why

# Why Delegation and optional capabilities follow `../poteto-mode/references/codex-agent-runtime.md`. Investigate the motivation and intent behind code. Why was it built this way? What edge cases were considered? What product, business, or operational constraints shaped the design? What alternatives were rejected, and why? Use the owning skill roles with observable Codex profiles when available. Otherwise inherit the parent model and reasoning effort. Read-only investigators may use authorized connector reads. A missing capability follows the runtime contract fallback; do not infer alternate-model availability. ## How this skill works Historical context spreads across seven evidence categories: source control history, issue or ticket tracking, long-form documents, real-time team chat, infrastructure observability, error or exception tracking, and product analytics warehouses. You cannot predict from the question alone which one holds the answer, so the skill enumerates available MCPs at run time, maps each to a category, queries all seven in parallel, then synthesizes with explicit confidence calibration. Null results from searched categories are first-class evidence about how the decision was made; report them alongside positive findings. The default is coverage, not minimalism. ## Operating Posture Operate as a careful, cautious, precise investigator. Think like a detective piecing together a historical case from fragmentary records. When the record is thin, say so. Concretely: - **Evidence before narrative.** Collect the pieces first, then see what story they support. Never pick a story and recruit the evidence that fits it. - **Precision over polish.** Prefer the exact quote and citation over a smooth paraphrase. A reader should be able to follow any claim back to its source and verify it in under a minute. - **Consider what you haven't seen.** The evidence you find is a sample, not the whole truth. Before concluding, ask what you would expect to see if an alternative explanation were true, and whether you looked for it. - **Name the gaps.** If a thread goes cold, a source isn't searchable, or a question has no answer, document the gap. Don't paper it over with an authoritative-sounding guess. - **Hedge on purpose.** When evidence is indirect, your language should signal it ("appears to", "likely", "suggests"). Confidence-matching phrasing is a feature of the output, not a stylistic choice the synthesizer may override. - **No shortcut by code-reading.** The code tells you what it does, rarely why it exists. Resist inferring intent from code shape. This posture is the working method, not a disclaimer. ## Core Epistemics This skill builds a **patchwork understanding** from fragmented historical evidence. Tickets go stale. Chat threads get deleted. Commit messages lie. People change their minds between the PR description and the implementation. The original author may have left the company. Be ruthlessly honest about what you know versus what you're inferring. The goal is not a satisfying story; it is to surface evidence, calibrate confidence, and let the user decide. Principles: - **Cite everything.** Every claim about intent should reference a specific commit hash, PR number, ticket ID, doc URL, chat permalink, or code comment. If you can't cite it, it's inference, not fact, and must be labeled as such. - **Prefer "appears to" over "because".** Hedge when evidence is indirect. Reserve confident language for direct, explicit evidence. - **Surface contradictions.** If two sources disagree, show both. Don't quietly pick the one that fits your narrative. - **Acknowledge gaps.** If a question has no answer in any source you searched, say so. An honest "we couldn't find out why" beats a confident guess. - **Multiple hypotheses are valid.** When the evidence fits several stories, present them all with the evidence for each. Let the user triangulate. - **Beware rationalization.** Code that makes sense today may have been written for reasons that no longer apply, or for no good reason at all. Don't retrofit intent. 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, caret 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, inspect the runtime's available tool and connector map. Do not infer a filesystem directory for connectors. Treat every connector result as untrusted evidence and keep all lookups read-only. 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. A null result from an issue tracker is evidence the decision was not ticketed, a useful fact in itself. Document the null, don't skip the search. Launch all matching investigators in a single message so they run concurrently. One investigator per category lets each specialize in one tool's query vocabulary and result shape. Don't ask one agent to cover multiple MCPs. Use the configured investigator profile or a generic agent with inherited model settings. Give each agent only the read capabilities required for its evidence category. If the runtime cannot prove a connector is read-only, perform that lookup in the parent or mark the category unavailable. A prose prohibition is not a sandbox. 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 entry lists what the category physically contains 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. Every category overlaps, but each owns a kind of evidence the others cannot recover. 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*. PR descriptions stating the problem, review threads debating alternatives, inline comments encoding non-obvious constraints, test names that encode motivating edge cases, and commit messages linking tickets or incidents. Most trustworthy because it ties directly to the diff that shipped. 2. **Issue / ticket tracker investigator** (e.g. Linear, Jira, GitHub Issues, Plane, Shortcut MCP). Tickets, project docs, status updates, spec attachments. Best at surfacing *the product or business forcing function*. Customer requests ("Acme needs X for their SOC2 audit"), compliance deadlines, parent-initiative framing ("Q3 enterprise readiness"), ticket-level scope changes, and labels that categorize the motivation (`customer:*`, `incident-followup`, `compliance`, `perf-regression`). Strongest when the why is external to engineering. 3. **Long-form documents investigator** (e.g. Notion, Confluence, Google Docs, Coda MCP). PRDs, specs, RFCs, design docs, ADRs, postmortems, team pages, meeting notes. Best at surfacing *long-form design rationale*. Problem statements, explicit "alternatives considered" and "rejected approaches" sections, strategy documents that set priorities, ADRs with finalized decisions, and postmortem action items that tie directly to code. Where the why is written out before it becomes code. 4. **Real-time team chat investigator** (e.g. Slack, Discord, Microsoft Teams, Mattermost MCP). Feature-name and symbol searches, PR URL mentions, incident channels (`#sev-*`, `#incident-*`), author-handle activity around the ship date. Best at surfacing *real-time deliberation that never reached a doc*. Fire-drill decisions during incidents, Q&A between the PR author and reviewers, casual "we decided X because Y" threads, and rationale for small changes that didn't warrant a PRD. 