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Found 540 Skills
Apply Upper Echelons Theory (Hambrick and Mason, 1984) to analyze how top management team characteristics — demographics, experiences, values — shape strategic choices and organizational outcomes. Use this skill when the user needs to evaluate TMT composition effects on strategy, predict strategic direction from leadership profiles, assess whether managerial discretion enables or constrains executive influence, or when they ask 'does leadership background matter for strategy', 'how does TMT composition affect decisions', or 'why did this management team make that choice'.
Educational map of risk exposure screening—typical risk indicator taxonomies, exposure value and percentage, address-level vs transaction-level engines, and common template families (entity label, multi-hop interaction, blacklist). Use when the user asks how commercial screening tools reason about labeled addresses, tainted flows, or deposit vs withdrawal checks—not for legal sanctions determinations or substituting a vendor’s live rules.
Production server monitoring stack covering Prometheus, Node Exporter, Grafana, Alertmanager, Loki, and Promtail on bare-metal or VM Linux hosts. USE WHEN: - Setting up monitoring for a new production server or VPS - Configuring Prometheus scrape targets for application or system metrics - Creating Grafana dashboards and datasource provisioning - Writing Alertmanager routing rules with email/Slack notifications - Implementing the PLG stack (Promtail + Loki + Grafana) for log aggregation - Performing live system diagnostics with htop, iotop, nethogs, ss, vmstat, iostat - Setting up uptime monitoring with UptimeRobot or healthchecks.io DO NOT USE FOR: - Kubernetes-native observability (use the kubernetes skill instead) - Application-level APM (distributed tracing with Jaeger/Tempo — use observability skill) - Cloud-managed monitoring (CloudWatch, GCP Monitoring, Azure Monitor) - Windows Server monitoring
Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
Draft or update requirement documents under `codestable/requirements/` for the project — use **user stories + plain language** to describe a capability's "reason for existence, solution approach, and boundaries", so non-technical readers can quickly understand the highlights of the system. Layered with architecture: requirement is the "problem space" (why this capability is needed), while architecture is the "solution space" (what structure is used to implement it). Two modes: new (draft a new requirement doc from scratch), update (refresh an existing doc based on new materials or implementation changes). Single-target rule — only modify one document at a time. Trigger scenarios: the user says "fill in a requirement doc", "write down the requirements for this capability", "update the requirements directory", or during the feature-design phase, it is found that there is no corresponding requirement for the capability to be implemented this time.
Builds robust, tool-specific prompts from user intent using a structured extraction and routing engine. Use when the user asks for prompt creation, prompt repair, prompt decomposition, or adapting prompts across Claude, GPT, reasoning models, Gemini, coding IDEs, autonomous agents, and image tools.
Discovers and inspects BigQuery Data Transfer Service (DTS) configurations. Use this to identify existing ingestion pipelines and extract datasource or transfer config metadata for data pipelines. Use when a user asks for ingestion scenarios while building or managing data pipelines or when a user asks to "ingest" or "add" data that may already be managed by a DTS transfer.
Use this skill when a user provides a torrent name or file name and wants to fix recognition issues, or asks to add/manage custom identifiers (自定义识别词). This skill generates identifier rules based on the WordsMatcher preprocessing logic, checks for duplicates against existing rules, and saves them via MCP tools. Because custom identifiers are global, generated rules must default to conservative, sample-specific regex patterns instead of broad matches unless the user explicitly wants global cleanup. Applicable scenarios include: 1) A torrent or file name is incorrectly recognized (wrong title, season, episode, etc.); 2) The user wants to block unwanted keywords from torrent names; 3) The user needs episode offset rules for series with non-standard numbering; 4) The user wants to force recognition of a specific media by TMDB/Douban ID.
Use when extracting requirements from Azure DevOps work items using dxs devops commands: fetching work items, reviewing relations, downloading attachments, compiling raw requirements. This is a utility skill — it extracts and structures work item content but does not build reports, datasources, or other artifacts.
Use when building NEW Datex Studio reports from scratch. This is the entry point for all new report work — it orchestrates requirements gathering, schema exploration, datasource creation, layout prototyping, and deployment as a single workflow. Trigger for: "create a report", "build a report", "report from work item", "report from requirements". For modifying EXISTING reports, use `report-editor`.
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
Generate editorial calendars for blogs with topic clusters, publishing schedules, content decay detection, freshness update plans, seasonal opportunities, content mix formula, template integration, and distribution scheduling. Plans monthly or quarterly calendars optimized for SEO topic authority and AI citation freshness requirements (30-day update cycles). Use when user says "editorial calendar", "content calendar", "blog calendar", "publishing schedule", "blog plan", "content plan", "what should I write".