Why

Investigate the motivation and intent behind code.

Companion to the how skill. how answers what the code does and how it works. why answers what forces led to its shape.

Each spawn below names a role from the pstack_models tool. Call pstack_models with { role: "<role>" } and pass the returned selector as the model field of the task item. If the tool is absent, read rule://pstack-models when it exists and use the role's line, otherwise the default alias. If a selector is rejected, use the role's default alias and say so.

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

Parse what the user is asking. 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, 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, anchor the investigation in concrete code. You need:

• The relevant file path(s) and line range(s)
• The key symbols (function names, class names, constants)
• An initial commit list. The last few commits touching the target.
• PR numbers from merge commits (pattern (#1234) in the subject line)

Build this inline.

# Blame target lines for last-touch commits
git blame -L <start>,<end> <file>

# Full file history, with patches, through renames
git log --follow -p -- <file>

# Last N commits touching the file, PR numbers visible
git log --oneline -20 -- <file>

# Extract PR numbers from a commit message
git log -1 --format=%B <commit>

Pull PR bodies and discussion via gh for any substantive commits:

gh pr view <number> --json title,body,author,createdAt,mergedAt,labels,closingIssuesReferences,comments,reviews

Capture this as seed context (file paths, symbols, commits, PR numbers, linked ticket IDs). Pass it to the investigators.

Step 3. Spawn Parallel Investigators (default posture)

Default to the full parallel investigation.

Discovery

Before spawning investigators, list the MCP tools this session exposes. Use the tool list already in context; MCP tools are named after their server. Inspect the mcp:// resources when a server offers them. Then check whether the tools reach the task subagents you are about to spawn, and if a tool does not reach them, record that as a gap rather than silently skipping the category.

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.

Aim for a complete coverage map, not a minimal one. Document the null, don't skip the search.

Launch all matching investigators together so they run concurrently. Don't ask one agent to cover multiple MCPs.

Spawn all investigators together with eval workpool("task", { name: "why-investigators", context: <question plus code anchor> }) and one .push(...) per category, or as one tasks[] batch on the task tool. Subagent config (each):

• agent: task. Not scout: investigators need bash for git and gh and need the session's MCP tools, which scout does not have. Investigators still shouldn't write anything, and the prompt says so.
• model: role why-investigators, default @smol
• name: one CamelCase name per category
• solutionSpace: how open the search is for that source

Each investigator gets:

1. The base prompt from references/investigator-prompt.md
2. The category playbook references/sources/<source>.md for the selected MCP, adapted from the examples in references/source-playbook.md
3. The cross-cutting 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)
4. The code anchor from Step 2 (file paths, symbols, commit hashes, PR numbers, ticket IDs)
5. The user's original question

Investigator roster. One per available evidence category

Spawn one investigator per category that has a matching MCP. Each owns exactly one tool or MCP.

Each entry names the category and the kind of "why" it uniquely surfaces. Use it to know what to expect back, how to name a gap when a category returns empty, and (only in the rare provably-irrelevant case) to justify a skip.

1. Source control investigator. Git history, gh for PRs, code comments, tests. Always spawn. The only guaranteed source. Best at surfacing implementation-time rationale captured during review.
2. Issue / ticket tracker investigator (e.g. Linear, Jira, GitHub Issues, Plane, Shortcut MCP). Best at surfacing the product or business forcing function. Strongest when the why is external to engineering.
3. Long-form documents investigator (e.g. Notion, Confluence, Google Docs, Coda MCP). Best at surfacing long-form design rationale. Where the why is written out before it becomes code.
4. Real-time team chat investigator (e.g. Slack, Discord, Microsoft Teams, Mattermost MCP). Best at surfacing real-time deliberation that never reached a doc. Especially important when the source control, ticket, and doc paper trail is thin.
5. Infrastructure observability investigator (e.g. Datadog, New Relic, Honeycomb, Grafana, Splunk MCP). Infra/runtime view. Best at surfacing infrastructure and runtime reality that motivated the code. Strongest when the target reacts to an infra signal (timeouts, retries, rate limits, circuit breakers).
6. Error / exception tracking investigator (e.g. Sentry, Rollbar, Bugsnag, Airbrake MCP). Best at surfacing the specific exceptions and error trajectories that motivated defensive or corrective code. Strongest for catch blocks, null guards, type checks, retries, and other defenses.
7. Product analytics warehouse investigator (e.g. Databricks, Snowflake, BigQuery, ClickHouse, dbt, Redshift MCP). Product/data view. Best at surfacing product and data reality that shaped the code. Strongest for flag-gated code, experiment-driven ships, data migrations, and "where did this number come from" questions.

When to skip an investigator

Only skip with an explicit, written justification that goes in the final "Sources Consulted" section. Two valid reasons:

• No MCP is available for that category in this environment. Flag this as a gap, not a choice. Example: "Real-time team chat skipped. No matching MCP available, so the conversational record was not searchable."
• The source is provably irrelevant, not just "probably irrelevant." A high bar. Example: "Error / exception tracking skipped. Target is a build-time script with no runtime code path."

If your scope assessment suggests a single-commit trivial target where the PR description already contains the complete answer, you may answer inline only after confirming all seven available category searches would be redundant. Say so explicitly. This should be rare.

Step 4. Synthesize

Spawn one synthesizer subagent:

• agent: task
• model: role why-synthesizer, default @slow

The synthesizer's quality check spot-verifies citations, which can require MCP access or gh, so it uses the full-access task agent. It still writes nothing.

The synthesizer gets:

1. The investigator findings, including any null results and any categories skipped with justification
2. The code anchor from Step 2 (file paths, symbols, commit hashes, PR numbers, ticket IDs)
3. The user's original question
4. The epistemics framework from references/epistemics.md
5. The synthesizer prompt template from references/synthesizer-prompt.md

Step 5. Present

Take the synthesizer's output and present it to the user. You may lightly edit for clarity or add context from the conversation, but do not rewrite the confidence language.

Output Format

The output structure is the one in references/synthesizer-prompt.md: The Question, The Code in Question, What We Found, What We Can Reasonably Infer, Competing Hypotheses, What We Don't Know, Sources Consulted, Confidence Summary. Adapt as needed, but keep the confidence separation intact, and keep Sources Consulted as one line per investigator, including the ones that returned nothing or were skipped, with the reason.

After the Sources Consulted block, if the user's why question is a precursor to actually changing this code, convert the lineage findings into a Preserve / Change / Avoid / Risk constraint set suitable for planning the change.

Common Failure Modes to Avoid

• Recency bias. Assuming the most recent commit is authoritative. The current shape is often the accretion of many earlier decisions. Trace back.

Reference Files

• references/epistemics.md. Confidence tiers and phrasing guide. The synthesizer must follow it.
• references/investigator-prompt.md. Base prompt template for investigator subagents.
• references/source-playbook.md. Index pointing at the category playbooks below.
• references/sources/*.md. One self-contained example playbook per category, plus cross-cutting incident-postmortem.md. Give an investigator the single file that matches its category and adapt it to the available MCP.
• references/synthesizer-prompt.md. Prompt template for the synthesizer subagent, including the output format.