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Readme & install
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Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill ruvnet/ruflo --skill agent-planner- SKILL.md4.6 KB
Overview
This skill is a strategic planning and task orchestration agent invoked with $agent-planner. It breaks down complex goals into actionable plans, assigns agents and resources, estimates timelines, and flags risks so multi-agent workflows execute reliably. Use it to convert high-level objectives into measurable phases with clear dependencies and success criteria.
How this skill works
The planner analyzes the full scope of a request, decomposes it into atomic tasks, and maps dependencies to reveal the critical path. It allocates agents and resources, produces timeline estimates, and generates risk assessments and mitigation steps. Outputs follow a structured plan format (objective, phases, tasks, critical_path, risks, success_criteria) and can be stored or broadcast via MCP memory hooks for swarm coordination.
When to use it
- Launching multi-agent projects that require coordinated task handoffs and parallel execution
- Designing implementation roadmaps from vague or high-level requirements
- Allocating limited compute or human resources across interdependent tasks
- Estimating timelines and identifying the critical path before execution
- Creating contingency plans and checkpoint validations for high-risk initiatives
Best practices
- Start with a clear objective and measurable success criteria to guide decomposition
- Break work into atomic, testable tasks with explicit inputs, outputs, and owners
- Map dependencies early and highlight critical-path tasks to prioritize effort
- Allocate resources for parallelism when safe, but plan validation checkpoints
- Store plans and status in shared memory so other agents can read and act
Example use cases
- Plan and orchestrate an authentication subsystem: research, design, implement, test, deploy
- Turn a product feature request into phased implementation tasks with agent assignments
- Coordinate a data-pipeline build across data engineers, modelers, and QA agents
- Estimate rollout timelines and contingency plans for a multi-region deployment
- Create a mitigation plan for a high-risk integration involving third-party APIs
FAQ
A structured plan including objective, phases with tasks (id, description, agent, dependencies, estimated_time, priority), critical_path, risks with mitigations, and success_criteria.
How does it coordinate with other agents?
Plans and status entries are written to shared memory via MCP hooks so other agents can read assignments, update progress, and trigger follow-up actions.