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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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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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.No reviews yet. Tell others what you thought.
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Shapes turns action-first: action leads, steps numbered, state restated. Use for ADHD-friendly output. Do not use to trim tokens; use response-compression.
The English summary is being prepared. Showing the original description.
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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