explain

explain is a skill for Claude Code from eddmann/agent-toolkit. It costs 19 tokens per session (234 once invoked), scanned A, original, no licence file.

A read-only guide for explaining how code works, including its architecture, recurring patterns, and design decisions.

In plain words
What is it for?
It is for describing code structure, explaining implementation choices, and answering questions about how a project works.
Why use it?
It helps someone understand an existing codebase without changing it.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Good fit It is for describing code structure, explaining implementation choices, and answering questions about how a project works.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/eddmann/agent-toolkit/explain
Install

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.

Any agent
npx skills add eddmann/agent-toolkit --skill explain
Clone the repo
git clone --depth 1 https://github.com/eddmann/agent-toolkit

Made for: Claude Code.

Wrote 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.

agentmods badge for explain

README.md
[![agentmods](https://agentmods.dev/badge/skills/eddmann/agent-toolkit/explain/github.svg)](https://agentmods.dev/skills/eddmann/agent-toolkit/explain)
Your own site
<a href="https://agentmods.dev/skills/eddmann/agent-toolkit/explain"><img src="https://agentmods.dev/badge/skills/eddmann/agent-toolkit/explain/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.

agentmods 80×15 button for explain

Your own site · 80×15
<a href="https://agentmods.dev/skills/eddmann/agent-toolkit/explain"><img src="https://agentmods.dev/badge/skills/eddmann/agent-toolkit/explain.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 234 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.00234
Opus 5.5 $0.00008 $0.00094
Sonnet 5.5 $0.00004 $0.00047
Haiku 4.5 $0.00002 $0.00023

Measured 23d ago against content hash 2bd545efac5e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

explain 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 23d 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.

skills/explain/SKILL.md · 24 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 23d ago Changed · -31 lines 2bd545efac5e
  2. 1mo ago First seen · 55 lines · 19 tokens per session scan A 9c46ce3ccada

Subscribe to this mod's changes

explain is a skill published in the GitHub repository eddmann/agent-toolkit (2 stars, last pushed yesterday), with no licence file. It adds 19 tokens to every session and 234 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 2026-08-31.

Related

Other skills, from other repositories

typesafe-ai

Build AI-powered software with TypeSafe and Jev, or use pi-typesafe's typesafequestion tool for typed semantic judgments and probabilities. Use when a feature needs programmable common sense, when an LLM prompt-and-parse step could become a structured decision, or when designing, calling, evaluating, or…

narumiruna/pi-extensions · 98 tokens

prompt-architect

Designs clear, testable prompts for agent workflows. Use when creating new prompts, refining weak prompts, or establishing reusable prompting patterns.

repowise-dev/claude-code-prompts · 31 tokens

pi-prompting

Internal guidance for composing prompts that Pi runs (DeepSeek by default) handle reliably for coding, review, diagnosis, and research tasks.

Agents365-ai/pi-plugin-cc · 31 tokens

telnyx-ai-assistants-javascript

AI voice assistants with custom instructions, knowledge bases, and tool integrations.

team-telnyx/ai · 24 tokens

llm-app-patterns

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

davila7/claude-code-templates · 54 tokens

prompt-engineer

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…

Jeffallan/claude-skills · 93 tokens