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/gpt-agi/clawd-code/explain-codenpx skills add GPT-AGI/Clawd-Code --skill explain-codegit clone --depth 1 https://github.com/GPT-AGI/Clawd-CodeWhat 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 | $0.00038 | $0.00134 |
| Opus 5 | $0.00019 | $0.00067 |
| Sonnet 5 | $0.00008 | $0.00027 |
| Haiku 4.5 | $0.00004 | $0.00013 |
Grade A, and why
explain-code 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 2d 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- explain-code — 100% identical, 25 lines differ
- explaining-code — 95% identical, 2 lines differ
- explaining-code — 95% identical, 2 lines differ
- explain-code — 89% identical, 2 lines differ
What it actually says
When explaining code, always include:
- Start with an analogy: Compare the code to something from everyday life
- Draw a diagram: Use ASCII art to show the flow, structure, or relationships
- Walk through the code: Explain step-by-step what happens
- Highlight a gotcha: What's a common mistake or misconception?
Keep explanations conversational. For complex concepts, use multiple analogies.
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.
- 2d ago First seen · 14 lines · 38 tokens per session scan A aa12f1242377
explain-code is a skill published in the GitHub repository GPT-AGI/Clawd-Code (572 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 134 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
codebase-to-course
Turn a local folder or GitHub repository into an interactive browser-based course that explains how the codebase works for non-expert programmers and AI-assisted builders. Use when a user asks to make a course, tutorial, walkthrough, learning guide, codebase explanation, or interactive lesson from a project.
compound-engineering
Make your AI agent learn and improve automatically. Reviews sessions, extracts learnings, updates memory files, and compounds knowledge over time. Set up nightly review loops that make your agent smarter every day.
compound-engineering
Make your AI agent learn and improve automatically. Reviews sessions, extracts learnings, updates memory files, and compounds knowledge over time. Set up nightly review loops that make your agent smarter every day.
playwright
Use when the task requires capturing or automating a real browser from the terminal.
good-docs-writing
Writing style guide derived from Modal's documentation voice. Apply when writing or editing docs, guides, tutorials, or technical prose that should read direct, second-person, confident, low-jargon, and example-first. Use to draft new docs in this voice or to revise existing prose toward it.
a-evolve
Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on…