// 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. Can critique architecture. Use why for motivation.
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Four LLM reviewers challenge changes from independent angles.
poteto's agent style for concise, detailed responses, deliberate subagents, unslopped prose, simple code, and verified work. Use for poteto, /poteto-mode, or requests to work in this style.
Apply when work is repetitive or bulk: many similar edits, a check you'll rerun, a population to transform. Build the tool that amortizes it (codemod, script, generator) once you know the recipe, instead of grinding by hand.
Spawn three parallel review subagents over the active transcript, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
| name | how |
| description | 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. Can critique architecture. Use why for motivation. |
Explore the codebase to answer "how does X work?" questions. Produce clear 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.
Two modes:
Parse what the user is asking about. They might say:
Identify the scope. If it's ambiguous, make your best guess and state your interpretation before exploring. Don't ask. Explore and let the user redirect if you're off.
Assess complexity to decide the approach:
When in doubt, lean toward the simple path. You can always spawn explorers if the explainer hits a wall.
Decompose the question into 2-4 parallel exploration angles. Each angle should cover a distinct slice of the subsystem so the explorers aren't duplicating work. For example, if the question is "how does the rate limiter work?", you might split into:
The right decomposition depends on the question. Use your judgment. For narrow questions, 2 explorers is fine. For broad subsystems, use up to 4.
Spawn all explorers in a single message:
subagent_type: generalPurposemodel: composer-2.5-fastreadonly: trueEach explorer gets the same base prompt from references/explorer-prompt.md, plus a specific exploration angle telling it which slice to focus on. Each explorer should:
Each explorer returns structured findings: the components it found, the flow it traced, the files it read, and anything non-obvious. Overlap between explorers is fine. The explainer will reconcile.
Then proceed to Step 3.
Spawn a single Task subagent that explores and explains in one pass:
subagent_type: generalPurposemodel: claude-opus-4-7-thinking-xhighreadonly: trueThis agent does its own exploration (Glob, Grep, Read) and writes the explanation directly. Read references/explainer-prompt.md for the communication style and output format. The agent follows the same structure, it just doesn't have explorer findings as input.
Proceed to Step 4.
Once all explorers have returned, spawn a single Task subagent to synthesize their findings into one coherent explanation:
subagent_type: generalPurposemodel: claude-opus-4-7-thinking-xhighreadonly: trueThe explainer gets all explorers' findings and writes the human-facing explanation (see output format below). Read references/explainer-prompt.md for the full prompt template. The explainer reconciles overlapping findings, resolves contradictions, and weaves the separate slices into a unified picture.
Take the explainer's output and present it to the user. You may lightly edit for clarity or add context from the conversation, but don't substantially rewrite. The explainer agent's communication is the product.
The explanation should follow this structure, but adapt it to what makes sense for the question. Not every section is needed for every question.
Overview. 1-2 paragraphs. What is this thing, what does it do, why does it exist. Someone should be able to read this and decide whether they need to keep reading.
Key Concepts. The important types, services, or abstractions. Brief definition of each, not exhaustive, just the ones needed to understand the rest.
How It Works. The core of the explanation. Walk through the flow: what triggers it, what happens step by step, where does data go, what are the decision points. Use prose, not pseudocode. Reference specific files and functions so the reader can go look, but don't dump code blocks unless a specific snippet is genuinely necessary to understand the point.
Where Things Live. A brief map of the relevant files/directories. Not every file, just the ones someone would need to find to start working in this area.
Gotchas. Things that are non-obvious, surprising, or that would trip someone up. Historical context that explains why something looks weird. Known sharp edges.
Triggered when the user asks for architectural issues, problems, or improvements, not just understanding.
Run the full explain flow above (Steps 1-4). You need to understand the architecture before you can critique it.
After the explanation is complete, spawn architectural critics. Launch all in a single message:
| Subagent | Model |
|---|---|
| Critic A | claude-opus-4-7-thinking-xhigh |
| Critic B | gpt-5.3-codex-high-fast |
| Critic C | gpt-5.5-high-fast |
For each critic:
subagent_type: generalPurposemodel: the model from the table. These are minimum reasoning levels. The lead should escalate any model when the architecture warrants deeper analysis.readonly: trueRead references/critic-prompt.md for the prompt template. Each critic gets:
references/critique-rubric.mdSame framework as the interrogate skill. You're a pragmatic lead, not an aggregator.
Categorize findings:
Present the explanation first (from Step 1), then the critique verdict below it. The explanation should stand on its own. Someone who just wants to understand the system shouldn't have to wade through critique.