why

Files (14)

• LICENSE
• SKILL.md
• epistemics.md · references/
• investigator-prompt.md · references/
• source-playbook.md · references/
• code-archaeology.md · references/sources/
• databricks.md · references/sources/
• datadog.md · references/sources/
• incident-postmortem.md · references/sources/
• linear.md · references/sources/
• notion.md · references/sources/
• sentry.md · references/sources/
• slack.md · references/sources/
• synthesizer-prompt.md · references/

SKILL.md (first lines)

# 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.

## 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.

```bash
# 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:

```bash
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 servers this session can reach: every `mcp__<server>__*` tool in the tool list, deferred tools included (a deferred tool's schema loads through ToolSearch), plus each server's instructions.

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`: `general-purpose`, the type that carries MCP tools. Investigators write nothing.
- `model`: `haiku`

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`: `general-purpose`, so its quality check can spot-verify citations through the MCPs
- `model`: `opus`

From HexSleeves/skills, MIT.

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