Agent skill

Openclaw Spawner

by LeoYeAI in LeoYeAI/openclaw-master-skills

Enables an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or parallel to handle alone.

MITAuto-check passedAgent Workflows

Install Openclaw Spawner

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill openclaw-spawner -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills openclaw-spawner --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/recursive-spawn .claude/skills/openclaw-spawner && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
openclaw-spawner
GitHub stars
2.2k
Token cost
~4.5k tokens
SKILL.md length
1,177 words
Files
4 (incl. scripts)
Skills in repo
972
Repo updated
First seen
Licence
MIT

At a glance

Enables an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or parallel to handle alone.

  • Works in 5 steps: Decide to Spawn → Freeze Parent State → Build the Spawn Payload → …
  • An Openclaw agent needs to delegate work to another Openclaw agent
  • SKILL.md covers Security Notes, When to Spawn, Spawn Depth Limit and Spawn Payload Schema, plus 5 more sections
  • Runs Python scripts from its folder; calls pip; needs ANTHROPIC_API_KEY and OPENAI_API_KEY

What it does

Openclaw Spawner is an agent skill from LeoYeAI/openclaw-master-skills. Enables an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or parallel to handle alone. Use this skill whenever an Openclaw agent needs to delegate work to another Openclaw agent, break a large task into concurrent sub-tasks, or hand off a portion of work mid-execution. Triggers include: task requires parallel execution, a sub-task is clearly separable from the main task, the agent detects it cannot complete the work alone within time/context limits, or the user…

Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including scripts (for example `_meta.json`, `manifest.json` and `scripts/spawn_openclaw.py`).

It sits in Agent Workflows, covering Subagents. It works with OpenAI and Anthropic API. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • An Openclaw agent needs to delegate work to another Openclaw agent
  • Break a large task into concurrent sub-tasks
  • Hand off a portion of work mid-execution
  • Include: task requires parallel execution

Example prompts

  • “delegate”
  • “parallelize”
  • “Use the openclaw-spawner skill to enable an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or…”
  • “/openclaw-spawner”

Requirements

  • Python 3
  • A credential in ANTHROPIC_API_KEY
  • A credential in OPENAI_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Decide to Spawn
  2. Freeze Parent State
  3. Build the Spawn Payload
  4. Spawn the Openclaw Sub-Instance
  5. Await and Integrate Results

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.litellm.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • ANTHROPIC_API_KEY
    • OPENAI_API_KEY
    • GEMINI_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Openclaw Spawner loads about 4.5k tokens when it runs. Until then it costs about 171 tokens; SKILL.md has 1,177 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~171
When it runs · the whole SKILL.md, loaded when a task matches
~4.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 1,177 words, ~4,478 tokens.

Download SKILL.mdSave it as .claude/skills/openclaw-spawner/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
openclaw-spawner
description
Enables an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or parallel to handle alone. Use this skill whenever an Openclaw agent needs to delegate work to another Openclaw agent, break a large task into concurrent sub-tasks, or hand off a portion of work mid-execution. Triggers include: task requires parallel execution, a sub-task is clearly separable from the main task, the agent detects it cannot complete the work alone within time/context limits, or the user asks to "spawn", "delegate", "parallelize", or "split" work across agents. Always use this skill — not ad-hoc improvisation — when spawning is needed.
env
Provider API key env var — which one depends on the model= argument passed at call time. Common ones: ANTHROPIC_API_KEY (Anthropic), OPENAI_API_KEY (OpenAI)…
security
CREDENTIAL: Set the provider-specific API key env var for your chosen model. Never embed keys in payloads or snapshots. FILESYSTEM: Passing file-access tools…

Openclaw Spawner

Allows an Openclaw agent to spawn child Openclaw agents, passing them exactly the context they need to carry out their piece of work and report results back.

Helper script: scripts/spawn_openclaw.py — copy this into your project and import from it. It contains spawn_openclaw(), spawn_openclaw_async(), is_error(), and read_result().

Multi-provider: Uses LiteLLM — pass any supported model string via the model= argument. Default is "anthropic/claude-opus-4-6".

Requires: litellm Python package (pip install litellm) and the API key env var for your chosen provider (e.g. ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY).

Tool format: OpenAI function-call format. LiteLLM translates to each provider's native format automatically.


