'recommend run' 검색 결과

Runs and interprets AWS Resilience Hub v2 failure mode assessments. Covers starting assessments, understanding findings (severity, categories, recommendations), triaging by achievability, working with AI-generated service functions, and resolving findings. Applies when the user wants to run an assessment, review findings, or understand failure modes, or has a specific finding and asks how to resolve, remediate, or fix it. Does not apply to initial setup (use resilience-hub-getting-started) or FIS experiments.

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Analyze Gluten ANSI-mode test results (run dev/verify-ansi-expressions.sh, parse JSON tracker output, produce root-cause analysis and fix recommendations). Trigger on user requests like "analyze ANSI tests", "run ANSI matrix", "why is this ANSI test failing".

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End-to-end website audit through ICP eyes. Builds synthetic personas (if they don't already exist), runs a structured scorecard review of the client's site, then runs a head-to-head competitive comparison against top competitors. Produces a single consolidated report with persona feedback, competitive positioning, and prioritized recommendations. The complete "how do our buyers actually experience our site vs the competition?" workflow.

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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.

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Build and verify a third-party `.ppmplugin` end to end. This recommended entry point detects Android and iOS source, confirms target platforms, then invokes the manifest, platform build, assemble, and audit stages in order. Advanced users can invoke a stage directly for focused reruns or debugging. Produces a native-only bundle (`manifest.json` plus an Android DEX and/or iOS framework) and stops on any failed stage rather than emitting an unverified partial build. Run after the native module exists.

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Determines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates consequence.

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Use when the user wants a dashboard of all phases or to run more than one phase in parallel. Shows phase status with recommended next actions, runs discuss inline, and dispatches plan and execute as background agents.

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Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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Restructure existing code without changing its behavior, through a test-gated refactoring loop: a named target, a green suite over that target before any edit, a planned sequence of small named refactorings, and the full suite re-run after every step. Use when the user wants to refactor, restructure, clean up, simplify, or improve the design of existing code, or to apply refactoring recommendations from a code-review or architectural-analysis report. This skill changes code; it does not review code (use code-review), assess architecture (use architectural-analysis), plan a multi-module restructure before executing it (use plan-a-change), map domain boundaries before restructuring along them (use ddd-analysis), or build new behavior test-first (use tdd). Do not use it on code inside an active tdd loop; the refactor step of tdd owns that cleanup. Runs its planned sequence to completion without pausing for review; to review each named refactoring as it lands, use pairing.

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Vulnerability audit and dataflow analysis built on the neug CPG engine. file_audit (default, recommended): first builds a function-level summary graph (tier1) over the entire project, then incrementally builds detailed CPGs (tier2) on demand for chosen functions; both tiers coexist in a single neug database (unified schema, cross-layer refines edges; C/Java/Python/Rust). All capabilities are exposed as CLI tool subcommands (audit/overview/build/query/methods/paths), so an agent can drive the audit step by step via shell; the audit subcommand can also run the built-in LLM loop. The same tooling also supports a dataflow analysis mode (Workflow C): no vulnerability hunting, only variable-level dataflow tracing for functions of interest, producing dataflow_analysis.json. repo_audit: multi-round LLM/Cypher audit over a full-repo CPG. Use when the user asks to audit a codebase/project for vulnerabilities, scan a project with CPG/neug, run repo_audit/file_audit, find source-to-sink taint paths, or analyze dataflow inside a function. Supports C, Java, Python, and Rust.

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Challenges a recommendation, design, large change, or any artifact submitted for hard review (spec, proposal, policy, plan) across 5 dimensions (consistency, completeness, actionability, alignment, risk), from the standards of a named senior expert in the artifact's domain and ideally from a fresh context or a different model. Searches assuming defects exist and reports only those that survive evidence. Panel mode runs several independent lenses in parallel on a release candidate, verifies every finding against the current head, and routes confirmed ones to file owners. Use before shipping a significant recommendation, design, large branch, or multi-agent release, or when asked to tear something apart, be brutally honest, or poke holes in it.

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Use when 王建硕 wants to systematically grow his X (Twitter) followers by running numbered, A/B-testable growth experiments and tracking which ones actually work. Every action gets a number, a hypothesis, a target metric, a before-state (for rollback), and a verdict. The North-Star metric is the new-follower ÷ profile-visit ratio (conversion) — every profile change is judged against it. Daily it ingests the X Analytics CSV export, scores each running experiment, and recommends keep / rollback. Triggers — "涨粉", "增加 X 粉丝", "X 涨粉实验", "follower growth", "A/B test my profile", "今天的涨粉检查", "/wjs-x-increasing-follower".

