How

SkillAI & models

Use for \"how does X work\", code walkthroughs before changing something, and placement / ownership / layering questions (\"where should this live\", \"which package owns this\", \"is this the right layer\"). Explains subsystem architecture, runtime flow, onboarding mental models. Use why for motivation.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the How skill

What this skill tells your AI

The instructions your AI receives, as published by dimon94/skills in skills/how/SKILL.md and read by ahel’s review.

Explore the codebase to answer "how does X work?" questions. Produce architectural explanations at the level of a senior engineer onboarding onto a subsystem, enough to build a working mental model, not so much that it reads like annotated source code.

Spawn subagents in the current runtime's native way. When the runtime accepts an explicit model per spawn, set it by role tier; a model the user explicitly chose always wins over this table. If the runtime cannot resolve a tier to a concrete model, leave model unset and continue — never block on it.

  • explorers: fast tier. The cheapest adequate general model in the current runtime.
  • explainer / synthesizer: strong tier. The strongest reasoning model available.

Step 1. Assess Complexity

If the scope is ambiguous, state your interpretation and explore. The user can redirect.

  • Simple (a single module, a small utility, a narrow question such as "how does function X work"): no explorers. One explainer explores and explains in a single pass. Go to Step 2b.
  • Complex (a subsystem spanning multiple files or services, a cross-cutting feature, a full architectural overview): spawn parallel explorers first, then hand off to the explainer. Go to Step 2a.

When in doubt, take the simple path.

Step 2a. Explore (complex questions only)

Decompose the question into 2 to 4 exploration angles, each a distinct slice of the subsystem. Spawn read-only explorer subagents in a single message, one per angle.

Each explorer gets the prompt in references/explorer-prompt.md with its angle filled in. Then go to Step 3.

Step 2b. Direct Explain (simple questions)

Spawn one read-only subagent that explores and explains in one pass:

Build its prompt from references/explainer-prompt.md without the explorer-findings section. Go to Step 4.

Step 3. Synthesize (complex questions only)

Once all explorers have returned, spawn one read-only subagent to synthesize their findings into one explanation:

Build its prompt from references/explainer-prompt.md with every explorer's findings filled in.

Step 4. Present

Present the explainer's output to the user. Light edits for clarity or context from the conversation are fine. Do not substantially rewrite it.

Output Format

The explanation uses the sections defined in references/explainer-prompt.md, dropping any that do not apply: Overview, Key Concepts, How It Works, Where Things Live, Gotchas.

Signals

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skill
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how-dimon94
Source
github.com/dimon94/skills