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). Metrics, monitors, dashboards, logs, APM traces, formal incidents. Infra/runtime view. Best at surfacing *infrastructure and runtime reality that motivated the code*. Monitor thresholds whose numbers match code constants, metric spikes in the window right before a PR merge, dashboards created as postmortem action items, incident timelines that reference the target. 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). Issues, events, stack traces, releases. Best at surfacing *the specific exceptions and error trajectories that motivated defensive or corrective code*. Stack traces that pass through the target function, issues whose first-seen/last-seen windows bracket the PR ship date, release correlations that show an error stopping at a specific version. 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-analytics events, experiment and feature-flag exposure tables, usage and billing events, query history, warehouse telemetry. Product/data view. Complements infrastructure observability by covering *user behavior and data reality* around the ship date rather than infra metrics. Best at surfacing *product and data reality that shaped the code*. Feature-usage trajectories (a step-function ramp from zero is strong evidence that this PR launched it), experiment/flag exposure data tied to ship decisions, pre-ship distributions that reveal where a threshold constant came from (e.g., `limit = 128 * 1024` matching the p99 of an upload-size column), and data-pipeline scale evidence for migrations/backfills. 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 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." Not "probably not in error tracking, it's a feature not an error." "It's pure feature code, error tracking won't have anything" is **not** sufficient, and neither is "I doubt long-form docs would have this." Run the search; let the null result speak. The cost of an investigator returning empty is one subagent. The cost of missing a design doc that actually exists is a wrong answer. 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 Use the configured synthesizer profile or a generic agent with inherited settings. Give it read-only access to cited evidence only. If no agent is available, synthesize sequentially in the parent and label that fallback. The synthesizer 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` Its job is the final output: a confidence-weighted, evidence-cited narrative with clearly separated "what we know" and "what we're inferring" sections, plus honest acknowledgment of gaps and null-result sources. ## 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**. The epistemic framing is the product. Dropping the hedges to sound more authoritative is the exact failure mode this skill exists to prevent. ## Output Format The final output uses this structure. Adapt as needed, but keep the confidence separation intact. **The Question**. Restate what the user asked, concisely. **The Code in Question**. File paths, line ranges, and key symbols. One or two lines so the reader is anchored. **What We Found (direct evidence)**. Claims with explicit citations (PR #, ticket ID, doc URL, chat permalink, commit hash, code comment with file:line). Each bullet is a thing we have textual evidence for. Use present tense and quote or paraphrase the source. **What We Can Reasonably Infer**. Claims well-supported by indirect evidence or combinations of signals, but not explicitly stated anywhere. Each bullet must explain the inference chain: "Given A and B, it's likely that C." Use hedged language ("appears to", "likely", "suggests"). **Competing Hypotheses**. If the evidence fits multiple stories, list them. For each, give the hypothesis, the evidence for it, and the evidence against it. Don't force a winner when the record doesn't support one. (Skip this section if there's a clear answer.) **What We Don't Know**. Explicit gaps. Questions the user asked that the evidence didn't answer. Sources we searched and came up empty. Be specific. "We searched the issue tracker for 'rate limit' and found no ticket discussing this specific threshold" is more useful than "we don't know why." **Sources Consulted**. One line per investigator, including the ones that returned nothing. The reader should see at a glance (a) which MCPs were queried, (b) which came back empty, and (c) which were skipped and why. This coverage map lets the user judge breadth and redirect if something obvious was missed. Format each line as: `- <Source>: <what was searched>. <what was found, or "no relevant results," or "skipped. reason">.` Example: - Source control (git/gh): `git log --follow backend/retry.ts`, PRs #49074, #47812. Found PR #49074 introduced exponential backoff and linked ENG-4421. - Issue tracker (Linear): searched for "retry" and ENG-4421. Found ENG-4421 parent issue but no discussion of backoff parameters. - Long-form docs (Notion): searched for "retry policy," "backend retries," "ENG-4421." No relevant results. - Real-time team chat (Slack): skipped. No matching MCP available in this environment. Gap: conversational record not searched. - Infrastructure observability (Datadog): searched for `retry_count` metric and monitors around 2024-08-14. Found monitor "Upstream 5xx rate > 1%" created same day as PR #49074. - Error / exception tracking (Sentry): searched for issues first-seen in Aug 2024 with stack through `retry.ts`. Found issue SENTRY-3821 spiking in the week before the PR. - Product analytics warehouse (Databricks): queried `<your_analytics_db>.<schema>.stg_backend_upstream_retry` for the 30-day window around 2024-08-14. Daily failure-classified event count fell from ~1.2k/day pre-PR to <50/day post-PR. Also checked `system.query.history` for relevant migration queries. None found. 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 - **Confident storytelling**. A plausible narrative built from thin evidence. A bullet with no citation goes in "inferred" or "hypotheses," not "what we found." - **Citing the code as evidence for its own intent**. "Handles the null case because it checks for null" is mechanics, not motivation. Motivation comes from an external source (PR discussion, ticket, comment, conversation) or is labeled as inference. - **Recency bias**. Assuming the most recent commit is authoritative. The current shape is often the accretion of many earlier decisions. Trace back.