By terrylica
MiniMax M-series production wiring: OpenAI-compat quirks, hybrid prompt caching, Tier F quant agentic stack, model-upgrade detection. Distilled from a 41-iteration M2.7-highspeed exploration campaign.
Production wiring for the MiniMax-M3 model — empirically verified flags, capabilities, and limits (thinking control via reasoning_split, native vision, response_format, ~1M input ceiling, 524K output cap, n=1, docs-vs-reality discrepancies). Use when wiring or tuning MiniMax-M3, choosing M3 vs M2.7/-highspeed, switching a service off M2.7-highspeed onto M3, getting clean output without <think>, or asking what M3 supports / how big its context is. TRIGGERS - MiniMax M3, MiniMax-M3, M3 model, switch to M3, reasoning_split, M3 context length, M3 vision, M3 options, get the most out of M3.
MiniMax M-series production wiring patterns for the OpenAI-compatible API at api.minimax.io. TRIGGERS - MiniMax, MiniMax-M2.7, Hailuo
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npx claudepluginhub terrylica/cc-skills --plugin minimaxCode quality and validation: clone detection, multi-agent E2E validation, performance profiling, schema testing
ITP workflow enforcement: Ruff Python linting, ASCII art blocking, graph-easy reminders, ADR/Spec sync, code-to-ADR traceability
Slash command factory and calendar event management with tiered sound alarms
Gemini Deep Research via browser automation: Playwright CDP, 8-step flow, 40k+ char research reports
Prescriptive git-town workflow enforcement for fork-based development: fork creation, contribution workflow, enforcement hooks that block forbidden raw git commands
Multi-agent orchestrator for Claude Code. Track work with convoys, sling to polecats. The Cognition Engine for AI-powered software factories.
Self-evolving research methodology: 18 universal principles for LLM-driven investigation, distilled from a 376-turn session with 1 positive + 17 null campaigns. Covers epistemic foundations (causality, nulls, record-keeping), investigation patterns (multi-lens agents, adversarial gates, per-trade enrichment), decision layer (constraint-pivot, supersede-not-rewrite), and emergent resurrection (failure archive with resurrect_if conditions).
This skill should be used when the model's ROLE_TYPE is orchestrator and needs to delegate tasks to specialist sub-agents. Provides scientific delegation framework ensuring world-building context (WHERE, WHAT, WHY) while preserving agent autonomy in implementation decisions (HOW). Use when planning task delegation, structuring sub-agent prompts, or coordinating multi-agent workflows.
HelloAGENTS — The orchestration kernel that makes any AI CLI smarter. Adds intelligent routing, unified QA gates, safety guards, and notifications.
Ultra-compressed communication mode. Cuts 65% of output tokens (measured) while keeping full technical accuracy by speaking like a caveman.
Frontend design skill for UI/UX implementation