Why

On Codex, read the platform mapping, including its per-skill notes, before following this skill.

On GitHub Copilot, read the platform mapping, including its per-skill notes, before following this skill.

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 pstack-models.md and a default in 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.

Every spawn is general-purpose 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 in the Claude Code environment. Use the tool list at the top of the system prompt (every MCP appears as a tool with prefix mcp__<server>__<name>). Otherwise read .mcp.json in the plugin/project, or run claude mcp list.

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 in Models.

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 motivated the code. Strongest when the target reacts to an infra signal (timeouts, retries, rate limits, circuit breakers).
6. Error / exception tracking (e.g. Sentry, Rollbar, Bugsnag, Airbrake MCP). Best at 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 (e.g. Databricks, Snowflake, BigQuery, ClickHouse, dbt, Redshift MCP). Best at 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 with an explicit, written justification that goes in the final Sources Consulted. Two valid reasons:

• No MCP is available for that category in this environment. Flag it as a gap, not a choice: "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: "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. model: the why synthesizer line, default in Models. It gets:

1. The investigator findings, including any null results and any categories skipped with justification
2. The code anchor
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

Present the synthesizer's output 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 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 Sources Consulted, if the user's why question is a precursor to changing this code, convert the lineage findings into a Preserve / Change / Avoid / Risk constraint set 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.

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.

• why investigators: opus
• why synthesizer: 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.