'failure' 검색 결과

General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.

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Use when running existing project validation commands and fixing failures after code changes, including lint, type-check, unit/integration test, build, CI, or local script failures. Do not use when the task is to design or author new component/E2E tests; Chinese triggers include validation, check failures, fix failures, CI failures.

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Knowledge base from the NASA Fault Management Handbook (NASA-HDBK-1002, Draft 2, April 2012). Use for the systems-engineering discipline of designing what a system does when it fails — the fault/failure/anomaly vocabulary, the five FM strategies (two prevention strategies — design-time fault avoidance and operational failure avoidance — plus three tolerance strategies — failure masking, failure recovery, and goal change), the six FM process activities across NASA mission phases Pre-A to E, response-latency-versus-time-to-criticality (TTC) design trades, fault/failure containment regions and FEPPs, the four redundancy approaches, top-down FM requirements development, FM verification vs. validation, the seven dedicated FM milestone reviews (FMCR/FMARR/FMPDR/FMCDR/FMTRR/FMLRR/FMCERR), FM organizational structure, and mined NASA Lessons Learned. Space-mission oriented (flight/ground/operations); spacecraft examples throughout. IMPORTANT: built from an UNAPPROVED DRAFT — several sections (detailed architecture building blocks, the Assessment & Analysis guidance, and Operations & Maintenance) are placeholders in the source and are thin here. Thin on detailed FDIR algorithms, terrestrial/IT fault management, and any approved-standard terminology that may have changed since Draft 2.

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Debugs failing GitHub Actions CI runs for PostHog PRs, commits, and branches, and answers broad CI-health questions ("is CI red?", "is master green today?", "what's broken right now?"). Use when the user asks why CI is red, asks for the current CI or master status, or mentions a failing check, GitHub Actions run, Depot runner, workflow, job, shard, merge queue kick, flaky test, lint failure, typecheck failure, snapshot diff, migration check, generated types drift, or skills build failure. Start with the `hogli ci:insights` digest (cross-run CI history from engineering analytics), then guides read-only inspection, failure classification, smallest local reproduction with hogli, and safe reporting without rerunning CI or posting to GitHub. Running unattended as the "Master-red diagnosis" workflow: see references/master-red-incident.md.

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TypeScript, Python, Go의 오류 처리를 설계하고 검토합니다. 오류 분류, 재시도, API 응답과 사용자 안내를 정리하고 개발자용 로그를 구분합니다.

• 새 서비스의 오류 분류를 설계합니다
• 외부 서비스의 일시적 장애를 재시도합니다
• API 오류 응답과 로그를 검토합니다

원인 불명의 버그, 간헐적 이상, 성능 저하를 코드 변경 전에 조사합니다. 관찰한 증상과 추측을 구분하고 증거로 가설을 좁혀 원인과 근거를 보고합니다.

• 원인 불명의 버그 조사
• 간헐적으로 발생하는 이상 원인 조사
• 성능 저하의 원인 파악

브라우저나 Electron에서 이미지가 저장되지 않거나 0 KB 파일로 내보내지는 문제를 조사합니다. 이미지 데이터 생성과 저장 경로를 나눠 원인을 찾고 수정합니다.

• 내보낸 이미지가 0 KB인 원인을 찾습니다
• 저장 대화상자와 쓰기 오류를 조사합니다
• Electron 이미지 저장을 수정합니다

Run or inspect local Release-Green checks for a specific code revision or combined pull request candidate. Report blocking failures, diagnostic differences, and missing evidence, compare against the base revision, and identify remaining CI checks.

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• Checking a pull request candidate before maintainer review
• Comparing failures against the exact base revision
• Separating blocking failures from diagnostic differences

Investigate intermittent Remotion CI failures, record their error patterns in a tracking issue, and increment counts for repeated failures. Find failed checks even without a supplied pull request, then rerun affected GitHub Actions jobs.

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• Investigate a CI failure that may pass on retry
• Find failed checks without a pull request number
• Record recurring failures and update their counts

Implement Python error handling with early input checks, appropriate exception types, and clear messages. Handle individual failures in batch jobs, preserve error context for debugging, and use Pydantic models to validate structured input.

