Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add madebyaris/advance-minimax-m3-cursor-rules --skill minimax-m3-long-contextgit clone --depth 1 https://github.com/madebyaris/advance-minimax-m3-cursor-rulesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/madebyaris/advance-minimax-m3-cursor-rules/minimax-m3-long-context)<a href="https://agentmods.dev/skills/madebyaris/advance-minimax-m3-cursor-rules/minimax-m3-long-context"><img src="https://agentmods.dev/badge/skills/madebyaris/advance-minimax-m3-cursor-rules/minimax-m3-long-context/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/madebyaris/advance-minimax-m3-cursor-rules/minimax-m3-long-context"><img src="https://agentmods.dev/badge/skills/madebyaris/advance-minimax-m3-cursor-rules/minimax-m3-long-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00101 | $0.01494 |
| Opus 5 | $0.00051 | $0.00747 |
| Sonnet 5 | $0.00020 | $0.00299 |
| Haiku 4.5 | $0.00010 | $0.00149 |
Grade A, and why
minimax-m3-long-context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
M3 Long-Context Discipline
M3 ships a 1M-token MSA context window. The room is large; the cost of using it badly is also real. This skill teaches the retention and compression decisions that keep long-context work honest.
When to Use
- The full content (files, search results, fetched pages, transcripts, design notes) might exceed ~200K tokens.
- The user explicitly asks to "keep all of this in mind", "use the whole repo", or "don't lose anything".
- You are tempted to start a fresh session to "free context" — that is usually a compression failure, not a context failure.
- Multi-file refactors across a large codebase, transcript analysis, full-repo synthesis, or retrieval-augmented synthesis.
- A research / debugging / migration task that you expect to iterate more than 3 times.
For a single-file edit or a small bug fix, you do not need this skill.
Step 0: Decide Retention Per Slice
For each chunk of evidence you are about to load, pick one of three retention modes before you load it:
- Keep verbatim — the file is the answer, the user asked to see it, or the next step depends on exact contents.
- Keep summary — the contents matter for context but you only need the high-signal lines.
- Drop — the chunk is tangential, redundant with something already in context, or only useful for one specific iteration that has passed.
This is the same as deep-research Phase 2's "drop tangential" rule, applied at the file level before loading.
Step 1: Plan The Loader
Before the first read or search, write a 4–6 line plan in your scratchpad:
Loader plan
In context at start: [system + always-on rules + user task]
Add verbatim: [the few files the answer depends on]
Add as summary: [reference docs, fetched pages, prior search results]
Drop: [tangential files, duplicate docs, raw search output past its iteration]
Compress at: [end of each iteration; before any new search round]
If you cannot write this plan, the task is under-specified — go back to the user or the codebase.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 128 lines · 0 tokens per session scan A 251fd2ca8cec
minimax-m3-long-context is a skill published in the GitHub repository madebyaris/advance-minimax-m3-cursor-rules (125 stars, last pushed 2mo ago), licensed MIT. It adds 101 tokens to every session and 1,494 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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