Security Notes

Credential: spawn_openclaw.py uses LiteLLM to call your chosen provider. Set the matching API key env var (e.g. ANTHROPIC_API_KEY, OPENAI_API_KEY). Never put keys in a payload or snapshot.

Tool format: Tools must be in OpenAI function-call format. LiteLLM translates them to each provider's native format automatically.

Filesystem access: Passing tools= to a child agent grants that child whatever capabilities those tools carry. File-access tools allow children to read and write arbitrary paths. Only supply tools you would trust the parent to use directly. When in doubt, omit tools= — the child will return results in its summary text.

Snapshot sanitization: progress_so_far is sent to the Anthropic API and injected into the child's context. Before spawning, review snapshots and strip any secrets, credentials, personal data, or other sensitive information.


When to Spawn

Spawn a child agent when any of these are true:

  • The remaining task has a clearly separable sub-task that can run independently.
  • Two or more subtasks can proceed in parallel, saving wall-clock time.
  • The current agent's context window is approaching its limit and a fresh context would help.
  • A sub-task requires different tools, permissions, or specialization.
  • The user explicitly asks to spawn / delegate / parallelize.

Do not spawn for trivial one-step tasks; keep it in the current agent.


Spawn Depth Limit

MAX_DEPTH = 3 (configurable in scripts/spawn_openclaw.py).

DepthRole
0Root / parent agent
1Direct child (default spawn)
2Grandchild (only if child's sub_task explicitly permits spawning)
3+Blocked — raises ValueError

Always pass depth=<current_depth + 1> when calling spawn_openclaw() from inside a child.


Spawn Payload Schema

Every spawn call must include these three fields:

json
{
  "main_task_title": "<short human-readable title of the overall parent task>",
  "progress_so_far": "<markdown summary of what has already been done, decisions made, artefacts produced, and anything the sub-agent must know to avoid redoing work>",
  "sub_task": "<clear, self-contained description of exactly what this child agent must do, including expected output format and where/how to return results>"
}
Field Guidelines
FieldRules
main_task_title≤ 10 words. Stable across all children of the same parent.
progress_so_farInclude: steps completed, key decisions, files written, variables/state the child needs. Exclude: raw data the child doesn't need. Keep it dense but readable.
sub_taskMust be self-contained. Assume the child has zero memory of the parent conversation. Include: what to do, inputs, expected output format, where to put results (file path, return value, etc.).

Step-by-Step Spawning Protocol

1 — Decide to Spawn

Confirm the sub-task is genuinely separable. If in doubt, handle it yourself.

2 — Freeze Parent State

Before spawning, write a progress snapshot. This becomes progress_so_far for the child and serves as the parent's own checkpoint in case it needs to resume.

Sanitize before spawning. progress_so_far is sent to the Anthropic API and injected into the child's context. Remove any secrets, API keys, passwords, tokens, or personal data before including them in the snapshot.

## Progress Snapshot — <main_task_title>
**Completed:**
- <step 1>
- <step 2>

**Artifacts produced:**
- <file or output name>: <one-line description>

**Decisions made:**
- <decision>: <rationale>

**Pending (what the child will handle):**
- <sub-task description>
3 — Build the Spawn Payload

Fill in the three required fields from the snapshot above.

4 — Spawn the Openclaw Sub-Instance

Import from scripts/spawn_openclaw.py:

Important: Without tools=, the child is a pure language model — it can reason and produce text but cannot read or write files. If the sub_task requires file I/O, pass the appropriate tool definitions (e.g. Anthropic computer-use tools, custom file tools). When tools are omitted, collect the child's result from the summary string directly rather than calling read_result().

