OpenClaw Skill & Command Generator

Generate well-structured OpenClaw skills or slash commands. Both are SKILL.md files with YAML frontmatter — they share the same structure but differ in how they're triggered and described. OpenClaw uses the AgentSkills spec (pi-coding-agent) with its own frontmatter fields, tool names, and path conventions distinct from Claude Code.

Phase 0: Fetch Current Documentation

Before generating, retrieve the latest OpenClaw skill documentation:

bash

clawdocs get "tools/skills" --no-header -q

Capture any frontmatter fields or options not already listed in

{baseDir}/references/frontmatter-options.md

. If

clawdocs

is unavailable, proceed with current references — they are sufficient. If new fields appear, use them and note the update.

Phase 1: Understand Requirements

Parse

$ARGUMENTS

for type hint. Users are often unclear on OpenClaw-specific conventions. Interview to gather:

Primary objective — What should this skill do?
Trigger scenarios — When should it activate? What exact phrases would a user say?
Inputs/outputs — What does it receive and produce?
Complexity — Simple, standard, or complex workflow?
Gating needs — Does it require specific binaries, env vars, or config keys? (drives
```
metadata.openclaw.requires.*
```
)
Execution needs — Sub-agent delegation via
```
sessions_spawn
```
? Command dispatch (bypass model)?

Proceed to Phase 2 when at minimum Objective and Trigger Scenarios are established.

Port Mode

When the user provides an existing Claude Code skill to port (file path or pasted content), skip the interview and apply this translation sequence:

Read the source skill and all its supporting files (scripts, references, examples)
Frontmatter — Remove invalid fields (
```
model
```
,
```
context
```
,
```
agent
```
,
```
allowed-tools
```
,
```
hooks
```
,
```
license
```
). Add
```
metadata
```
with
```
requires
```
if scripts need specific binaries. See
```
{baseDir}/references/frontmatter-options.md
```
for valid fields.

Tool names — Apply the translation table from

{baseDir}/references/claw-patterns.md

Bash

→

exec

Read

→

read

Write

→

write

Edit

→

edit

Glob

Grep

→

exec

find

rg

WebSearch

→

web_search

WebFetch

→

web_fetch

Task

→

sessions_spawn

AskUserQuestion

→conversational asking

Paths — Replace
```
$CLAUDE_PLUGIN_ROOT
```
with
```
{baseDir}
```
. Remove
```
@file
```
injection and bang-backtick references.
Scripts/references — Copy as-is if they are platform-neutral Python with no Claude Code SDK dependencies. Update any internal tool name references.
Proceed directly to Phase 2 Step 7 (Validate) with translated content, then Phase 3 (Deliver).

Phase 2: Generate

Apply throughout generation: use imperative voice and terse phrasing because every token in a generated skill body costs budget on every invocation. Prefer instruction over example — state the rule with its reasoning so it generalizes to every input.

Initialize directory first (when creating a new skill directory, not editing an existing file):

bash

python3 {baseDir}/scripts/init_claw_skill.py <name> --path <dir> [--resources scripts,references,assets]

Exit 0 = directory scaffolded, proceed to Step 1. Exit 1 = naming collision; ask user whether to overwrite or rename.

Step 1 — Choose type

Skills: Trigger-rich, third-person description ("This skill should be used when..."); auto-triggered by routing
Commands: Concise, verb-first description, under 60 chars; user-invoked via
```
/
```
menu
Dispatch commands:
```
command-dispatch: tool
```
with
```
command-tool
```
— bypasses model entirely, routes directly to a named tool (rare; for pure pass-through cases)

Step 2 — Write frontmatter

Read

{baseDir}/references/frontmatter-options.md

for the full OpenClaw field catalog, description patterns, and the

metadata

single-line JSON constraint.

Key constraint: metadata
must be a single-line JSON object on one line. Multi-line YAML mappings under

metadata

are not valid in OpenClaw.

Description density rules: Keep descriptions under ~400 characters / ~100 tokens (600 chars / 150 tokens absolute max) — they load every session. Per the OpenClaw cost formula, each skill costs

195 + 97 + field lengths

characters in the system prompt; a 10-skill install with verbose descriptions burns significant context on routing metadata alone. Derive trigger phrases from the user's actual words in Phase 1, not paraphrases. See the token budget and trigger derivation principles in

{baseDir}/references/frontmatter-options.md

Intensional over extensional — state the rule with its reasoning rather than listing examples that imply the rule. An intensional rule generalizes to every input the skill will encounter; an extensional list only covers the shapes shown.

Step 3 — Validate description discoverability

Before writing the body, verify the description will route correctly. Mentally generate:

3 should-trigger prompts — realistic user messages that should activate this skill. Include at least one naive phrasing from a user who has never heard of the skill.
3 should-NOT-trigger prompts — messages in adjacent domains that are close but should not activate. These test whether the description is too broad.

