MateClaw is a pluggable agent runtime that coordinates AI agents, tools, conversations, memory, and workflows. It is designed for personal and multi-user AI assistants, including agents used through chat platforms. Catalogue add-ons provide skills for extending its agent workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mateaix/mateclaw --skill subagent-driven-developmentgit clone --depth 1 https://github.com/mateaix/mateclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mateaix/mateclaw/subagent-driven-development)<a href="https://agentmods.dev/skills/mateaix/mateclaw/subagent-driven-development"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/subagent-driven-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mateaix/mateclaw/subagent-driven-development"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/subagent-driven-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.02389 |
| Opus 5.5 | $0.00007 | $0.00956 |
| Sonnet 5.5 | $0.00003 | $0.00478 |
| Haiku 4.5 | $0.00002 | $0.00239 |
Grade A, and why
subagent-driven-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
92% identical to subagent-driven-development — 25 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Subagent-Driven Development
Overview
Execute implementation plans by dispatching fresh subagents per task with systematic two-stage review.
Core principle: Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration.
When to Use
Use this skill when:
- You have an implementation plan (from writing-plans skill or user requirements)
- Tasks are mostly independent
- Quality and spec compliance are important
- You want automated review between tasks
vs. manual execution:
- Fresh context per task (no confusion from accumulated state)
- Automated review process catches issues early
- Consistent quality checks across all tasks
- Subagents can ask questions before starting work
The Process
1. Read and Parse Plan
Read the plan file. Extract ALL tasks with their full text and context upfront. Create a todo list:
# Read the plan
read_file("docs/plans/feature-plan.md")
# Create todo list with all tasks
todo([
{"id": "task-1", "content": "Create User model with email field", "status": "pending"},
{"id": "task-2", "content": "Add password hashing utility", "status": "pending"},
{"id": "task-3", "content": "Create login endpoint", "status": "pending"},
])
Key: Read the plan ONCE. Extract everything. Don't make subagents read the plan file — provide the full task text directly in context.
2. Per-Task Workflow
For EACH task in the plan:
Step 1: Dispatch Implementer Subagent
Use delegate_task with complete context:
delegate_task(
goal="Implement Task 1: Create User model with email and password_hash fields",
context="""
TASK FROM PLAN:
- Create: src/models/user.py
- Add User class with email (str) and password_hash (str) fields
- Use bcrypt for password hashing
- Include __repr__ for debugging
FOLLOW TDD:
1. Write failing test in tests/models/test_user.py
2. Run: pytest tests/models/test_user.py -v (verify FAIL)
3. Write minimal implementation
4. Run: pytest tests/models/test_user.py -v (verify PASS)
5. Run: pytest tests/ -q (verify no regressions)
6. Commit: git add -A && git commit -m "feat: add User model with password hashing"
PROJECT CONTEXT:
- Python 3.11, Flask app in src/app.py
- Existing models in src/models/
- Tests use pytest, run from project root
- bcrypt already in requirements.txt
""",
toolsets=['terminal', 'file']
)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 352 lines · 17 tokens per session scan A dacaccb7cbb1
subagent-driven-development is a skill published in the GitHub repository mateaix/mateclaw (1,166 stars, last pushed yesterday), licensed Apache-2.0. It adds 17 tokens to every session and 2,389 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to subagent-driven-development, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
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Orchestrate specialized, autonomous AI agents to execute parallel subtasks with strict boundaries.
subagent-delegation
Delegate routine work to subagents — boilerplate generation, data transformation, file analysis, documentation drafting. Use when splitting tasks into independent subtasks for parallel subagent execution.
orchestrator
Providerneutrales Protokoll zum Zerlegen komplexer Aufgaben, zum Beauftragen unabhängiger Worker und zur evidenzbasierten Abnahme ihrer Ergebnisse.
swarmclaw
AI agent runtime and multi-agent orchestration platform. Teaches agents how to use SwarmClaw's 6 primitive tools, persistent memory, dreaming, delegation, connectors, credentials, and the skill system. Use when an agent is running on SwarmClaw and needs to understand the platform's capabilities.
multi-agent-workflow
Orchestrate multi-agent workflows with discovery, budgeted delegation, TaskGraph, and AG-UI streams on ProtoOS.
orchestration
Multi-agent orchestration authority — agent memory protocols, task delegation, SOUL/AGENTS patterns, session startup, inter-agent communication, OpenClaw workspace coordination, and agent lifecycle management.