python
from spawn_openclaw import spawn_openclaw, is_error, read_result

# my_tools = [...]  # OpenAI function-call format tools if the child needs file I/O

payload = {
    "main_task_title": "Refactor authentication module",
    "progress_so_far": (
        "## Progress Snapshot\n"
        "**Completed:**\n- Audited existing auth flow\n- Identified 3 outdated JWT helpers\n\n"
        "**Artifacts produced:**\n- `/tmp/audit_report.md`: full list of issues\n\n"
        "**Decisions made:**\n- Use PyJWT 2.x API; drop legacy HS256 fallback\n\n"
        "**Pending (child handles):**\n- Rewrite auth/jwt_helpers.py per audit report"
    ),
    "sub_task": (
        "Rewrite `/src/auth/jwt_helpers.py` using PyJWT 2.x. "
        "Read `/tmp/audit_report.md` for issues to fix. "
        "Write the rewritten file to `/tmp/jwt_helpers_new.py` and a "
        "one-paragraph summary to `/tmp/jwt_helpers_changes.md`."
    ),
}

try:
    # Swap model= to use any LiteLLM-supported provider:
    # "openai/gpt-4o", "gemini/gemini-2.0-flash", "groq/llama-3.3-70b-versatile", etc.
    summary = spawn_openclaw(payload, depth=1, model="anthropic/claude-opus-4-6", tools=my_tools)
except (ValueError, FileNotFoundError) as exc:
    raise  # bad depth, missing payload key, or SKILL.md not found — fix the call

if is_error(summary):
    print("Child failed:", summary)
else:
    result = read_result("/tmp/jwt_helpers_changes.md")
    if result is None:
        print("WARNING: child did not write expected result file.")

If SKILL.md is not adjacent to spawn_openclaw.py, pass the path explicitly:

python
import pathlib
summary = spawn_openclaw(payload, depth=1, skill_path=pathlib.Path("/your/path/to/SKILL.md"))
5 — Await and Integrate Results
  1. Check is_error(summary) — handle failures before reading files.
  2. Use read_result(path) to safely read child output; treat None as child failure.
  3. Merge into parent progress snapshot.
  4. Continue with the next step of the parent task — or spawn another child if needed.

Spawning Strategies

StrategyWhen to useParent blocks?
SequentialChild output is needed for next parent stepYes, until child done
Parallel-gatherMultiple independent children; parent needs all before continuingYes, until all done
Fire-and-forgetChild works on a separable track; parent has its own work nowNo — merge later

Show full SKILL.md (479 more words)Show less
Strategy A — Sequential

Use spawn_openclaw(payload, depth=1) as shown in Step 4. Read result, check for errors, continue.


Strategy B — Parallel Gather
python
import asyncio
from spawn_openclaw import spawn_openclaw_async, is_error, read_result

async def main():
    payloads = [
        {
            "main_task_title": "Generate market research report",
            "progress_so_far": "Outline approved. Three sections assigned in parallel.",
            "sub_task": "Write 'Competitive Landscape' (600 words). Save to /tmp/section_competitive.md."
        },
        {
            "main_task_title": "Generate market research report",
            "progress_so_far": "Outline approved. Three sections assigned in parallel.",
            "sub_task": "Write 'Customer Segments' (600 words). Save to /tmp/section_customers.md."
        },
        {
            "main_task_title": "Generate market research report",
            "progress_so_far": "Outline approved. Three sections assigned in parallel.",
            "sub_task": "Write 'Market Trends' (600 words). Save to /tmp/section_trends.md."
        },
    ]

    summaries = await asyncio.gather(
        *[spawn_openclaw_async(p, depth=1, model="openai/gpt-4o", tools=my_tools) for p in payloads],
        return_exceptions=True,
    )

    result_paths = [
        "/tmp/section_competitive.md",
        "/tmp/section_customers.md",
        "/tmp/section_trends.md",
    ]

    for summary, path in zip(summaries, result_paths):
        if isinstance(summary, BaseException):
            print(f"Child raised exception for {path}: {summary}")
            continue
        if is_error(summary):
            print(f"Child failed for {path}:", summary)
            continue
        content = read_result(path)
        if content is None:
            print(f"WARNING: no result file at {path}")
        else:
            print(f"Merging {path} ({len(content)} chars)")
            # merge content into parent output ...

Strategy C — Fire-and-Forget

The parent delegates a sub-task and immediately continues its own work. The child writes results to a known file path. The parent checks at a planned merge point.