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A C-level strategy advisory board, run as a team of named specialist agents, that takes an executive from a raw strategic question to a defended, board-ready recommendation. Use this skill whenever the user faces a significant business decision or wants strategic analysis — market entry, build-vs-buy, M&A, digital transformation, annual planning, portfolio prioritization, pricing, a big investment, a competitive threat, a turnaround. Trigger on requests like "should we invest in / build / buy / enter…", "develop a strategy for…", "evaluate this opportunity", "make the business case", "stress-test this plan", "run a pre-mortem", or "prepare a board presentation" — in any language, even when the user doesn't say the word "strategy". Also trigger when the user names a board member (Drucker, Porter, Christensen, Graham, Grove, Wack, Taleb, Minto) or asks to convene the board / open a boardroom session.

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Set up Claude Tag for oncall for a team, from that team's standing (public) oncall / monitoring channel. Two things: it sets the channel up for monitoring, so alerts and incident posts here (from a person or another Slack bot) get triaged and acted on automatically, and it finds and sets up the connectors, plugins and skills Claude uses for oncall. A workspace can have several such channels; run once in each, by anyone, and every run adds or updates that team's section in the one oncall memory in shared workspace memory. Use on "set up oncall", "init oncall", "configure Claude for on-call". Surveys the agent connectors an admin configured for Claude, proves one with a read-only pull so access is verified, explores tools, Slack, repos and docs to learn how oncall works here, then recommends what's missing and walks the team through setup. The result goes into the indexed oncall memory every channel reuses; the other skills (incident init, investigation, handoff) pick it up automatically.

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First-run onboarding for the marketing harness. Walks new users through product discovery, generates MARKETING.md, and recommends the first command to run. Use when a user has just installed Kai and types /kai-start, "get started with Kai", "set up Kai", "first time using Kai", or when MARKETING.md doesn't exist yet.

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One command from "plugin installed" to "running brief on your idea." Combines /mycelium:setup (project-state initialization) and /mycelium:interview (10-minute discovery brief) into a single flow. Run this immediately after installing the Mycelium plugin — it's the recommended entry point for first-time users.

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Audits brand/ files for completeness, consistency, and freshness. Use when the agent or user wants to review brand files, audit marketing docs, check brand consistency, verify brand health, or assess what's missing from brand memory. Produces a structured audit report with per-file scores, cross-file contradiction detection, staleness flags, and recommended next skills to run. This is the quality gate for the brand memory system. Also use when someone says 'check my brand', 'brand audit', 'what's missing', 'is my brand ready', 'brand health', 'review my marketing docs', or before running any campaign to verify brand foundations are solid.

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Structured marketing brainstorming when direction is unclear. Use when the agent doesn't know which skill to run, the user is vague about what they need, there are multiple valid marketing paths, or someone says 'I don't know where to start', 'what should we market', 'explore approaches', 'help me think through this', 'marketing ideas', 'what campaign should I run', 'where do I start with marketing', 'what's our marketing plan', or 'I have no idea how to promote this'. Explores 2-3 approaches and recommends the best path forward with a specific next-skill handoff. Even vague frustration like 'nobody knows about my product' or 'how do I get users' should trigger this when no specific channel or tactic is mentioned.

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Provides guidance for troubleshooting Apache Airflow DAGs (failed DAG runs and task instances) in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Use when figuring out reasons for DAG run or task instance failures. Don't use when looking for overall recommendations for Managed Airflow environment performance.

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Install Taskfile (task) and scaffold or audit Taskfile.yml configurations with ecosystem-aware templates. Auto-detects Node.js/pnpm, JVM/Gradle, Python/uv, Docker, and recommends single-file or multi-file (includes) patterns based on project complexity. Use when user runs /devtools:taskfile-setup, mentions "taskfile setup", "setup task runner", "audit taskfile", "scaffold taskfile", "add taskfile", "taskfile.yml", or "developer tasks".

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Answer Engine Optimization — get AI assistants to recommend your brand. Run AEO audits, build Answer Intent Maps, track AI recommendation positions, and maintain a 7-layer AEO infrastructure for any brand or product category.

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Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".

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Use for Vue 3 testing best-practices review and recommendations — designing and auditing Vitest + Vue Test Utils tests for components, composables, Pinia stores, Vue Router, async/Suspense, Teleport, forms, and accessibility, plus Playwright E2E test-design strategy. Advisory/review only — recommends patterns and flags test smells; does not run tests. Complements mk:vue-best-practices (feature code). Invoke for "review my Vue tests", "how should I test this component/composable/store", or "Vue test best practices".

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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