Why

作者 backnotprop3a604672c46c無授權條款1.3K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

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.

AI 產生的概覽

透過平行查詢程式碼庫、工單、文件、聊天、可觀測性、錯誤追蹤與分析資料,調查程式碼或設計決策背後的原因。

功能
這個技能會針對某段程式碼、某種模式或某個具名設計決策背後的動機,進行結構化調查。它先用 git blame、檔案歷史與拉取請求資料,把問題錨定到具體程式碼,再平行啟動多個調查子代理,每個子代理只查詢一類證據來源,例如原始碼控管、問題追蹤系統、長篇文件、團隊聊天、基礎設施可觀測性、錯誤追蹤或產品分析資料倉儲。接著由綜合子代理把各方發現合併成一份附引用的報告,內容涵蓋已發現的事實、可合理推論的結論、相互競爭的假設、未知項目、已查閱來源與信心程度摘要。當這個問題是為了後續修改程式碼做準備時,還會輸出一組保留、變更、避免、風險的條件限制。
適用情境
適用於「為什麼會這樣運作」「當初為什麼選這個做法」之類的問題,以及回歸問題、事後檢討和以資料為依據的門檻設定。它針對的是設計理由與取捨的問題,而不是程式碼在執行時做什麼的問題。
執行需求
需要 git 與 gh 命令列工具來建立程式碼錨點,並需要執行環境支援子代理;其他證據類別所需的 MCP 伺服器為選用,缺少時會記錄為涵蓋範圍的缺口。這個技能不含指令碼,只有參考用的 markdown 檔案,並會讀取一個選用的 pstack 模型設定檔來選擇子代理模型。

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 line in the pstack settings file (~/.cursor/rules/pstack-models.mdc in Cursor, ~/.agents/pstack-models.md in other harnesses) and a default. Set model to that line's value, or to the default if the file or the line is missing. Leave model unset when the value is auto or inherit-parent. If your subagent 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 or your harness's model list.

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.

bash
# Blame target lines for last-touch commitsgit blame -L <start>,<end> <file>
# Full file history, with patches, through renamesgit log --follow -p -- <file>
# Last N commits touching the file, PR numbers visiblegit log --oneline -20 -- <file>
# Extract PR numbers from a commit messagegit 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 connected to your harness. Use the available-tools map when present. In Cursor, you can also inspect the mcps/ directory it exposes for enabled MCP servers. In other harnesses, MCP tools usually appear in your tool list with the server name in the tool name (for example mcp__<server>__<tool> in Claude Code).

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.

Other harnesses. The spawns in this skill use Cursor's Task tool. In another harness, use its subagent tool: Agent in Claude Code (subagent_type: general-purpose), task in OpenCode (subagent_type: general), spawn_agent in Codex. Keep the prompt and the model. Drop parameters your tool doesn't have. If your harness has no subagent tool, as in Pi without an extension, run each investigator yourself, one after another.

Subagent config (each):

  • subagent_type: generalPurpose
  • model: the why investigators line, default grok-4.7-xhigh-fast
  • 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: generalPurpose
  • model: the why synthesizer line, default claude-opus-5-5-xhigh
  • 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.

來源與署名

來源:backnotprop/pstack位於skills/why提交3a60467

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