258건 · 관련도순
Classify outstanding pull request CI failures, automated checks run on code changes, using logs and changed files. Produce JSON decisions recommending a rerun, a comment, or no action, and identify specific nondeterministic tests when evidence supports it.
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Investigate 403 and 500 errors in Google Cloud Gemini Enterprise Agent Platform. Examine logs, registered destinations, agent identities, and authorization policies to produce a diagnostic report with evidence, recommended fixes, and verification steps.
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Review daily Google Ads and Meta Ads results for budget pacing, declining performance, and unusual activity. Get prioritized findings with supporting numbers and concrete recommendations for a marketing team to assess and carry out.
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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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Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.
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Find a starting set of community legal skills through an interview about role, practice area, industry, and daily work. Review recommendations, install selected skills, and save preferences for trusted sources, updates, and available integrations.
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Create an AI impact assessment in your team's format. Gather system details through conversation, analyze risks and applicable regulatory classifications, check consistency with internal policy, and document recommendations, safeguards, and conditions for deployment.
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Validate a Kiln release candidate with standard code checks and selected tests against live APIs. Diagnose failures, inspect outdated model selections and coverage gaps, and produce a report with recommended actions without changing code or tests.
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Use this skill to run multiple Simulink simulations in parallel with parsim, batchsim, and DesignStudy. TRIGGER when: user asks for parameter sweep, sweeping/varying parameters across simulations, Monte Carlo study, batch simulation, running multiple simulations, parallel simulations, large-scale multiple simulations, any task involving repeated simulations with different parameter values, user mentions having an existing script/function for parameter sweeps, or user wants to migrate from parfor/set_param/for-loop patterns to recommended workflows.
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RFP creation, distribution, evaluation, and shortlisting for in-house legal teams selecting outside counsel. Draft a new legal services RFP from scratch, evaluate firm responses against weighted criteria, produce a shortlist recommendation with selection rationale for GC sign-off, or design the end-to-end RFP process from scratch. Trigger on: 'draft an RFP', 'run an RFP process', 'evaluate firm responses', 'score the RFP submissions', 'which firms should we shortlist', 'selection recommendation', 'how do we run a panel RFP', 'RFP for legal services', 'pitch process', 'firm selection process', 'compare the proposals', 'we're reviewing our panel', 'panel refresh RFP', 'we need to go to market', 'request for proposal', 'evaluate the pitches', 'which firm won the RFP', 'write a legal RFP', 'RFP evaluation criteria', 'design the RFP process', 'how do we weight the criteria'.
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Fix Gorse recommendation engine pods crashing with "user: unknown userid 65532" error. Use when: (1) Gorse master/server/worker pods show CrashLoopBackOff or Error status, (2) Logs show "failed to get user directory" with unknown userid, (3) Running gorse containers with runAsNonRoot security context and custom UID. The gorse images call os.UserHomeDir() at startup which requires the UID to exist in /etc/passwd.
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Email triage system that handles both one-time setup and recurring triage in a single skill. Auto-detects state: if no knowledge base exists at ${WORKSPACE}/Email/, runs an 8-section onboarding interview (grill-me discipline, one question at a time) to build a 7-file personalized KB, then offers to run triage immediately. If the KB already exists, skips setup and goes straight to triage — searches recent emails, classifies them via the user's taxonomy, researches new senders, generates TAKE IT/WORTH/PASS/FLAG recommendations, drafts replies (NEVER sends), delivers a report, and updates the KB. Use with 'update' argument to refresh preferences. Setup triggers: 'set up my inbox', 'configure inbox triage', 'set up my email system', 'configure email triage', 'build my email knowledge base', 'initialize email management'. Triage triggers: 'triage my inbox', 'inbox triage', 'check my email', 'run email triage', 'process my inbox', 'what's new in my email', 'handle my email', 'email triage'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.
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Detect non-deterministic (flaky) tests by reading CI run logs or test result history. Aggregates pass rates per test, identifies intermittent failures, recommends quarantine or fix, and maintains a flaky test registry. Best run during Polish phase or after multiple CI runs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.
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Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.
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MCP 서버를 실행하지 않고 설정을 읽어 승인 후에도 다른 코드를 가져올 수 있는 참조를 확인합니다. npm, Python, Docker의 버전 지정을 분류합니다.
저장소 설정과 연결된 도구를 읽어 현재 사용할 수 있는 기능과 부족한 부분을 정리합니다. 다음에 추가할 ECC 스킬이나 연동을 우선순위에 따라 제안합니다.
사용자나 상황에 맞는 상위 후보를 고르는 추천 및 순위 처리 흐름을 설계합니다. 후보 수집부터 점수 계산, 선택, 기록까지 여섯 단계로 나누고 실행 가능한 코드 틀을 만듭니다.
AI의 조언·계획·견적·분석 뒤에 짧은 질문 두세 개를 덧붙입니다. 답변의 구체적인 내용을 바탕으로 행동 전에 숫자·논리·누락 정보를 검토하도록 돕습니다.
Analyze performance in Claude Flow swarms, groups of agents working together. Identify delays in communication, processing, memory, and networking, then generate reports and prioritized recommendations. Compare previous runs or apply available automatic optimizations.
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Design recommendation and ranking systems that select the best items for a user or situation. Organize retrieval, enrichment, filtering, scoring, selection, and background actions into six stages, then generate a runnable scaffold for your chosen stack.
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Review PostgreSQL queries and data designs using execution plans, which show how queries run, and targeted index recommendations. Get guidance on JSONB, arrays, full-text search, analytical queries, extensions, and routine database maintenance.
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Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
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