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Create a structured review after a production incident, focusing on systems and processes without blaming individuals. Reconstruct events, quantify impact, investigate causes, and document specific follow-up actions with named owners and deadlines.
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Investigates GitHub or Linear issues through code, change history, and reproduction attempts. Produces a structured handoff covering causes, assumptions, effort, and impact, then requests the appropriate Factory workflow stage update when applicable.
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Analyze a difficult problem with two agents chosen for contrasting reasoning styles. They independently investigate causes and plan a solution, then exchange arguments to resolve disagreements and produce a shared recommendation without changing files.
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Investigate failing or slow BigQuery jobs and unexpected cost increases. Compare execution details and resource usage with a normal baseline to isolate bottlenecks, identify likely causes, and determine practical remediation steps.
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Investigate recurring problems by mapping feedback loops, the cycles through which effects influence their causes. Identify underlying structures, match known patterns, and assess where changes could help and what unintended effects they might produce.
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Investigate errors, crashes, and unexpected behavior by reproducing failures, tracing execution, and comparing logs. Tests one explanation at a time, then provides a verified fix, supporting evidence, and tests or safeguards to prevent recurrence.
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Investigate production incidents and bugs using logs, metrics, and code evidence. The skill guides hypothesis testing, root cause analysis, fix validation, and reusable investigation notes, with access to configured observability tools such as Axiom.
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Build an MCP integration skill using a template that reads server-specific reference notes before calling tools. The Sentry example gathers unresolved errors, groups them by likely root cause, and produces a report with investigation suggestions.
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Finds the root cause of a bug, failure or recurring problem by asking "why" until the answer is a cause you can fix. Use when the user asks why something keeps happening, or wants the root cause rather than a quick fix.
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Locate root causes of failing regression tests by analyzing code changes, error messages, and test dependencies. Use when regression tests start failing after code changes, investigating test failures in CI/CD, debugging flaky tests, or understanding why previously passing tests now fail. Analyzes git diffs, stack traces, test output, and dependency changes to produce structured markdown reports ranking likely causes. Triggers when users ask to find why tests are failing, debug regression failures, investigate test breakage, or analyze failing test suites.
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Finds the cause of a hard bug by running several read-only subagents in parallel, each attacking it from a different angle (change history, code path, data and state, environment, reproduction), then checking their evidence, ranking the hypotheses and naming the one experiment that would confirm the leader. It diagnoses and does not fix. Use when someone says "hunt this bug with multiple agents", "swarm on this regression", "investigate in parallel", "find the root cause", or when a failure is intermittent, crosses components, or has resisted a first look.
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Investigate the root cause of a recurring or systemic defect across a series of incidents, not a single one. Use on the third recurrence, or when someone asks 'why again?', 'five whys', 'fix the root'. Skip for one or two ordinary cases, an already proven simple cause, or a one-off external outage. Triggers: /five-whys, five whys, fix the root.
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Turn Strix vulnerability findings into focused code fixes, prioritized by severity. Address root causes, rerun security scans and original exploit examples, and report the changes and verification results for each issue.
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Investigate bugs and slow applications through reproducible examples, logs, environment comparisons, and hypothesis testing. The skill structures root cause analysis and performance measurement, helping developers narrow down failures and verify that a fix works.
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Investigate a mismatch between the general ledger and a supporting ledger by tracing both sides to their original entries. Compare transaction details and return a concise cause, responsible team, expected resolution date, and recommended action.
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Investigate failed Power Automate cloud flows through FlowStudio MCP. Inspect each action’s inputs and outputs, trace incorrect data upstream, fix expression or data issues, and replay a previous run to check the repair.
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Turn defects found during Oracle-to-PostgreSQL migration into structured bug reports. Document expected and actual behavior, reproduction steps, severity, root causes, proposed changes, and validation on both databases so teams can act on the findings.
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Guide a structured review after a security incident, using timelines and response data to identify root causes and process gaps. Produce review notes, response metrics, and practical improvements with named owners and deadlines.
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Fix defects in Composio SDK through focused reproduction, root-cause analysis, regression tests that check for recurrence, and targeted verification. Use it for reported bugs, failing tests, CI regressions, runtime errors, or incorrect SDK behavior.
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Investigate a GitHub issue or bug report by tracing code, checking relevant history, and testing the diagnosis where needed. Receive a concise verdict, supporting evidence, a proposed fix direction, and a record for follow-up questions.
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Repair an entire broken feature by mapping its files and dependencies, checking configuration and logs, and confirming root causes. Apply fixes in sequence, then test the feature and the code that uses it.
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Assess Web3 vulnerability findings before submission by checking scope, reproducibility, prerequisites, and attack costs. Organize the root cause, measurable impact, working proof of concept, and proposed fix into an Immunefi report, with examples for comparison.
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Investigate a bug's root cause, implement a focused repair, and verify the same behavior before and after the change. Select lasting regression tests, preserve reproduction evidence, and record the corrected user journey when possible.
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Investigate a reported bug before choosing a fix. Trace the actual failure, test competing explanations, and examine design history so the proposed change preserves intended behavior and clearly separates verified findings from unresolved assumptions.
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