'recommend run' 검색 결과

Recommend runtime and V2 scene management: run recommendation requests, manage recommend scenes and rules, and verify the deployed recommendation path.

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Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

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• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age

Audit GitHub Actions workflows and run history to identify wasted automated checks and runner time. Get up to three evidence-based improvements aimed at reducing runtime or cost while preserving required validation.

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• Investigate long GitHub Actions runs
• Review CI costs and unnecessary workflow runs
• Improve caching and cancellation of overlapping runs

Design Google Cloud infrastructure around application needs, scale, and budget. Get service recommendations, cost estimates, infrastructure configuration files, and deployment guidance, with support for automated delivery and security configuration reviews.

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• Designing a mobile backend on Cloud Run
• Planning a SaaS architecture on GKE
• Building an event analytics pipeline with BigQuery

Find WMI event subscriptions that let attackers retain access to Windows systems. Correlate Sysmon events with stored subscriptions and Autoruns results, inspect execution triggers and commands, and document risks and recommended remediation.

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• Triaging Sysmon alerts for events 19, 20, and 21
• Finding WMI persistence during incident response
• Distinguishing suspicious subscriptions from legitimate settings

Run a Codex prompt on a cron schedule or at a specific future time. Codex app Automations is the official, always-on surface for durable recurring schedules — recommend it FIRST; this local skill is a terminal fallback that only fires while its daemon runs. Triggers include "/schedule", "cron", "every Monday 9am", "run this at 3pm", "schedule a codex prompt", "recurring codex task", and one-time future runs ("run once at 3pm", "remind me to check X tomorrow"). For fixed-interval repeats ("every N minutes"), use the separate `loop` skill instead.

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Evaluate a Claude Managed Agent against a written rubric, a set of checkable criteria, and guide improvements within a fixed attempt limit. After a version passes, prepare separate evaluation cases to check performance beyond the examples used for revision.

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• Defining checkable criteria for agent outputs
• Improving an agent within a fixed attempt limit
• Testing a passing version on unseen cases

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

Investigate failed workflows and tasks in Managed Service for Apache Airflow. The skill compares remote logs, task states, and source code to explain failures, show supporting evidence, and recommend specific code or configuration fixes.

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• Investigating a failed DAG run
• Diagnosing task timeouts and queue delays
• Checking service errors against DAG code

Assess reported phishing emails by checking sender authentication, links, and attachments. Post a risk rating, supporting evidence, and a recommended action to Slack, leaving blocking, deletion, and other changes to the security team.

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• Triage newly reported suspicious emails
• Reassess a specific reported email
• Explain the evidence behind a risk rating

Inspect Mac performance to find processes using excessive CPU, GPU, memory, or energy. Combine measurements with explanations of known system processes to diagnose slowness, heat, and startup load, then recommend actions without changing system settings.

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• Find the cause of a slow Mac
• Investigate loud fans and overheating
• Check memory pressure and swap activity

Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文（用于「文献综述工具选型」与「一键安装/运行」）。

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Turn a skill's recommendations into real, executed actions — open the tickets, file the issues, post the updates — safely: dry-run preview, risk-classified, approval-gated, then recorded back to the brain. Use when asked to act on a plan, file tickets from a checklist, create issues from a PRD, execute the recommended next steps, or wire a skill's output into GitHub/Linear/Slack. Produces a dry-run actions plan with per-action risk, executes only after approval via the connected action MCP, and logs what was done. Nothing acts silently.

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Enforce Biome formatting and lint discipline in this repository. Use when making any file edits, when asked to run format/lint, and immediately before any commit. Always execute `bun run lint` (from package.json), apply resulting fixes, and re-run until clean. Treat `bun run lint:links` as optional by default, and recommend it when files under `content/` are changed.

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Reads invocation logs from eval_tracker and recommends which of the 50+ skills to prune or consolidate. Run monthly to keep the skill surface tight. Triggers: "audit skills", "prune skills", "which skills do I never use", "skill cleanup".

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Evaluate recommender system quality for the CSX4207 Vinyl Record Store. Use whenever you must compute or report recommender metrics (Precision@k, Recall@k, HitRate@k, MRR, MAP@k, NDCG@k, coverage, diversity, novelty, serendipity, personalization), design an evaluation protocol/split, run a baseline comparison, or write the evaluation section of a course deliverable. Covers formulas, JavaScript reference implementations, and the reporting checklist.

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사용 가능한 전문 에이전트를 분야별로 살펴보고 최대 5개를 골라 같은 과제를 병렬로 맡깁니다. 결과와 공통 의견, 제안의 차이, 다음 조치를 한 보고서로 정리합니다.

• 사용 가능한 전문 에이전트 확인
• 서로 다른 분야의 에이전트 선택
• 공개 전 사이트의 보안과 SEO 점검

Review an AI team's work against its acceptance criteria using collected evidence. Match requirements to task results or artifacts, distinguish verified facts from agent claims, and identify uncertainty or missing work before recommending completion.

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• Review evidence before closing an AI team run
• Check whether deliverables meet acceptance criteria
• Separate verified results from agent claims

Review changes since the last npm release through parallel change analysis, hands-on quality checks, and an overall release assessment. Receive a readiness verdict, blocking issues, version recommendations, and a draft changelog with compatibility concerns.

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• Assess readiness before an npm release
• Check breaking changes and migration needs
• Review version bump recommendations

Review existing tests and CI, the automated checks run during development, for redundant coverage, misplaced tests, and opportunities to reduce runtime or cost. Receive file-specific recommendations based on documented improvement patterns, with uncertain findings separated.

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• Investigating a slow test suite
• Finding redundant or misplaced tests
• Reviewing CI runtime and cost

Review product performance by comparing usage, retention, revenue, and other metrics with previous periods and targets. Turn the numbers into a scorecard, explain meaningful trends and uncertainties, and recommend investigations or improvement experiments.

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• Prepare weekly, monthly, or quarterly metrics reviews
• Investigate sudden spikes or drops
• Compare product performance with targets

Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.

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• Add support for a specific language model
• Check whether listed models are still available
• Update provider model identifiers

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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• Sorting outstanding CI failures on pull requests
• Identifying transient failures suitable for reruns
• Checking whether PR changes caused a failure

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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• Investigating 403 errors when an agent calls an external API
• Diagnosing Agent Runtime 500 errors involving Model Armor
• Checking registration and permissions for failing MCP connections