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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 Diagnose failures, inspect outdated model selections and coverage gaps, and produce a report with recommended actions without changing code or tests.
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Turn a business scenario into a compliance checklist covering GDPR and Chinese privacy, data, and advertising regulations. Organize legal references, risk levels, current status, and recommended actions into a prioritized remediation plan.
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Runs HTTP load tests with gradually increasing concurrent connections. It measures response-time percentiles, throughput, and errors, identifies where performance starts to deteriorate, and recommends a concurrency level based on the results.
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Review OpenAPM specification changes through four specialist perspectives covering schemas, registries, dependency resolution, and web architecture. The skill combines findings and mechanical checks into one advisory comment, separating immediate fixes from work deferred to later versions.
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Review an APM pull request through specialist agent perspectives selected for the changed areas. Findings are combined into one advisory comment with follow-up priorities and a shipping recommendation, leaving the final decision to maintainers and authors.
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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'.
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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.
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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.
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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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读取MCP配置，在不运行服务器的情况下检查批准后仍可能获取不同代码的包引用，分类npm、Python和Docker版本指定，并建议固定到团队已审阅的版本。
读取仓库设置和智能体运行环境，盘点已连接工具与可用功能，区分缺少工作流程和缺少外部集成的情况，并按优先级提出三至五项ECC后续建议。
将按用户或情境选择优先候选项的推荐与排序流程，拆分为获取、补充信息、过滤、评分、选择和记录六个阶段，并生成可运行的代码框架。
在AI的建议、计划、估算或分析后附上两三个简短问题，帮助用户在行动前检查数字、逻辑与缺失信息。问题紧扣回答中的具体内容，可直接向代理追问。