Results for “multi-skilling”

Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Handles source capture, precision passes, authoring, registration, and validation.

Guides contributors through adding, updating, publishing, documenting, validating, and wiring public skills in the BuilderIO/skills repository, covering plain skills, instruction-style skills, app-backed/MCP skills, and Plan skill sync.

Create or update Cursor agent skills (SKILL.md). Use when authoring a new skill, updating an existing skill, or asking about SKILL.md structure and best practices.

Discover and reorganize the skill inventory into hierarchical sub-skill bundles. Use when the user asks to review, group, or consolidate skills into a parent bundle.

Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

Provides nine skills for Nature-journal-family academic publishing, covering figure creation, prose polishing, manuscript writing, citation formatting, data availability statements, paper reading, reviewer responses, paper-to-PPT conversion, and academic search via an MCP server.

Simulator.Company skill registry specialist — the data-driven analogue of these built-in skills. Use when the user wants to RUN a saved playbook ("run skill", "use the … skill", "/skill <slug>", "is there a skill for …", "what skills do I have"), or to AUTHOR one ("create a skill", "save this as a skill / playbook", "teach simulator to …", "make a reusable procedure"). A skill is an actor of the `Skills` system form whose `description` holds a step-by-step procedure (which MCP tools to call, with concrete entity ids) for a workspace-specific task such as "create a smart contract" or "onboard a client". Activate on: "run skill", "use playbook", "is there a skill for", "what skills do I have", "save as skill", "create a skill", "teach simulator", "запусти скіл", "використай скіл", "є скіл для", "які скіли є", "збережи як скіл", "створи скіл", "навчи simulator", "запусти навык", "используй навык", "сохрани как навык", "создай навык".

Runs a structured multi-agent deliberation: four specialist agents write independent position papers, two cross-examiners debate them, and the orchestrator synthesizes a final recommendation with implementation plan and risk register.

Auto-create new skills from repetitive workflows. When you notice yourself doing the same multi-step process repeatedly, extract it into a reusable SKILL.md that any agent can use.

MMBench: Is Your Multi-modal Model an All-around Player?

multi-llm-review

Structure a decision or design challenge through multiple perspectives before committing to action. Use as a synthesis step after scoping, mapping, and dilemma navigation when a group needs a wiser next step.

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

Advanced multi-agent coordination with parallel dispatch and synthesis. Use for complex tasks requiring multiple specialist perspectives.

Apply an approved sub-skill grouping by moving user-specified skills into a parent bundle, with timestamped backups of every modified directory.

Design multi-agent systems with context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, and decision frameworks for when multiple agents are justified.

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.