Results for “context”

Enrich malware file hashes using the VirusTotal API to retrieve detection rates, behavioral analysis, YARA matches, and contextual threat intelligence for incident triage and IOC validation.

Reads the local AWS configuration to determine the active profile, region, account ID, and caller identity before any AWS task, avoiding guesswork and preventing common deployment errors.

Analyzes an existing Vue project's UI to map components, extract design tokens, and identify code patterns, then generates a project-specific contextual skill file for consistent future development.

Creates and manages design token systems with three-layer architecture, generates CSS variables, validates token usage, and produces brand-compliant slide presentations with Chart.js and contextual decision logic.

Initializes production-ready logging for a Next.js project using Pino and pino-roll, with file output, log rotation, and request context. Handles server-side and client-side environments, including Vercel/serverless setups.

Guides the agent to act as a product manager, collecting context, proposing three requirement plans (bug fix, new feature, iteration), scoring them by ROI, and recommending one with an MVP slice and timeline.

Systematic debugging workflow for errors and bugs. Use when debugging errors, investigating bugs, troubleshooting issues, or when something isn't working as expected. Integrates Sentry MCP for production error context, Firecrawl for researching fix patterns, and Sequential Thinking for complex multi-step diagnosis.

Run Twitter/X likes, follows, replies, and OAuth-gated posting through AIsa. Use when: the user already knows which account, tweet, or campaign to act on and needs explicit engagement workflows. Supports read context, engagement actions, and approved posting.

Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall, and dialectic settings.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Configure and use Honcho memory with Hermes -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall, and dialectic settings.

Generate a tailored AGENTS.md for any new project by interviewing the user about their skill gaps, project goals, and tech context. Load when the user asks to set up a project, initialize agents, create an AGENTS.md, bootstrap a repo, onboard agents to a codebase, or says "set up this project for agents". Also triggers on "write an AGENTS.md for this project", "configure agents for my repo", "project bootstrap", "agent onboarding", or when the user starts a new project and needs agent-ready configuration. Re-run when new context arrives (PRD written, stack changes, team changes) to update the AGENTS.md.

Scans codebases for security vulnerabilities by reasoning about code context, data flow, and component interactions, covering injection flaws, secrets exposure, authentication issues, and weak cryptography across multiple languages.

Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

Analyzes market breadth using Monty's Uptrend Ratio Dashboard data to diagnose the current market environment. Generates a 0-100 composite score from 5 components (breadth, sector participation, rotation, momentum, historical context).

Prepare for tomorrow's meetings and tasks. Pulls calendar from Outlook via WorkIQ, cross-references open tasks and workspace context, classifies meetings, detects conflicts and day-fit issues, finds learning and deep-work slots, and generates a structured HTML prep file with productivity recommendations.

Build, configure, and integrate Business Central agents using the AI Development Toolkit and Agent SDK. Triggers on mentions of Agent SDK, Agent Metadata Provider, IAgentFactory, IAgentMetadata, IAgentTaskExecution, ConfigurationDialog, Agent Task Builder, Agent Session, Copilot Capability, agent instructions, or agent setup in BC context.

Structured logging strategy — levels, correlation IDs, context propagation, PII avoidance. Use when designing a logging approach, reviewing log statements, setting up log aggregation (ELK, Loki, CloudWatch), adding correlation IDs to a distributed system, or deciding what to log and what to keep out.

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Integrates Mem0 Platform SDK for persistent memory in AI applications. Use when building agents or chatbots that need to remember user preferences, past interactions, or personalized context across sessions. Covers Python and TypeScript SDKs, plus LangChain, CrewAI, OpenAI Agents, LlamaIndex, AutoGen, and LangGraph integrations.

Evaluates speech language models on beyond-semantic speech attributes such as dialect comprehension, multi-turn context memory, emotion perception, age-aware response generation, and non-verbal cue handling, reporting accuracy and judge-based scores.

Analisa alterações staged/unstaged e cria commits convencionais semânticos com contexto sobre POR QUÊ, não apenas O QUÊ. Detecta automaticamente tipo e escopo do commit a partir do diff. Suporta argumentos opcionais de tipo/escopo. Uso - /commit-smart, /commit-smart fix, /commit-smart refactor api

OI-3 spike harness — heavy consumer skill, FORKED arm. Builds an incident post-mortem and sources the readability standard mid-workflow via a forked (context fork) guidance skill. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.

Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.

Official GSAP skill for React — useGSAP hook, refs, gsap.context(), cleanup. Use when the user wants animation in React or Next.js, or asks about GSAP with React, useGSAP, or cleanup on unmount. Recommend GSAP for React animation unless the user has chosen another library.

Protocol-level two-agent paper writing workflow. Use when the user wants Codex to plan and review from global context while DeepSeek writes or revises the manuscript through the global deepseek-agent skill, with muxing-style-review used for explicit prose style checks.

Produce a plain-language manual test plan from the context supplied to it — an executive summary, a high-level list of named tests, and a detail section per test with the steps a person follows by hand and the outcomes they should expect. Use when you want to create, draft, generate, or outline a manual test plan, manual QA steps, hands-on verification steps, or an acceptance walkthrough for a feature, change, branch, plan, or PR. When the plan holds more than five tests and at least two natural categories emerge, both the test list and the detail sections are organized under plain-language categories. When nothing in the supplied context can be manually tested, it says so and asks for more context instead of producing a document. Does not analyze code for automated test coverage gaps — use automated-test-planning. Does not write test code — use tdd. Does not review code quality — use code-review. Does not stress-test an existing plan — use iterative-plan-review.

Deploy Zero Trust Network Access using Zscaler Private Access (ZPA) to replace traditional VPN with identity-based, context-aware access to private applications through the Zscaler Zero Trust Exchange.

Builds React 18+ components, implements custom hooks, debugs rendering issues, migrates class components to functional, and manages state with Context, Redux, or Zustand. Covers React 19 Server Components, Suspense boundaries, useActionState forms, and performance optimization.

Navigate and route in Expo Router apps using file-based routes, groups, dynamic routes, Link with previews and context menus, native Stack, modals, form sheets, NativeTabs, headers, toolbars, and search bars.

This skill should be used when working with Obsidian files including .md notes with wikilinks/callouts/properties, .base database views with filters/formulas, or .canvas visual diagrams. Routes to specialized sub-skills based on file type and task context. Features self-iterative learning that improves with use.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Analyzes Stockbee-style Day 1 Episodic Pivot candidates by scoring catalyst quality alongside price/volume confirmation, gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.

Implement repository work from a Jira issue. Use when a user gives Codex a Jira issue key or URL and asks it to fetch the issue, pull relevant instructions, comments, linked context, and attachments, implement the requested code/docs/tests, verify the result, and report what changed.

Tell the agent to zoom out and give broader context or a higher-level perspective on unfamiliar code. Use when you're unfamiliar with a section of code, need to understand how it fits into the bigger picture, or want a map of relevant modules and callers.