why

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.

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 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.

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, 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, 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 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.

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

Launch all matching investigators in a single message so they run concurrently. Don't ask one agent to cover multiple MCPs.

Subagent config (each):

• subagent_type: patina-reader
• model: the why investigators line, default in Models
• readonly: false (agent mode). Do not use readonly/Ask mode. It strips MCP access, which disables MCP-backed investigators entirely. Investigators still shouldn't write anything.

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:

• subagent_type: patina-reader
• model: the why synthesizer line, default in Models
• readonly: false (agent mode). The synthesizer's quality check spot-verifies citations, which can require MCP access. Readonly/Ask mode strips MCPs and defeats that.

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.

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. patina-agent becomes subagent_type: "patina-agent-<level>". patina-reader becomes subagent_type: "patina-reader-<level>". general-purpose, or no subagent_type, becomes subagent_type: "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.