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 agentmods add skills/mateaix/mateclaw/writing-plansnpx skills add mateaix/mateclaw --skill writing-plansgit 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/writing-plans)<a href="https://agentmods.dev/skills/mateaix/mateclaw/writing-plans"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/writing-plans.svg" alt="Measured on agentmods" 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 | $0.00016 | $0.01719 |
| Opus 5 | $0.00008 | $0.00860 |
| Sonnet 5 | $0.00003 | $0.00344 |
| Haiku 4.5 | $0.00002 | $0.00172 |
Grade A, and why
writing-plans 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 6d 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
94% identical to writing-plans — 21 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Implementation Plans
Overview
Write comprehensive implementation plans assuming the implementer has zero context for the codebase and questionable taste. Document everything they need: which files to touch, complete code, testing commands, docs to check, how to verify. Give them bite-sized tasks. DRY. YAGNI. TDD. Frequent commits.
Assume the implementer is a skilled developer but knows almost nothing about the toolset or problem domain. Assume they don't know good test design very well.
Core principle: A good plan makes implementation obvious. If someone has to guess, the plan is incomplete.
When to Use
Always use before:
- Implementing multi-step features
- Breaking down complex requirements
- Delegating to subagents via subagent-driven-development
Don't skip when:
- Feature seems simple (assumptions cause bugs)
- You plan to implement it yourself (future you needs guidance)
- Working alone (documentation matters)
Bite-Sized Task Granularity
Each task = 2-5 minutes of focused work.
Every step is one action:
- "Write the failing test" — step
- "Run it to make sure it fails" — step
- "Implement the minimal code to make the test pass" — step
- "Run the tests and make sure they pass" — step
- "Commit" — step
Too big:
### Task 1: Build authentication system
[50 lines of code across 5 files]
Right size:
### Task 1: Create User model with email field
[10 lines, 1 file]
### Task 2: Add password hash field to User
[8 lines, 1 file]
### Task 3: Create password hashing utility
[15 lines, 1 file]
Plan Document Structure
Header (Required)
Every plan MUST start with:
# [Feature Name] Implementation Plan
> **For the agent:** Use subagent-driven-development skill to implement this plan task-by-task.
**Goal:** [One sentence describing what this builds]
**Architecture:** [2-3 sentences about approach]
**Tech Stack:** [Key technologies/libraries]
---
Task Structure
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.
- 6d ago First seen · 297 lines · 16 tokens per session scan A 585c6ff26233
writing-plans is a skill published in the GitHub repository mateaix/mateclaw (1,078 stars, last pushed yesterday), licensed Apache-2.0. It adds 16 tokens to every session and 1,719 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to writing-plans, differing in 21 lines, and is treated as a copy.
Other skills, from other repositories
stream-chain
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows.
agent-orchestrator
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
project-orchestration
Orchestrate multi-agent workflows for feature development using planning agents, context handoff, and stage management.
company-product-context
Compiles comprehensive company product context from PDF documents, web research, and industry knowledge.
workflow-creator
Compose durable multi-step workflows with the workflowcreate tool — step shape, agent binding, required skills, and the validation errors worth avoiding.
project-execution
Executes implementation plans with progress tracking, checkpoint validation, and quality gates. Use after planning is complete and tasks are ready to implement.