Parent:  ──[spawn child]──────────────────────────[merge point]──▶ continue
Child:            └──[work independently]──[write result file]──▶ done
python
import asyncio
from spawn_openclaw import spawn_openclaw_async, is_error, read_result

async def main():
    child_result_path = "/tmp/child_analysis.md"
    payload = {
        "main_task_title": "Refactor authentication module",
        "progress_so_far": (
            "Audit complete. Parent is now rewriting core auth logic. "
            "Child is assigned to analyse test coverage gaps in parallel."
        ),
        "sub_task": (
            f"Read `/tmp/audit_report.md`. Identify which functions lack test coverage. "
            f"Write a markdown report of gaps to `{child_result_path}`. "
            f"Include function name, file, and suggested test cases for each gap."
        ),
    }

    # Spawn — returns immediately, child runs in background
    child_task = asyncio.create_task(
        spawn_openclaw_async(payload, depth=1, model="anthropic/claude-opus-4-6", tools=my_tools)
    )

    # Parent does its OWN work right now
    await do_parent_work()

    # Merge point — collect child
    try:
        summary = await child_task
    except (ValueError, FileNotFoundError) as exc:
        print("Child raised configuration error:", exc)
        return
    if is_error(summary):
        print("Child failed:", summary)
    else:
        content = read_result(child_result_path)
        if content is None:
            print("WARNING: child did not write expected result file.")
        else:
            await integrate_child_output(content)


async def do_parent_work():
    pass  # replace with actual parent steps

async def integrate_child_output(text: str):
    print(f"Merging {len(text)} chars from child...")
Rules for fire-and-forget
  1. Always specify result_path in sub_task — it is the only rendezvous.
  2. Plan your merge point before spawning — know exactly when the parent will need the child's output.
  3. Use read_result() — returns None safely on any filesystem error (missing file, permission denied, etc.).
  4. One result file per child — if a child produces multiple artefacts, have it write a manifest JSON listing them all.
  5. Don't fire-and-forget if parent needs the result immediately — use Sequential instead.

Error Handling

What raises vs. what returns an error string:

SituationBehaviour
Anthropic API error (network, rate limit, etc.)Returns JSON error string — never raises
Empty or non-text API responseReturns JSON error string — never raises
depth >= MAX_DEPTHRaises ValueError — programmer error, fix your call
Missing required payload key (main_task_title, progress_so_far, sub_task)Raises ValueError — fix your payload
Non-JSON-serializable value in payloadRaises ValueError — fix your payload
SKILL.md not foundRaises FileNotFoundError — fix your path config

Always check with is_error() after a successful call. Wrap the call itself in try/except if you need to handle the two programmer-error exceptions gracefully:

python
try:
    summary = spawn_openclaw(payload, depth=1)
except (ValueError, FileNotFoundError) as exc:
    print("Configuration error:", exc)
    raise  # or handle

if is_error(summary):
    import json
    err = json.loads(summary)
    print("Runtime error:", err["error"])
    print("Partial results at:", err.get("partial_results"))  # may be None
    # decide: retry, fallback, abort parent
else:
    # success — read result files
    result = read_result("/tmp/some_output.md")
    if result is not None:
        # merge result ...
        pass
    else:
        print("WARNING: child did not write expected result file.")

Anti-Patterns to Avoid

Anti-PatternWhy It's BadFix
Sending the full conversation history as progress_so_farWastes tokens; child gets confusedSummarize: only what the child needs
Including secrets in progress_so_farSnapshot is sent to Anthropic API and visible to childStrip API keys, passwords, tokens, and personal data before spawning
Passing overly-permissive tools to childrenChildren gain filesystem or network access beyond what their sub_task needsScope tools to minimum required capability; omit tools= if the task only needs text output
Vague sub_task like "handle the rest"Child doesn't know what to doBe explicit: inputs, steps, output location
Spawning for a 2-line taskOverhead > benefitDo it in the parent
Not writing a progress snapshot before spawningParent loses state if it crashesAlways freeze state first
Omitting tools= when sub_task requires file I/OChild is a pure LM — it cannot read or write files; read_result() always returns NonePass tool definitions or collect results from the summary string instead
Ignoring is_error() on child summarySilent failures; parent merges nothingAlways check before reading result files
Fire-and-forget with no result fileNo rendezvous; parent can't collect outputAlways specify result_path in sub_task
Awaiting fire-and-forget child immediately after spawnDefeats the purposePut await child_task at the merge point
Omitting depth= when spawning from inside a childDepth check never triggers; runaway treesAlways pass depth=current_depth + 1
Child spawning further children without explicit permissionRunaway tree; hard to debugOnly spawn if sub_task explicitly says so

