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why

Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds.

Skill by luckenco

IndexedMIT
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About

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.

Use cases: design, devops, data

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What's inside

Skill 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.
License
MIT
Instructions
147 lines in SKILL.md · view on GitHub

Files (13)

  • 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.

Use Pinnacle's `subagent` tool. Each child has a fresh conversation: supply the question, code anchor, source playbook, and the exact reference paths to read. Choose each task's model. Investigators should not write to the repository or remote services.

## 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. In a Jujutsu repo, prefer `jj file annotate <file>`, `jj log -p <file>` and `jj log -r 'ancestors(@, 20)' <file>`; use Git read-only history when following renames or exact pickaxe searches requires it. In a Git repo:

```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, run `pi mcp list --json` to list connected native MCP servers, then use `tool_search` to inspect their tools and instructions. Record the exact `mcp__<server>__<tool>` names each investigator may need. Do not infer availability from configuration alone. If native MCP is unavailable, record the missing categories.

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 available locally through `jj` or `git`; use `gh` only when authenticated and there is a GitHub remote. 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 one parallel `subagent` call (up to eight tasks, four concurrent). Don't ask one agent to cover multiple MCPs. Give source-control investigators `tools: ["read", "grep", "find", "ls", "bash"]`. Give MCP investigators a `tools` allowlist containing `read`, `grep`, `find`, `ls`, `tool_search`, and the exact native MCP tool names recorded for that server. A child's explicit allowlist also filters `tool_search`, so naming only `tool_search` hides every MCP tool. If the child cannot expose the required tools, investigate that source in the parent and record the limitation. No write tools for any investigator.

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 available category. Each owns exactly one source 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**. Jujutsu or Git history, `gh` for GitHub PRs when available, 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:

- Run one `subagent` task with the same source-specific read-only tool access used by investigators when spot-verification requires it. If MCP access is unavailable to the child, spot-verify citations in the parent before presenting them.

The synthesizer gets:
1. The investigator findings, including any null results and any categories skipped with justification

From luckenco/pinnacle, MIT.

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Last commit (this folder)
Oct 15, 2026 (13 days ago)

From GitHub (fetched Oct 24, 2026).

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License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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why skill. Agent Mart. https://agentmart.dev/skills/luckenco-why (last updated 2026-10-24)