Evaluate: does the description cover all should-trigger prompts? Would it plausibly reject the should-NOT-trigger prompts? If coverage is weak, revise the description — add missing trigger phrases, tighten language to exclude adjacent domains, or add a negative trigger ("Not for X").

This step catches routing misses before the rest of the skill is built. Proceed when description coverage is adequate.

Step 4 — Write body

Construction rules:

State objective explicitly in first sentence
Use imperative voice ("Analyze", "Generate", "Identify") — no first-person ("I will", "I am")
Context only when necessary for understanding
XML tags only for complex structured data
No "When to Use This Skill" section — body loads only after triggering; routing guidance there is never read by the routing decision
Avoid headers deeper than H3 — deep nesting signals content that belongs in
```
references/
```
, not
```
SKILL.md
```
```
{baseDir}
```
is the path variable for skill-relative file references (substituted before model sees the skill body)

Both skills and commands follow the same body pattern:

markdown

# Name

Brief overview (1-2 sentences).

## Process
1. Step one (imperative voice)
2. Step two
3. Step three

Dynamic Content:

Syntax	Purpose
`$ARGUMENTS`	All arguments as string
`$1` , `$2` , `$3`	Positional arguments
`{baseDir}`	Absolute path to skill directory (substituted at load time)

Note:

@file

injection and bang-backtick command expansion are Claude Code features specific to Claude Code's skill loader implementation — the pi-coding-agent skill loader only supports

{baseDir}

path substitution and does not implement these extensions. Do not use them in generated OpenClaw skills.

Step 5 — Script opportunity scan

Read

{baseDir}/references/script-patterns.md

and apply the five signal patterns to every workflow step in the skill being generated:

Signal	Question	If yes →
Repeated Generation	Does any step produce the same structure with different params across invocations?	Parameterized script in `scripts/`
Unclear Tool Choice	Does any step combine multiple operations in a fragile sequence naturally expressible as one function?	Script the procedure
Rigid Contract	Can you write `--help` text for this step right now without ambiguity?	CLI candidate
Dual-Use Potential	Would a user want to run this step from the terminal, outside the skill workflow?	Design as proper CLI from the start
Consistency Critical	Must this step produce bit-for-bit identical output for identical inputs?	Script — never LLM generation

For each identified script candidate:

Choose the archetype from
```
{baseDir}/references/script-patterns.md
```
(init/validate/transform/package/query)
Scaffold the script in
```
scripts/
```
using the Python template from
```
{baseDir}/references/script-patterns.md
```
Wire it into SKILL.md with: trigger condition, exact invocation using
```
exec
```
tool, output interpretation

Wiring rule: A script reference must state when to invoke (trigger condition), how to invoke (exact command with flags), and what to do with the result (exit code handling, which output fields matter).

Scripts are invoked via the

exec

tool (not

Bash

). Reference paths using

{baseDir}/scripts/script.py

Step 6 — Check delegation

Read

{baseDir}/references/claw-patterns.md

for delegation patterns,

sessions_spawn

usage, cross-skill reference conventions, and tool group translations (Claude Code → OpenClaw tool name mapping).

Scan for existing resources before finalizing:

Review available OpenClaw skills (check ~/.openclaw/skills/ and workspace/skills/)
For each workflow step, ask: "Do we already have this?"

Common delegation patterns:

To invoke another OpenClaw skill: tell the model to read
```
{baseDir}/../<other-skill>/SKILL.md
```
via the
```
read
```
tool, or instruct the user to type
```
/<other-skill-name>
```
For background delegation: use
```
sessions_spawn
```
(non-blocking; result announced back to chat)

Documentation lookups:

exec: clawdocs get "<slug>" --no-header -q

There is no

Skill

tool in OpenClaw — skills are invoked by the routing model, not programmatically from within another skill.

Step 7 — Validate

When generating a new skill directory (not editing an existing single file):

bash

python3 {baseDir}/scripts/validate_claw_skill.py <skill-directory> --output json

Exit 0 = proceed to Phase 3. Exit 1 = parse the

errors

array; each entry has

field

message

severity

. Resolve all

critical

and

major

items before writing to disk.

Explain Your Choices

When presenting the generated skill/command to the user, briefly explain:

What you set and why — "Added
```
metadata.openclaw.requires.bins: [jq]
```
because the skill calls jq in a subprocess"
What you excluded and why — "Left
```
user-invocable
```
at default (true),
```
command-dispatch
```
omitted (skill routes through model)"
Add more trigger phrases if routing misses expected inputs

Phase 3: Deliver

Output Paths

Type	Location	When active
Workspace skill	`<workspace>/skills/<name>/`	Next session in that workspace
Managed skill	`~/.openclaw/skills/<name>/`	Shared across all agents on this machine

Skills are session-snapshotted — changes take effect on the next new session, not the current one.

