“root-cause”的搜索结果

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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• Reproducing and fixing reported SDK bugs
• Investigating failing tests
• Fixing regressions in automated CI checks

Review an existing bug-fix pull request using validated reproduction tests and the complete code diff. Assess root-cause coverage, scope, and regressions, then document behavioral evidence and follow the workflow's branch and approval rules.

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• Independently verifying an existing bug-fix pull request
• Checking a complete diff for scope and root-cause coverage
• Recording the user journey on the proposed fix

Writes a single failing test that reproduces a bug after its root-cause analysis is complete, before any fix is written. TRIGGER when: a bug has a completed root-cause analysis and you need the failing reproduction test, writing a test that proves a bug exists, the second step of the bug-fixing pipeline. DO NOT TRIGGER when: still triaging or diagnosing the bug → opsmill-dev-analyzing-bugs; implementing the fix once the test exists → opsmill-dev-fixing-bugs; general feature test-first work → superpowers test-driven-development.

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Use when a framed problem needs root-cause investigation rather than a symptom-level fix, applying Five Whys, Fishbone (Ishikawa), Current Reality Tree, and constraint identification. This should trigger when an issue's Root Cause Analysis point of view needs evaluation, or when a maintainer directly asks to find the root cause of a problem before proposing a fix. Part of Plinth Toolkit

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Root-cause debugging discipline. Use when a test, build or pipeline fails, behaviour does not match expectations, a bug task arrives, or a task comes back in need_revision — before proposing any fix.

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Systematic root-cause isolation and minimal fix proposal for software bugs.

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Investigate root causes and manage CAPAs (Corrective and Preventive Actions) for compliance deviations. Covers investigation method selection (5-Why, fishbone, fault tree), structured root cause analysis, corrective vs preventive action design, effectiveness verification, and trend analysis. Use when an audit finding requires a CAPA, when a deviation or incident occurs in a validated system, when a regulatory observation needs a formal response, when a data integrity anomaly requires investigation, or when recurring issues suggest a systemic root cause.

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Investigate why a defect or insufficiency was introduced and accepted by tracing code, pull requests, task context, and accessible development sessions. Use for second-order learning after a fix, blameless root-cause analysis, or preventing a recurring class of engineering mistakes.

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Investigate failing tests, broken builds, and unexpected behavior through a structured debugging process. Reproduce the failure, narrow down its source, fix the root cause, and add a regression test that catches the same problem.

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• Investigating a test that fails after a code change
• Finding the cause of a broken build
• Tracing a feature that stopped working

Investigate failed GAIA benchmark questions by inspecting execution records and classifying causes such as missing tools, reasoning errors, or answer extraction bugs. Follow targeted fixes, verify them through another run, and save findings for future debugging.

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• Investigate an incorrect answer for a specific task
• Diagnose a question that timed out
• Investigate a drop in benchmark pass rate

Analyze Antigravity coding session artifacts to explain scope changes, repeated rework, and underlying causes. Produce an evidence-based report with confidence levels, prompt assessments, recurring problem areas, and prioritized recommendations for improving future development sessions.

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• Reviewing sessions with repeated rework
• Separating scope changes from agent and codebase problems
• Finding files and subsystems associated with recurring friction

Investigate software failures by reproducing the problem, reducing the failing example, and locating its root cause. Receive an evidence-based diagnosis that separates symptoms from causes, with fixes applied only when authorized.

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• Investigating a crash
• Diagnosing intermittently failing tests
• Tracing incorrect output

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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• Investigating a classified ledger reconciliation mismatch
• Comparing posting dates and exchange rates on both sides
• Identifying account mapping errors or duplicate transactions

Monitor Power Automate across an organization's tenant using FlowStudio MCP's cached data. Review failure rates, run-health trends, flow and app inventories, and inactive owners to prepare health and compliance reports. Requires FlowStudio for Teams or MCP Pro+.

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• Identify flows with high failure rates across the organization
• Review run-health trends over a selected period
• Inventory environments, flows, apps, and connections

Inspect existing LLM application records in Arize to investigate errors, slow operations, or behavior changes. Export traces, which group related operations, or individual operations and conversation sessions as JSON using the ax command-line tool.

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• Investigate errors in an LLM application
• Review the inputs and outputs of a conversation
• Find slow operations in recorded traces

When a user corrects a factual error, trace it through stored knowledge, memory, identity settings, and fact records. Fix the underlying source, check related records where needed, and report the cause, changes, and safeguards against recurrence.

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• Tracing the source of a repeated factual error
• Correcting mistaken facts about people or companies
• Updating outdated information and related records

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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• Triaging a newly reported bug
• Tracing the root cause of unexpected behavior
• Investigating a regression and its originating change

Map repeatable business processes by owner and analyze time spent working, waiting, or reworking. Using stage-level timing data, produce a process diagram, ranked bottlenecks, and cycle-time measures to focus improvements on the main constraint.

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• Documenting procurement and employee onboarding workflows
• Identifying bottlenecks in slow business processes
• Making waits between teams visible

Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.

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• Reviewing retention metrics and reasons for churn
• Allocating support investment across customer tiers
• Estimating customer success staffing needs

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

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• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

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 failed or slow BigQuery jobs
• Locate bottlenecks in execution stages
• Diagnose sudden increases in query or capacity costs

Review Apache ShardingSphere pull requests or authorized downstream changes using repository evidence. Assess code correctness, merge readiness, or CI failures, then produce consolidated findings or a ready-to-copy reply to a review discussion.

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• Review code changes for correctness and regression risks
• Assess whether a pull request is ready to merge
• Investigate CI failures

Investigate Apache ShardingSphere issues using official documentation, code, tests, and issue discussions. Distinguish bugs, unsupported usage, duplicates, and enhancement requests, then draft a concise maintainer-style reply explaining the evidence and next action.

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• Distinguish bugs from unsupported configurations
• Check whether an issue duplicates an earlier report or fix
• Draft a GitHub issue reply with clear next steps

Investigate production errors using Sentry stack traces, which show the code execution path, and events leading up to the failure. Identify likely causes, triggering conditions, affected users, and releases before changing code.

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• Investigating an error from a Sentry link
• Finding what triggers user crashes
• Checking affected users and releases