Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
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Delegates tasks to the MiniMax CLI (mmx) via delegation-core. Use when delegation-core selects MiniMax or large-context batch work is needed.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
This skill implements conjure:delegation-core for the official MiniMax
CLI, mmx. It provides MiniMax-specific authentication, quota
management, and command construction.
For shared delegation patterns, see Skill(conjure:delegation-core).
Skill(conjure:delegation-core) determines MiniMax is suitablemmx CLI is installed and authenticatedconjure:delegation-core)Installation:
Install the official CLI, published as mmx-cli and maintained by
MiniMax at https://github.com/MiniMax-AI/cli. It requires Node.js 18
or later and installs a binary named mmx.
npm install -g mmx-cli
mmx --version
Install mmx-cli by that exact name. Unrelated third-party packages
publish a binary called minimax, and this skill never invokes that
name.
Authentication:
Credentials are managed by the CLI, not by an environment variable.
mmx stores them in ~/.mmx/config.json.
mmx auth login # interactive: OAuth or an API key
mmx auth login --api-key sk-xxxxx # non-interactive, for CI
mmx auth status # canonical readiness check
mmx auth status is what delegation_executor.py --verify runs. A
successful mmx --version alone does not mean the CLI is authenticated.
Regions: MiniMax serves a global region (api.minimax.io) and a
mainland China region (api.minimaxi.com). API keys are issued per
region and are not interchangeable. API-key login detects the matching
region by probing both, so no manual base URL is normally needed. Set
the region explicitly with mmx config set --key region --value cn or
the MINIMAX_REGION environment variable.
# Basic file analysis
python scripts/delegation_executor.py minimax "Analyze this code" --files src/main.py
# With specific model
python scripts/delegation_executor.py minimax "Summarize" --files src/ --model MiniMax-M3
# Verify the CLI is installed and authenticated
python scripts/delegation_executor.py minimax --verify
The executor reads the requested files and inlines their contents into
the prompt, because mmx has no @path context syntax. Inlined context
is capped at 96 KiB to stay inside the operating system limit on a
single argument, and any remainder is reported in the prompt and the
log.
# Basic command
mmx text chat --message "Analyze this code"
# Specific model
mmx text chat --model MiniMax-M3 --message "Summarize this design"
# JSON output
mmx text chat --message "Extract the function names" --output json
# File contents must be passed in, not referenced
mmx text chat --message "Review this: $(cat src/main.py)"
mmx text chat --message "..." > delegations/minimax/$(date +%Y%m%d_%H%M%S).md
The shared delegation executor can auto-select the best service:
uv run python scripts/delegation_executor.py auto "Analyze large codebase" \
--files src/
For MiniMax-specific models, CLI options, cost reference,
and troubleshooting, see modules/minimax-specifics.md.
mmx --version and mmx auth status both exit 0 before any task is
delegated. A missing installation or failed authentication is reported and
stops execution.mmx binary. No command in this
skill or in Delegator.SERVICES["minimax"] invokes a binary named minimax.@path reference
appears in a prompt sent to mmx.delegations/minimax/YYYYMMDD_HHMMSS.md (timestamp format matching the
Quick Start example), and that file exists on disk after delegation.conjure:delegation-core selected a different provider, this skill is
not invoked. MiniMax delegation only runs when delegation-core routes to it.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Inverts burden of proof for code additions. Use when reviewing PRs, planning refactors, or running unbloat to challenge every addition's necessity.
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Tracks per-agent token usage and flags waste in parallel dispatch. Use when evaluating parallel agent efficiency or after a multi-agent run.
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Coordinates Claude agent teams via filesystem protocol. Use when orchestrating parallel agents with task dependencies. Do not use for single-agent tasks.
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Evaluates API surface design, consistency, and exemplar alignment. Use when reviewing public API changes or before releasing a new API surface.
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Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.
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