omk-agent
Distill a top-level principle into knowledge/rules.md. Trigger when user says 'add rule', 'new principle', 'enforce this',…
Full plan lifecycle: deep understanding → write plan with TDD checklist → parallel review → Ralph Loop execution. Trigger when user says 'plan', 'design', 'implement', 'build', 'architect', '@plan', '@execute', or describes a multi-step task that needs structured breakdown. Also
$ npx -y skills add kaimingwan/oh-my-kiro --skill omk-planning --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/omk-planningContext preview
The summary Claude sees to decide when to auto-load this skill.
Full plan lifecycle: deep understanding → write plan with TDD checklist → parallel review → Ralph Loop execution. Trigger when user says 'plan', 'design', 'implement', 'build', 'architect', '@plan', '@execute', or describes a multi-step task that needs structured breakdown. Also
name: omk-planning description: "Full plan lifecycle: deep understanding → write plan with TDD checklist → parallel review → Ralph Loop execution. Trigger when user says 'plan', 'design', 'implement', 'build', 'architect', '@plan', '@execute', or describes a multi-step task that needs structured breakdown. Also trigger for feature requests, system redesigns, or migration projects."
One skill for the full plan lifecycle: write → review → execute.
Before writing any plan, build deep understanding of the goal. Skip this phase only if the user provides a fully specified design doc.
Start with `generate_codebase_overview` to get the project's high-level structure, then read relevant code, docs, and recent commits to understand the context. Do NOT ask questions yet — first build your own mental model of:
Based on your understanding, ask questions **one at a time**. Each question must:
**Dynamic termination:** Stop asking when remaining uncertainty won't materially affect the plan. Don't ask for the sake of asking.
**Soft cap:** Maximum 5 questions. If you still have uncertainty after 5, state your assumptions and proceed.
After questions are answered, judge whether research is needed:
**Research dimension principle:** When research IS needed, cover both theoretical foundations (papers, docs, design rationale) AND engineering practice (real implementations, battle-tested patterns, known pitfalls). One without the other leads to either ivory-tower designs or cargo-culted solutions.
This is your judgment call — not every plan needs research.
After research, absorb what you learned. Only ask the user about findings you **cannot resolve from research alone** — things requiring user decisions or preferences.
If no supplementary questions needed, proceed directly to Phase 1.
When the task involves creative/architectural work (new features, new components, significant behavior changes), present the design before writing the plan:
Skip this step for simple refactors, bug fixes, or tasks with a fully specified design doc.
Before proceeding to Phase 1, verify the task is well-defined across 4 dimensions:
| Dimension | ✅ Criterion | Greenfield | Brownfield | |-----------|-------------|------------|------------| | **Goal** | 能用一句话无歧义地说清楚要做什么 | Required | Required | | **Constraints** | 边界、非目标、限制条件已明确 | Required | Required | | **Success Criteria** | 至少有 2 个可测试的验收标准 | Required | Required | | **Context** | 理解被修改的现有代码/系统 | Skip | Required |
Rules:
**Challenge Modes** (activate on 2nd+ question in this step):
After the Readiness Check, reverse-engineer from Success Criteria before writing the plan:
For each Success Criterion: 1. **What must be TRUE** for this criterion to pass? 2. Which of these truths **already exist** in the codebase? 3. Which must be **newly created**? → These become Tasks 4. What are the **dependencies** between new truths? → These determine Task order
This ensures the plan covers everything needed for the goal, not just what seems obvious forward-thinking.
Then validate each major design decision: 1. **Essence** — What is the core problem this decision solves? 2. **Framework** — Does the current codebase already solve this? What known patterns apply? 3. **Application** — Is this feasible on all target platforms? Does benefit > maintenance cost?
Drop any decision that fails step 2 (already solved) or step 3 (infeasible/not worth it).
Then proceed to Phase 1 with the accumulated understanding.
Your AI agent forgets everything. Mine doesn't. oh-my-kiro is a framework that gives your AI coding agent persistent memory, deterministic workflows, and self-evolving intelligence. Like oh-my-zsh for Zsh — but for AI agents. Day 1: generic agent.
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