while you edit these rules.

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Repository Layout

.cursor/
├── rules/                         # 20 rules (2 always-on + 18 requestable)
│   ├── minimax-m3-core.mdc                  ★ always-on · execution spine + reasoning protocol + M3 disciplines
│   ├── minimax-m3-status-verification.mdc   ★ always-on · proof contract (+ multimodal-grounded, red → green)
│   ├── fable5-coding-craft.mdc              requestable · frontier coding judgment distillation
│   ├── fable5-reasoning.mdc                 requestable · frontier thinking protocols
│   └── …                                    requestable: runtime + domain
├── agents/                        # subagents (/debugger, /verifier)
│   ├── debugger.md                          root-cause analysis: hypothesis ledger, bisection, fix-at-the-owner
│   └── verifier.md                          adversarial validation: claim-gaming hunt, proof execution
└── skills/                        # 7 deep, structured skill packs
    ├── anti-slop-design/
    ├── 3d-web-experiences/
    ├── deep-research/
    ├── incident-triage-harness/
    ├── minimax-multimodal-toolkit/
    ├── minimax-m3-long-context/             # new · 1M-context retention/compression
    └── minimax-m3-multimodal-input/         # new · native image/video input workflow
docs/
├── AGENTS.md                      # portable agent contract (non-Cursor)
└── FOR-NON-DEVELOPERS.md          # plain-language guide for non-programmers
examples/
└── agent-teams-product-prototype.md
harness/                           # evaluation harness — run the rules against a real model
├── agent.py                                 minimal tool-using agent loop (rules as system prompt)
├── quiz.py · intent_test.py                 no-tool knowledge + intent probes
└── seeds/                                   visible+hidden tasks (calc, roman, tally)

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Why This Repo Exists

This repo makes MiniMax M3 feel strong exactly where the M3 release puts its emphasis:

• 1M-token MSA context — and the discipline to use it without bloating
• native multimodal input (image, video) — and the discipline to ground visual claims in the actual file
• higher agentic and coding benchmarks — leveraged through role separation and explicit verification
• frontier coding judgment — the fable5-* craft rules distill the habits behind SWE-Bench-class scores (root-cause method, test integrity, interleaved thinking) into a form open models can follow
• agent harnesses and multi-agent collaboration, including /best-of-n as a first-class team pattern
• long skill packs and detailed tool contracts that load only when relevant
• dynamic tool discovery in changing environments (Cursor 3.7's evolving MCP + plugin surface)

The goal is not to make MiniMax imitate another provider's tone. It is to transfer the judgment — where to change code, how to prove a fix, when to switch strategy — while M3 keeps its own voice. A durable execution spine that complements its official positioning around real-world engineering, complex skills, agent workflows, long context, and multimodal grounding.

<details> <summary><b>Why M3-native (and what that optimizes for)</b></summary> <br/>

MiniMax positions M3 as a generational shift: 1M-token MSA context, native multimodal input, and higher agentic and coding benchmarks (model page).

So this repo optimizes for:

• explicit retention and compression decisions on 1M tokens (not "fit it all and hope")
• grounding every visual claim in the actual attached image/frame (multimodal-grounded)
• bounded repo exploration instead of reading everything
• smallest proving slices for large tasks
• explicit role and handoff discipline for multi-agent work, including /best-of-n for high-stakes choices
• strong skill contracts instead of vague long prompts
• truthful runtime and verification reporting

</details> <details> <summary><b>The MoE / MSA note — what you can and cannot control</b></summary> <br/>

These rules do not assume you can steer a model's internal routing through persona text. M3 swaps full attention for MiniMax Sparse Attention (MSA), which selects KV-blocks per query — and the controllable levers are still external:

• cleaner context (with explicit retention decisions)
• better decomposition
• better tool routing (including the Cursor 3.7 surface)
• better verification loops, including multimodal-grounded visual proof
• clearer definitions of done

If M3 performs better after a rule change, the likely reason is improved external problem structure — not magic access to hidden experts.

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The Solver Loop

The single most important behavior this repo transfers into M3:

1. Define the outcome in operational terms.
2. Inspect the repo and runtime before deciding.
3. Find the spine: entry points, data flow, state, persistence, user-visible behavior.
4. Build the smallest vertical slice that proves the feature works.
5. Verify at the surface where the user experiences the change.
   - For visual claims: re-read the actual post-change frame (multimodal-grounded).
6. Expand scope only after the core slice works.

For app-building, that means: don't start with a pile of components — resolve key flows first, prove one end-to-end slice early, then add polish.

| New-app proving loop | | |---|---| | 1 | install / setup succeeds | | 2 | dev server or health check starts | | 3 | production build succeeds | | 4 | one primary happy-path flow works | | 5 | promised integrations (styling, routing, persistence, auth) are actually verified | | 6 | any visual claims are multimodal-grounded (re-read the post-change frame) |

Example — for "build a task app", prioritize create → list → complete → persist → reload. Delay filters, collaboration, settings, and animations until the core path works.

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Execution Guarantees

A few behaviors the repo treats as non-negotiable:

• New packages, frameworks, and toolchains are checked against current authoritative sources before they are recommended or installed.
• Scaffolding uses the framework's official CLI / create / init path when one exists.
• Scaffold output is inspected before continuing.
• Runnable work is not "done" until there is runnable proof, not just static confidence.
• Bug fixes are pro