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• Validating user input and API parameters
• Designing exception types and clear error messages
• Continuing batch jobs after individual failures

Prevent recurring coding-agent mistakes by turning repository evidence into specific instructions, automated checks, regression tests, and failure records. Also checks whether guidance has become outdated and documents changes, verification results, and follow-up work in an adoption report.

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• Create agent instructions tailored to a repository
• Turn recurring mistakes into regression checks and failure records
• Detect outdated guidance and broken verification commands

Monitor selected GitHub Actions checks on an SGLang pull request until they pass on its latest commit. Diagnose failures, push small validated fixes, and retry only failed jobs within defined limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitor selected PR checks until they pass
• Separate code failures from infrastructure problems
• Apply small fixes and track the resulting checks

Analyze Windows authentication logs for repeated password guesses against Remote Desktop services. Group failures by source IP and account, check for later successful logins, and produce a JSON report highlighting attack patterns and possible compromise.

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• Investigate repeated login failures on exposed RDP services
• Identify accounts targeted by password guessing
• Check for successful logins after repeated failures

Plan controlled failure experiments to uncover system weaknesses. Define normal operating metrics, estimate affected users and reliability risk, set abort and recovery conditions, and turn experiment results into a structured review with follow-up actions.

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• Designing a controlled failure experiment
• Estimating affected users and reliability risk
• Planning a team failure response exercise

Trace failing cmux Swift package tests to the commits that introduced them by running older revisions in CI, the automated testing environment. It helps distinguish outdated expectations, code regressions, and intermittent failures, then guides focused fixes and verification.

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• Investigating a package suite failing on main
• Finding the PR that first broke a test
• Distinguishing outdated tests from code regressions

Analyze repeated CI test runs to find tests that pass and fail without code changes. Summarize failure rates, investigate likely causes, and recommend fixes, monitoring, or temporary exclusion based on the available evidence.

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• Investigating intermittent CI test failures
• Comparing failure rates across repeated test runs
• Reviewing tests already marked for temporary exclusion

Classify a failed NemoClaw GitHub Actions job using logs with sensitive information hidden and output size limited. Optionally inspect saved artifacts to identify failure patterns and gather evidence for further investigation.

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• Classifying a failed GitHub Actions job
• Inspecting saved artifacts for failure evidence
• Gathering initial evidence for an E2E failure

Diagnose GKE TPU slice creation failures caused by shared nodes. Check slice status, partition labels, and monitoring data to identify conflicting resource groups, then provide resolution steps for the user to apply.

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• Investigating node overlap behind SliceCreationFailed
• Finding slices that share nodes across partition shapes
• Checking whether resource conflicts existed at incident time

Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.

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• Build evaluation datasets from conversation logs
• Generate simulated user scenarios
• Choose scoring criteria for responses and tool use

Helps AI agents recover from repeated failures by reviewing previous attempts, checking original sources, testing different hypotheses, and verifying results. It combines structured troubleshooting with performance review rhetoric modeled on large technology companies.

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• Breaking out of repeated failed attempts
• Investigating configuration or deployment failures
• Verifying an agent's claimed completion

Diagnose failed GitHub Actions runs by finding the first meaningful error, separating code problems from infrastructure issues and intermittent failures, and comparing the base branch. Get a focused report explaining the PR’s involvement and recommended next steps.

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• Investigating failed PR checks
• Separating intermittent test failures from code defects
• Checking whether the base branch already fails

Investigate failed GitHub Actions jobs using logs and recent runs. Receive a report explaining the actual failure trigger, recurrence, any verified breaking commit, and existing pull requests that may fix the issue.

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• Finding the actual cause of a failed job
• Checking whether failures are intermittent or recurring
• Identifying the commit that introduced a failure

Analyze agent runs that fail before any work begins. Check iteration counts, tool use, logs, and outputs to identify startup failures, then direct investigation toward environment setup, input formatting, permissions, or resource limits.

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• Investigating failed runs with zero iterations
• Checking startup errors across multiple agents
• Recording failures that require system review

Handle repeated tool failures by documenting what went wrong and which deliverables remain blocked. Produce useful partial work with clear limits, distinguish unverified content from completed work, and provide concrete steps for recovery or handoff.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reporting repeated PDF extraction failures
• Providing partial research with clear reliability limits
• Handing off blocked tasks with errors and next steps