Quick Reference Card

SPAWN CHECKLIST
───────────────────────────────────────────
[ ] Sub-task is genuinely separable?
[ ] depth= will stay within MAX_DEPTH (default 3)?
[ ] Progress snapshot written (parent state frozen)?
[ ] main_task_title: ≤ 10 words, stable
[ ] progress_so_far: dense summary, no raw dumps
[ ] sub_task: self-contained, explicit result_path

STRATEGY SELECTION
[ ] Parent needs result before next step?        → Sequential (A)
[ ] Multiple children, all needed before merge?  → Parallel-gather (B)
[ ] Parent has its own work to do right now?     → Fire-and-forget (C)

AFTER EVERY SPAWN
[ ] is_error(summary) checked?
[ ] read_result(path) used (handles missing files safely)?
[ ] None result handled — don't merge silently?

FIRE-AND-FORGET EXTRAS
[ ] result_path agreed before spawning?
[ ] merge point placed after parent's own work?

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts) in skills/recursive-spawn of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • _meta.json
  • manifest.json
  • scripts/spawn_openclaw.py

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Openclaw Spawner next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Openclaw Spawner compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Openclaw Spawner this skillLeoYeAI/openclaw-master-skills2.2k—~4.5kAutomated safety check: PassMIT
Claudish UsageMadAppGang/claudish1k—~9kAutomated safety check: PassNone
Prime Agentwcygan/dotfiles196—~1.8kAutomated safety check: PassNone
Personalization Subagent Patterngrowthenginenowoslawski/coldoutboundskills742—~2.8kAutomated safety check: PassMIT
Efficient DispatchNecmttn/ax116—~1.9kAutomated safety check: PassAGPL-3.0
Agent Squad Python Guide2FastLabs/agent-squad7.8k—~4.7kAutomated safety check: PassApache-2.0

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Questions about Openclaw Spawner

What does Openclaw Spawner do?

Enables an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or parallel to handle alone. Openclaw Spawner is an agent skill from LeoYeAI/openclaw-master-skills. Enables an Openclaw agent to spawn sub-agents (child Openclaw instances) when a task is too large, complex, or parallel to handle alone.

When should I use Openclaw Spawner?

Openclaw Spawner fits situations like: an Openclaw agent needs to delegate work to another Openclaw agent; break a large task into concurrent sub-tasks; hand off a portion of work mid-execution; include: task requires parallel execution.

How do I install Openclaw Spawner in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill openclaw-spawner -a claude-code`. Or copy the skill folder (skills/recursive-spawn in LeoYeAI/openclaw-master-skills) into .claude/skills/openclaw-spawner in your project. Claude Code loads it when a task matches its description.

How do I install Openclaw Spawner in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill openclaw-spawner -a codex`. Or copy the skill folder (skills/recursive-spawn in LeoYeAI/openclaw-master-skills) into .agents/skills/openclaw-spawner in your project. Codex loads it when a task matches its description.

Can I use Openclaw Spawner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill openclaw-spawner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/openclaw-spawner, .gemini/skills/openclaw-spawner, .github/skills/openclaw-spawner and .opencode/skills/openclaw-spawner in your project.

What does Openclaw Spawner need to run?

Going by SKILL.md and its folder, Openclaw Spawner needs Python for the scripts in its folder, the command-line tools its instructions call (pip) and credentials named ANTHROPIC_API_KEY, OPENAI_API_KEY and GEMINI_API_KEY. Our summary lists: Python 3; A credential in ANTHROPIC_API_KEY; A credential in OPENAI_API_KEY.

Does Openclaw Spawner access the network?

SKILL.md names 1 domain. As links in the text: docs.litellm.ai. This is read from the text; nothing was executed.

Is Openclaw Spawner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Openclaw Spawner use?

Openclaw Spawner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Openclaw Spawner use?

About 4.5k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Openclaw Spawner?

Skills that share tags, products or a category with Openclaw Spawner: Claudish Usage (MadAppGang/claudish, 1k stars), Prime Agent (wcygan/dotfiles, 196 stars), Personalization Subagent Pattern (growthenginenowoslawski/coldoutboundskills, 742 stars) and Efficient Dispatch (Necmttn/ax, 116 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Openclaw Spawner?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,159 GitHub stars. The repository holds 972 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.