planz Skill
description: Manage hierarchical project plans in SQLite. Tree-based structure with phases and tasks. Multiple output formats (text, JSON, XML, markdown).
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Why use this skill
planz is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around productivity skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Claude Code Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in majiayu000/claude-skill-registry, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
--- name: planz description: Manage hierarchical project plans in SQLite. Tree-based structure with phases and tasks. Multiple output formats (text, JSON, XML, markdown). --- # planz - Hierarchical Project Planning Binary: `~/.local/bin/planz` | DB: `~/.local/share/planz/plans.db (SQLite WAL) ## Fractal Planning: Refine Until Nothing is Left for Interpretation **The core principle:** Start with a high-level outline, then iteratively refine each node until every leaf task is so specific that executing it requires no further thought or decision-making. ### The Process 1. **Start broad** - Create 3-7 top-level phases that capture the major milestones 2. **Identify ambiguity** - Look at each leaf node and ask: "Could I execute this right now without any decisions?" 3. **Refine the unclear** - Use `planz refine` to break down ambiguous nodes into smaller, clearer steps 4. **Repeat** - Continue until every leaf node is atomic and unambiguous 5. **Execute & update** - Work through leaves, marking done, and refine new ambiguity as it emerges ### ⚠️ Important: No Slashes in Task Names The `/` character is the path separator. **Never use slashes in task titles:** ```bash # ❌ WRONG - creates nested path "API" -> "auth endpoint" planz add myplan "API/auth endpoint" # ✅ CORRECT - use other separators planz add myplan "API - auth endpoint" planz add myplan "API: auth endpoint" planz add myplan "API auth endpoint" ``` ### What "Nothing Left for Interpretation" Means A leaf node is **ready for execution** when: - ✅ You know exactly which file(s) to touch - ✅ You know the specif ...