how

# How On Codex, read the [platform mapping](../poteto-mode/references/codex-tools.md), including its per-skill notes, before following this skill. On GitHub Copilot, read the [platform mapping](../poteto-mode/references/copilot-tools.md), including its per-skill notes, before following this skill. 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. Each spawn below names a role line in `pstack-models.md` and a default in [Models](#models). Set `model` to that line's value, or to the default if the sheet or the line is missing. Leave `model` unset when the value is `auto` or `inherit-parent`. If the `Agent` tool rejects a slug, use the default and say so. If it rejects the default, use the closest valid slug of the same family from its error message. ## 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 all explorers in a single message: - `subagent_type`: `general-purpose` - `model`: the `how explorer` line, default in [Models](#models) - `readonly`: `true` 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 `Agent` subagent that explores and explains in one pass: - `subagent_type`: `general-purpose` - `model`: the `how explainer` line, default in [Models](#models) - `readonly`: `true` 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 `Agent` subagent to synthesize their findings into one explanation: - `subagent_type`: `general-purpose` - `model`: the `how explainer` line, default in [Models](#models) - `readonly`: `true` 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. ## Models Role defaults, stamped from `plugins/pstack/models.json` (edit there, rerun `tools/generate.mjs`). A matching role line in the `pstack-models.md` override sheet overrides each at runtime; `/setup-pstack` writes it and lists its path per runtime. - how explorer: `opus` - how explainer: `opus` ## Reasoning effort A role value in the override sheet may name a reasoning effort after its model, as in `opus @xhigh`. Levels on Claude Code: `low`, `medium`, `high`, `xhigh`, `max`. Which ones apply depends on the model. A value without `@` takes the sheet's `default effort` line, a level or `session`, and `session` when the sheet has no such line. `session` sets no effort, so the dispatch is the usual one. Strip the suffix before reading the model: `inherit-parent` or `auto` still omits `model` at every level, and a model name is passed as `model`. On Claude Code, a level picks the effort agent from the `subagent_type` you would otherwise use. `pstack:poteto-agent` becomes `subagent_type: "pstack:poteto-agent-<level>"`. `general-purpose`, or no `subagent_type`, becomes `subagent_type: "pstack:effort-<level>"`. The effort agents set only `effort`, so the model you pass still decides the model. On Codex, pass the level as `spawn_agent`'s `reasoning_effort` and keep the usual instructions.