/why

name: why
description: "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."
disable-model-invocation: true

Why

Investigate the motivation and intent behind code. The companion `how` skill answers what the code does and how it works. `why` answers what forces led to its shape.

Each spawn below names a role line in the `pstack-models.mdc` rule and a default. Set `model` to that line's value, or to the default if the rule or the line is missing. Leave `model` unset when the value is `auto` or `inherit-parent`. If the Task 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. Pick each subagent's reasoning level and fast or standard speed for the job, per the Subagents section of the **poteto-mode** skill.

Every spawn is `generalPurpose` with `readonly: false` (agent mode). **Do not use readonly/Ask mode.** It strips MCP access, which disables the MCP-backed investigators and the synthesizer's citation spot-checks. Investigators still shouldn't write anything.

Operating Posture

Operate as a **careful, cautious, and precise investigator**. Be honest about what you know vs what you're inferring. Read `references/epistemics.md` for the full confidence framework and phrasing guide. The synthesizer must follow it.

Step 1. Understand the Target and the Question

The **target** is usually a chunk of code, a pattern, a feature, or a named design decision. The **question** is usually a design rationale, a tradeoff, a motivating edge case, an external constraint, dead code, or a broad history sweep.

If the target is vague ("why do we do it this way?" with no clear referent), make your best guess from conversation context (open files, recent edits, cursor location, what was just discussed). State your interpretation briefly so the user can redirect if you're off, then proceed.

Step 2. Establish the Code Anchor

Before spawning investigators, build the seed context inline: file paths and line ranges, key symbols (functions, classes, constants), the last few commits touching the target, PR numbers from merge commits (pattern `(#1234)` in the subject line), and any linked ticket IDs. Run `git blame -L <start>,<end> <file>` for last-touch commits, `git log --follow -p -- <file>` for full history with patches through renames, `git log --oneline -20 -- <file>` for recent commits with PR numbers visible, and `git log -1 --format=%B <commit>` to extract PR numbers. Then pull the PR bodies and discussion for any substantive commit: `gh pr view <number> --json title,body,author,createdAt,mergedAt,labels,closingIssuesReferences,comments,reviews`. This anchor goes to every investigator and to the synthesizer.

Step 3. Spawn Parallel Investigators

**Default to the full parallel investigation.** The goal is a complete coverage map, not a minimal one. Document the null, don't skip the search.

Discovery

Before spawning investigators, list the available MCPs from the Cursor environment. Use the available-tools map when present. Otherwise inspect the `mcps/` directory Cursor exposes for enabled MCP servers.

Map each available MCP to one evidence category:

1. Source control history 2. Issue / ticket tracker 3. Long-form documents 4. Real-time team chat 5. Infrastructure observability 6. Error / exception tracking 7. Product analytics warehouse

Source control is always available through git and `gh`. For the other six, classify using the MCP name, server instructions, tool names, and resource descriptors. If an MCP could fit more than one category, choose the one matching its primary evidence. Record ambiguous cases in the coverage map.

Spawn one investigator per category that has a matching MCP, all in a single message so they run concurrently, each owning exactly one tool or MCP. Don't ask one agent to cover multiple MCPs. `model`: the `why investigators` line, default `grok-4.7-xhigh-fast`.

Each investigator's prompt is `references/investigator-prompt.md` filled in, plus:

• the category playbook `references/sources/<source>.md` for the selected MCP, adapted from the examples in `references/source-playbook.md`
• `references/sources/incident-postmortem.md` **if the target code looks defensive** (null checks, retry logic, timeout handling, rate limiting, feature flags, egress guards, OOM handlers)
• the code anchor and the user's original question

Investigator roster, one per available evidence category

Each category below is uniquely positioned to surface one shape of "why", shown after "Best at". Use it to know what to expect back, to name a gap when a category returns empty, and, only in the rare provably-irrelevant case, to justify a skip.

1. **Source control** (git history, `gh` for PRs, code comments, tests). Always spawn, the only guaranteed source. Best at *implementation-time rationale captured during review*. 2. **Issue / ticket tracker** (e.g. Linear, Jira, GitHub Issues, Plane, Shortcut MCP). Best at *the product or business forcing function*. Strongest when the why is external to engineering. 3. **Long-form documents** (e.g. Notion, Confluence, Google Docs, Coda MCP). Best at *long-form design rationale*, written out before it becomes code. 4. **Real-time team chat** (e.g. Slack, Discord, Microsoft Teams, Mattermost MCP). Best at *real-time deliberation that never reached a doc*. Especially important when the source control, ticket, and doc paper trail is thin. 5. **Infrastructure observability** (e.g. Datadog, New Relic, Honeycomb, Grafana, Splunk MCP). Best at *infrastructure and runtime reality that mot