| name | skill-creator |
| description | Create, edit, or review any skill — a SKILL.md-format module that extends an agent's capabilities. Use whenever authoring a new skill OR touching an existing SKILL.md at all (even small edits): adding or changing a section/gotcha/example, refactoring, or editing the `name`/`description` frontmatter that controls when the skill auto-triggers. Also use when the user says "create a skill for X", "turn this into a skill", "update/edit the X skill", or wants to capture a repeatable workflow as auto-invoked guidance. Works for any SKILL.md target (zdx, Anthropic Claude apps, etc.) and covers skill-vs-alternatives choice, frontmatter rules, save locations, per-step structure, and the draft-then-confirm-then-write flow. |
| license | Complete terms in LICENSE.txt |
Skill Creator
Build skills: SKILL.md modules that auto-invoke when their description matches the situation. The format is the same across runtimes (Anthropic Claude apps, zdx, etc.); save location and packaging differ. This skill covers deciding whether a skill is the right shape, drafting it, and writing it to the right place.
Step 0: Is a skill the right shape?
Skills earn their context tax (the description sits in every prompt) by being auto-invoked when the model recognizes the situation. Before drafting, check the alternatives:
- Custom command / slash command (e.g. zdx
.zdx/commands/<name>.md, Claude Code custom commands) — the workflow is user-fired at a chosen moment (/skillify, /release-notes). Suggest a command and stop.
- Standalone script — deterministic transform with no decision-making. Drop it in the repo or a bin dir.
- One-off ask — the user just wants the thing done now, not a reusable artifact. Do the work; don't make a skill.
- Skill — the agent needs to recognize a class of situations and apply procedural knowledge it doesn't already have, without the user remembering to invoke anything.
If "skill" wins, continue.
Anatomy
<skill-name>/
├── SKILL.md # required
├── references/ # optional; loaded into context only when needed
├── scripts/ # optional; deterministic helpers, may run without being read
└── assets/ # optional; files used in skill output, not loaded into context
Most skills are a single SKILL.md. Add references/, scripts/, or assets/ only when the content is too big or too deterministic to live inline.
Frontmatter rules
---
name: <kebab-case>
description: <see formula below>
---
name: lowercase letters, digits, hyphens; ≤64 chars; no leading/trailing/double hyphens; matches the directory name.description: ≤1024 chars; no < or >; this is the single most important field because it's the only thing the agent matches against to decide whether to load the skill.- Allowed extra keys (rarely needed):
license, metadata, compatibility, allowed-tools. Anything else is invalid.
scripts/quick_validate.py <skill-dir> enforces all of the above; run it when in doubt.
Description formula
Lead with "Use when the user…" and include 2–3 concrete trigger phrases the user is likely to say.
- Bad:
"Helps with PDFs."
- Good:
"Read, create, and edit PDFs. Use when the user asks to extract text from a PDF, fill a form, rotate pages, or says 'merge these PDFs' or 'convert this PDF to images.'"
Where to save
Pick the location based on the runtime the skill targets. Ask the user if it's not obvious.
- zdx project skill —
.zdx/skills/<name>/SKILL.md — workflow references this repo.
- zdx user skill —
$ZDX_HOME/skills/<name>/SKILL.md — follows the user across all repos.
- Anthropic / external skill — author anywhere convenient (often
skills/public/<name>/ or skills/private/<name>/), then package into a .skill zip with scripts/package_skill.py for distribution.
- Default to project when the workflow names this repo's files; otherwise user/external.
Core principles
Concise is key
The context window is shared. Default assumption: the agent is already smart. Only add what it doesn't already know. Challenge each line: does it justify its tokens?
Set appropriate degrees of freedom
- High (prose) — multiple valid approaches; decisions depend on context.
- Medium (pseudocode, parameterized scripts) — preferred pattern with variation.
- Low (specific scripts, fixed sequences) — fragile, error-prone, or consistency-critical.
A narrow bridge with cliffs needs guardrails. An open field allows many routes.
Artifact location discipline
When a skill produces files (reports, screenshots, audio, HTML, generated docs), default outputs to the runtime's artifact dir. In zdx that's $ZDX_ARTIFACT_DIR (use $ZDX_ARTIFACT_DIR/tmp/ for scratch). For other runtimes, follow their convention or write under a clearly-named subdir the user can find. Avoid casual tmp/, output/, /tmp defaults in examples.
Don't fabricate
When building a skill from observed work (a thread, a PR, a workflow you watched), only encode steps that actually happened. Don't invent steps to make the workflow feel more complete.
