explain-for

explain-for is a skill for Claude Code, Codex from kirodotdev/KiroCrew. It costs 102 tokens per session (3,257 once invoked), scanned A, original, Apache-2.0.

Explain a topic, a piece of code, an error, or a design decision calibrated to one named audience — a 5-year-old, a 5th grader, a manager, a designer, a graduate student, a parent. Resolves who the explanation is for (from the request, or from what memory already records about that person), establishes the ground…

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/kirodotdev/kirocrew/explain-for
Any agent
npx skills add kirodotdev/KiroCrew --skill explain-for
Clone the repo
git clone --depth 1 https://github.com/kirodotdev/KiroCrew

Made for: Claude Code, Codex.

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-for

README.md
[![agentmods](https://agentmods.dev/badge/skills/kirodotdev/kirocrew/explain-for.svg)](https://agentmods.dev/skills/kirodotdev/kirocrew/explain-for)
Your own site
<a href="https://agentmods.dev/skills/kirodotdev/kirocrew/explain-for"><img src="https://agentmods.dev/badge/skills/kirodotdev/kirocrew/explain-for.svg" alt="Measured on agentmods" height="20"></a>
Per session 102 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,257 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original 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 $0.00102 $0.03257
Opus 5 $0.00051 $0.01629
Sonnet 5 $0.00020 $0.00651
Haiku 4.5 $0.00010 $0.00326

Measured yesterday against content hash 78359d37b7a9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

explain-for 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 yesterday.

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.

src/kiro_crew/builtin_skills/explain-for/SKILL.md · 263 lines

How it starts

The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.

explain-for — write it for one named audience

One explanation cannot serve everyone. This skill makes you name the audience first, verify the facts second, and only then write.

Step 0 — check that this is actually an audience request

Trigger matching here is word-overlap against the phrases above, so it is probabilistic and it will sometimes fire on a request that merely contains the word "explain". Before entering the skill, confirm the user wants an explanation pitched at somebody. If they just want to know why CI failed, answer that question normally and ignore everything below.

Step 1 — resolve the audience

Pick exactly one audience.

Check memory before guessing. Crew carries a persistent user profile, and a request like "explain this to my daughter" or "break this down for Priya" often resolves against something already recorded — an age, a role, an interest. Search memory and lessons for that person and use what is there; guessing an age the profile already contradicts is the most visible way this skill fails.

If nothing resolves and no audience is named, do not silently invent one:

  • Explicit smallest-audience phrasing ("dumb it down", "explain like I am") means Age 5. That is the request.
  • Anything else with no audience — ask which audience in one line, or state the row you assumed in one clause so the user can correct it.

If the request names someone you cannot classify (a specific colleague, a title absent from the tables), map them onto the nearest row and say which row.

Ages

Audience Calibration
Age 5 Smallest words. Analogies from toys, animals, candy, playground. One idea per sentence.
Age 10 Elementary. Cause-and-effect is fine. Analogies from school, sports, video games.
Age 15 Some abstraction is fine. Analogies from phones, games, social apps. Casual, never try-hard.
Age 20-30 Direct and clear. Analogies from work, money, daily logistics.
Age 40+ Respectful, unhurried. Analogies from home ownership, career, running a household.

Read the full file on GitHub · 263 lines

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. yesterday First seen · 263 lines · 102 tokens per session scan A 78359d37b7a9

Subscribe to this mod's changes

explain-for is a skill published in the GitHub repository kirodotdev/KiroCrew (3,532 stars, last pushed 2d ago), licensed Apache-2.0. It adds 102 tokens to every session and 3,257 once invoked, about $0.0005 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 2026-09-02.

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