Write and Confirm

Before writing:

Writing to: [path]
This will [create new / overwrite existing] file.
Proceed?

After Creation

Summarize what was created:

Name and type
Path and when it takes effect
How to invoke/trigger
Suggested test scenario

Publish to ClawHub

Invoke only when the user explicitly requests distribution:

bash

clawhub publish <skill-directory> --slug <slug> --version X.Y.Z --tags latest

Exit 0 = published. Exit 1 = validation or auth failure; read stdout for details.

Phase 4: Evaluate

Score the generated skill/command:

Dimension	Criteria
Clarity (0-10)	Instructions unambiguous, objective clear
Precision (0-10)	Appropriate specificity without over-constraint
Efficiency (0-10)	Token economy — maximum value per token
Completeness (0-10)	Covers requirements without gaps or excess
Usability (0-10)	Practical, actionable, appropriate for target use

Target: 9.0/10.0. If below, refine once addressing the weakest dimension, then deliver.

Re-run

validate_claw_skill.py

after any revisions and verify the validation checklist below before finalizing.

Degrees of Freedom

Level	When to Use	Format
High freedom	Multiple valid approaches, context-dependent decisions	Text instructions, heuristics
Medium freedom	Preferred pattern exists, some variation acceptable	Pseudocode, scripts with parameters
Low freedom	Fragile operations, consistency critical, specific sequence required	Exact scripts, few parameters

Quality Standards

Format Economy:

Simple task → direct instruction, no sections
Moderate task → light organization with headers
Complex task → full semantic structure

Remove ruthlessly: Filler phrases, obvious implications, redundant framing, excessive politeness

Validation Checklist

Before finalizing an OpenClaw skill or command:

Structure:

SKILL.md exists with valid YAML frontmatter
Frontmatter has
```
name
```
and
```
description
```
fields
```
metadata
```
field (if present) is single-line JSON on one line
Markdown body is present and substantial
Referenced files actually exist

Description Quality:

Uses third person ("This skill should be used when...")
Includes specific trigger phrases users would say (verbatim)
Trigger phrases derived from user's actual words, not formalized paraphrases
Under ~400 chars (~100 tokens); 600 chars (~150 tokens) absolute max
Negative triggers present if adjacent skills could false-trigger
Lists concrete scenarios ("create X", "configure Y")
Not vague or generic

OpenClaw Correctness:

No Claude Code-only fields: no

model

context

agent

allowed-tools

hooks

license

No Claude Code tool names referenced in body: no

Bash

WebSearch

WebFetch

Read

Write

Edit

Glob

Grep

Task

Skill

AskUserQuestion

EnterPlanMode

ExitPlanMode

Uses OpenClaw tool names:

exec

read

write

edit

web_search

web_fetch

sessions_spawn

Uses
```
{baseDir}
```
for skill-relative paths (not
```
$CLAUDE_PLUGIN_ROOT
```
)
Output path is OpenClaw workspace (
```
<workspace>/skills/
```
) or managed (
```
~/.openclaw/skills/
```
)

Content Quality:

Body uses imperative/infinitive form, not second person
Body is focused and lean (1,500–2,000 words ideal, <5k max)
Detailed content moved to
```
references/
```
Scripts are executable and documented
Script opportunities identified via five signal patterns
Script references in SKILL.md include trigger condition, invocation (
```
exec
```
), output handling
Consistency-critical steps are scripted, not left to LLM re-generation

Progressive Disclosure:

Core concepts in SKILL.md
Detailed docs in
```
references/
```
Utilities in
```
scripts/
```
SKILL.md references these resources
```
examples/
```
present if skill produces user-adaptable output (see
```
{baseDir}/examples/sample-command/SKILL.md
```
for a minimal command example)

Error Handling

Issue	Action
Unclear requirements	Ask clarifying questions before generating
Missing context	Request usage examples or target scenarios from user
Path issues	Verify target directory exists; let `init_claw_skill.py` create it
Type unclear	Default to skill (auto-triggered) if user hasn't specified
`clawdocs` unavailable	Proceed with current references — they are sufficient

Execute phases sequentially. Always fetch current documentation first.

create-claw-skill

NPX Install

Tags

SKILL.md Content

OpenClaw Skill & Command Generator

Phase 0: Fetch Current Documentation

Phase 1: Understand Requirements

Port Mode

Phase 2: Generate

Step 1 — Choose type

Step 2 — Write frontmatter

Step 3 — Validate description discoverability

Step 4 — Write body

Step 5 — Script opportunity scan

Step 6 — Check delegation

Step 7 — Validate

Explain Your Choices

Phase 3: Deliver

Output Paths

Write and Confirm

After Creation

Publish to ClawHub

Phase 4: Evaluate

Degrees of Freedom

Quality Standards

Validation Checklist

Error Handling