User corrections are gold
Wherever the user pushed back, redirected, or refined the work — those are the rules to encode. They're more valuable than the happy path.
Progressive disclosure
Keep SKILL.md under ~500 lines. When it grows, split into reference files and link from SKILL.md:
- Multi-step workflows, sequential logic, conditional branching → see
references/workflows.md
- Output formats, templates, quality standards → see
references/output-patterns.md
Reference files load only when the agent follows the link. Keep SKILL.md focused on the core workflow and navigation; push variant-specific details, schemas, and examples into references. Avoid deeply nested references — all reference files should link directly from SKILL.md. For files >100 lines, include a table of contents at the top.
Workflow
1. Understand the skill
Get concrete examples — either from the user, or by inferring from observed work. Ask:
- "What should this skill do?"
- "Which runtime is it for? (zdx, Anthropic Claude apps, other)"
- "Give me 2–3 example user requests that should trigger it."
Stop asking once you have enough. Don't over-interview. A 2-step workflow doesn't need four rounds of questions.
If the skill is being built from observed work in this thread or a past one, use the available memory/thread tools for context that's scrolled out; the conversation history is the source material — don't re-derive from scratch.
2. Decide what's reusable
For each example, identify what would be helpful to bundle:
- Same code rewritten each time →
scripts/<name>.py
- Domain knowledge, schemas, API docs →
references/<topic>.md
- Templates, boilerplate, brand assets used in output →
assets/<file>
Most skills don't need any of these. Don't add directories for hypothetical needs.
3. Initialize the directory
Two ways, pick whichever fits:
- Direct write (best for simple single-file skills, especially zdx user/project skills):
mkdir -p <path>/<name> and write SKILL.md directly.
- Scaffold via
scripts/init_skill.py <name> --path <dir>: creates the directory plus example scripts/, references/, assets/ files. Useful when starting a complex skill or when targeting Anthropic distribution. Delete the example files you don't need.
4. Draft SKILL.md in chat
Write the full SKILL.md as a fenced markdown block in the response. Include the chosen save path. Ask one short confirmation question. Don't write the file yet.
5. Confirm and write
On confirmation, write the file. Do not write before confirmation.
6. Validate (and package, if distributing)
- Run
scripts/quick_validate.py <skill-dir> to check frontmatter shape.
- If the skill is being distributed as a
.skill archive (Anthropic apps), run scripts/package_skill.py <skill-dir> [output-dir] — this re-validates and produces <name>.skill (a zip).
- For zdx skills, no packaging step — the runtime loads the directory directly.
7. Final summary
Tell the user:
- The file path
- The final name and description
- Which steps came from explicit user corrections (if any)
- Anything left as
TODO inside the SKILL.md
- Whether a
.skill package was produced and where
The skill auto-invokes on description match — no slash command needed. The user can edit SKILL.md directly to refine triggers.
Per-step schema (when needed)
For multi-step workflows, annotate each step. Required: Success criteria. Optional: the rest.
### 1. <Step name>
What to do. Specific commands when relevant.
**Success criteria:** how you know this step is done.
**Execution:** Direct (default), `[human]`, or a named subagent the runtime supports.
**Artifacts:** data this step produces that later steps need.
**Human checkpoint:** when to pause (irreversible actions, destructive ops, external messages).
**Rules:** hard constraints, especially user corrections from the source material.
- For zdx, the built-in subagents are Explorer, Oracle, and Task. Reference them as proper nouns in prose; use the lowercase id (
explorer, oracle, task) only when it's the literal value passed to invoke_subagent. Other runtimes have their own subagent names — use those instead.
- For parallel steps, use sub-numbers:
3a, 3b.
- Steps the user must do themselves get
[human] in the title.
- Simple skills don't need annotations on every step. A 2-step workflow can stay plain prose.
What not to include
A skill should only contain files that directly support its function. Do not create:
README.md, INSTALLATION.md, CHANGELOG.md, QUICK_REFERENCE.md- Setup/testing notes
- Meta-commentary about how the skill was made
The skill is for an AI agent, not a human reader. Extra files are clutter.
Iterate
After the skill runs on real tasks, watch for:
- Skill didn't trigger when it should have → tighten the description, add trigger phrases.
- Skill triggered when it shouldn't have → narrow the description.
- A step kept failing → add success criteria or a
Rules block with the correction.
- Same content rewritten across runs → promote to
scripts/ or references/.
Edit SKILL.md directly. Re-run scripts/quick_validate.py after frontmatter changes. No build step for zdx; re-run scripts/package_skill.py if you're shipping a .skill archive.
