do-in-steps
by NeoLabHQdo-in-steps helps an agent tackle complex tasks by splitting work into ordered subtasks, orchestrating sub-agents, and verifying each step before moving on. It is a strong fit for repository changes, multi-step refactors, migrations, and do-in-steps for Agent Orchestration when you need controlled handoff and fewer silent failures.
This skill scores 71/100, which means it is worth listing for directory users who want a structured way to run complex tasks in steps. The repository shows a real workflow, not a placeholder: it defines a clear trigger, sequential orchestration pattern, model selection, and step verification. That said, users should still expect to inspect the long SKILL.md carefully because the install decision value is reduced by the lack of companion files and the absence of an explicit install command.
- Clear task trigger and argument hint for complex multi-step work
- Strong operational framing: sequential subtasks, context passing, and independent step verification
- Substantial skill body with many headings and workflow/constraint signals, suggesting real execution guidance
- No install command and no support files, so adoption may require manual setup or more reading
- The document is long, which helps completeness but may slow quick evaluation for users
Overview of do-in-steps skill
The do-in-steps skill helps an agent handle complex work by breaking it into ordered subtasks, running them sequentially, and verifying each step before moving on. It is most useful when the job has dependencies, multiple files or systems, or a high chance of silent failure unless each stage is checked.
This do-in-steps skill is a strong fit for repository changes, multi-step refactors, migration work, agent orchestration, and any task where you want fewer assumptions and more controlled handoff between steps. Its main differentiator is the built-in meta-judge → LLM-as-a-judge flow, which adds a quality gate between execution and progression.
What this skill is for
Use do-in-steps when the task cannot be done safely in one pass and each result should inform the next. It is designed to keep context tight, preserve order, and reduce cascading mistakes in complex execution.
Why it stands out
Unlike a generic prompt that only says “think step by step,” do-in-steps is a workflow skill for Agent Orchestration. It emphasizes task decomposition, model selection by subtask, context passing, and independent verification, which makes it more dependable for longer tasks.
Best-fit readers
This do-in-steps guide is best for agents working on codebases, automation authors, or users who need structured execution rather than creative ideation. If you want an orchestrated plan with checks after each step, this skill is a better fit than a single-shot prompt.
How to Use do-in-steps skill
Install and load the skill
For do-in-steps install, add the skill from the repository path used by your environment, then load SKILL.md as the primary instruction source. In this repo, the skill lives at plugins/sadd/skills/do-in-steps, so the important part is getting the skill file into the agent’s active skills set before starting work.
Turn a vague goal into usable input
The do-in-steps usage pattern works best when your prompt includes the objective, the target repo or system, constraints, and the expected finish line. Good input names the deliverable and the risky parts, not just the theme.
Example stronger prompt:
Refactor UserService to remove duplicated validation, update all callers, keep public APIs stable, and verify behavior with tests.
That is better than:
Improve the service layer.
Read these files first
Start with SKILL.md to understand the orchestration logic, then inspect any referenced project docs or adjacent skill files if your installation exposes them. In this repository, there are no supporting rules/, resources/, or scripts/ folders, so the skill file itself carries most of the operational guidance.
Run it in ordered stages
Use the skill as a sequential workflow: analyze the task, decompose dependencies, execute the first subtask, verify it, then pass only relevant context to the next step. The quality gain comes from preserving step boundaries instead of letting later work drift away from earlier decisions.
do-in-steps skill FAQ
Is do-in-steps better than a normal prompt?
Yes, when the task has dependencies or needs verification between steps. A normal prompt can work for small jobs, but do-in-steps is better when you need controlled orchestration, model choice per subtask, and fewer hidden failures.
When should I not use it?
Do not use do-in-steps for trivial edits, one-off questions, or tasks where a direct response is enough. The orchestration overhead is only worth it when sequencing and validation materially improve the outcome.
Is this beginner-friendly?
Yes, if you can describe a task clearly. The main learning curve is providing enough context for decomposition and accepting that the workflow may ask for intermediate evidence before continuing.
How does it fit Agent Orchestration?
It is explicitly built for do-in-steps for Agent Orchestration: a supervisor coordinates specialized sub-agents, passes summaries forward, and uses an independent judge to reduce step-level error. That makes it especially useful in multi-agent coding or operations workflows.
How to Improve do-in-steps skill
Give it better boundaries
The fastest way to improve do-in-steps results is to define what must not change, what must be verified, and what the final output should look like. Clear constraints help the orchestrator choose the right subtasks and avoid rework.
Supply decision-critical context
If you want stronger output, include affected files, target environment, test expectations, and any compatibility requirements up front. The skill performs best when it can decompose with real constraints instead of inferring them late.
Watch for common failure modes
The main risk is under-specifying the task, which leads to weak decomposition or shallow verification. Another failure mode is overloading the first step with too much context; better to provide enough to decide the plan, then let each subtask inherit only what it needs.
Iterate after the first pass
If the first result is close but incomplete, refine the task with specific gaps: missing tests, unclear dependency order, or a broader change boundary. For do-in-steps, improvement usually comes from better task framing, not from asking for more words.