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 DevelopersGlobal/ai-agent-skills --skill code-explanationgit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skillsWrote 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/developersglobal/ai-agent-skills/code-explanation)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/code-explanation"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/code-explanation/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/developersglobal/ai-agent-skills/code-explanation"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/code-explanation.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 | $0.00034 | $0.00828 |
| Opus 5.5 | $0.00014 | $0.00331 |
| Sonnet 5.5 | $0.00007 | $0.00166 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
code-explanation 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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Understanding unfamiliar code is a daily engineering task — onboarding to a codebase, debugging a library, or reviewing a PR. AI agents can explain code, but without structure, explanations are either too high-level to be useful or too detailed to absorb.
This skill produces layered, targeted explanations: start with what it does, then why, then how to work with it.
When to Use
- Onboarding to an unfamiliar codebase
- Understanding a complex function or algorithm before modifying it
- Debugging code you didn't write
- Reviewing a PR for a part of the system you don't know well
Process
Step 1: The 30-Second Summary
- In 2–3 sentences: What does this code do? What problem does it solve?
- What is the expected input? What is the output/effect?
- Where does this fit in the larger system?
Deliver: A 2–3 sentence plain-English summary a junior engineer can understand.
Step 2: Key Concepts and Patterns
- What design patterns does this use? (observer, factory, pipeline, etc.)
- What external libraries or frameworks are being used and why?
- Are there any non-obvious algorithmic choices? (Why O(n log n) and not O(n²)?)
- What are the key data structures and why were they chosen?
Deliver: 3–5 bullet points explaining the key design decisions.
Step 3: Execution Walkthrough
- Walk through the primary execution path step by step.
- For each significant step: what happens? what state changes?
- Highlight any surprising or non-obvious behavior.
- Show example input → output.
Deliver: A numbered step-by-step walkthrough of the happy path.
Step 4: Edge Cases and Gotchas
- What inputs cause unexpected behavior?
- What are the performance characteristics? (O(n) per call? Expensive on large inputs?)
- What side effects does this have? (Modifies global state? Makes network calls?)
- What could go wrong? What does failure look like?
Deliver: A "watch out for" section with at least 2 gotchas.
Step 5: How to Work With This Code Safely
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 · 93 lines · 34 tokens per session scan A d00a8a9be473
code-explanation is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (177 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 828 once invoked, about $0.0001 per session on Opus 5.5. 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.
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