Results for “agent-team”

Orchestrating multi-session parallel execution via Claude Code Agent Teams API and Codex CLI Subagents — launch, manage, coordinate concurrent tasks. Use when parallel work is needed.

Go-to-market strategy for AI products, covering positioning, pricing, and enterprise objections.

Diverga v12.0 setup wizard. 4-step researcher profile setup. Captures discipline, experience, tools, database access, and Agent Teams + VS Arena preference. Triggers: setup, configure, 설정, install

Coordinates a 13-agent team for rigorous academic research on any topic, supporting eight modes from quick briefs to systematic reviews with meta-analysis.

Run an AI Startup division meeting with the seven AI Startup agents (no AI Lab agents). Use when the user requests to "start a startup meeting", "convene the startup team", or "run a sprint review for the product".

Orchestrating specialist AI agent teams as a meta-coordinator: decomposes requests into minimum viable chains, spawns each as an independent session, drives to final output. For multi-domain tasks.

You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration.

You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration.

You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration.

Runs an adversarial, decision-aware review of all session work before committing, checking decision adherence and code maintainability, and applies only user-authorized fixes.

Self-hosted open-source collaboration platform — chat, bots, webhooks, plugins, AI integration, Slack/Teams alternative with full data control. Triggers on: 'mattermost', 'self-hosted slack alternative', 'open source teams alternative', 'mattermost bot', 'mattermost webhook', 'mattermost plugin', 'mattermost API', 'mattermost integration', 'AI agent mattermost', 'deploy mattermost', 'mattermost docker', 'mattermost kubernetes', 'enterprise chat self-hosted', 'mattermost AI features', 'mattermost incoming webhook', 'mattermost slash command'.

Control Claude Code via MCP protocol. Trigger with "plan" to write a precise execution plan then feed it to Claude Code. Also supports direct commands, persistent sessions, agent teams, and advanced tool control.

Use DuDuClaw — a self-hosted AI-employee platform — for cross-session memory, team-shared wiki knowledge, task boards, and messaging humans over LINE/Telegram/Discord/Slack. Applies when the user mentions DuDuClaw, asks their agent to remember things durably, or wants to reach people on messaging channels from an agent.

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.

Use when sharing knowledge between agents, companies, or teams to ensure effective transfer of understanding. This skill provides procedures for structured knowledge sharing that ensures the receiver gains actionable understanding, not just information.

Full daily team standup across all 6 verticals: Sales, Social Media, Content, SEO, Creative, and Strategy. Spawns all vertical agents in parallel, collects reports, and presents a unified standup summary. Run at 9 AM daily.

Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.

Use when driving a feature prompt to a production-ready PR through discovery, definition, design, spec, issues, and dev phases, or when the user runs /feature or /feature resume — orchestrates the A-Team agentic pipeline over a target repo.

Use to set concurrency and work-in-progress limits for support teams — chat concurrency, personal WIP on async work, and limits as quality protection rather than arbitrary caps. Trigger for "how many chats per agent", "concurrency settings", "WIP limits", "agents juggling too many tickets", async backlog per person, or quality dropping as concurrent load rises.

Run three agents as a newsroom — a Writer, an Editor, and a Fact-checker — that draft, critique, and verify in parallel and argue until the writing survives with zero flags. This is the level above a single self-review loop, for the pieces that matter most. Best run in Claude Cowork against the user's files. Use for high-stakes writing the user wants bulletproof: a newsletter, a launch post, a client email, a public announcement. Trigger whenever the user says 'run the team', 'use the swarm', 'writer editor fact-checker', 'spawn agents to work on this', or wants the strongest possible version of a piece. For a lighter single-agent loop, use red-pen instead.

Orchestrates a full software development pipeline by sequentially loading and executing seven role-specific skills (BA, TechLead, PM, BE Dev, FE Dev, Tester, QA/QC), with configurable project levels and gate checks.

Compact the current conversation into a handoff document so a fresh agent can pick up the work with zero prior context. Use when the user wants to end a session and continue later, is running low on context, says "write a handoff", "hand this off", "summarize for the next session", "compact this", or wants to transfer work to another agent or teammate.

Use to design support career progression with IC and lead tracks, skill gates instead of tenure alone, and paths that do not treat leaving the phones as the only promotion. Trigger for "career pathing", "progression framework for support", "IC track", "how do agents get promoted", "support ladder", "team lead vs senior agent", or fixing promotion bottlenecks and title inflation.

Automate multi-turn adversarial conversations against LLM agents using Microsoft PyRIT, including Crescendo and Tree-of-Attacks-with-Pruning (TAP) attack chains with scorer feedback loops.

Coordinate agents via the AMQ CLI for file-based inter-agent messaging. Use this skill whenever you need to send messages to another agent (codex, claude, or any named handle), check your inbox, drain queued messages, set up co-op mode between agents, join a swarm team, route messages across projects, or diagnose delivery issues. Also use it when you receive a message and need to know how to reply, inspect receipts, or handle priority. Covers any multi-agent coordination task where agents need to talk to each other — review requests, questions, status updates, decision threads, wake notifications, and orchestrator integration (Symphony, Kanban). For collaborative spec/design workflows specifically, prefer the /amq-spec skill which provides structured phase-by-phase guidance. Not intended for distributed systems design (RabbitMQ, Kafka), CI/CD pipelines, or single-agent tasks with no partner.

Simulator.Company digital-twin & actor-agent specialist — talk to an agent AS an agent and delegate work to it. An agent is ANY actor whose `description` holds an "# Agent" competency profile (what it does, what it knows, whether it fits a task). The common case is a person: every workspace user has a 1:1 twin actor (`systemObjType="user"`) carrying that profile. But any actor can be an agent — a service/bot twin, a team or department, an organization, a process. This skill discovers the agent (`findAgent`), loads its profile (`getAgent`), adopts it as the persona, then either does the task, finds a better-suited agent, or hands the decision to the user (for a person: a task or a p2p message; for a non-person: propose another executor or run/trigger it as an actor). It is the actor-analog of `simulator-skills` (the `Skills`-form registry), but the registry is the workspace's agent actors. Use when the user wants to "delegate", "assign this to <someone/something>", "can <X> do this", "who/what should do this",

Use to define handoff standards for support transfers and to measure whether context survives the handoff — cold vs warm, required fields, and re-ask rate after transfer. Trigger for "transfer protocol", "warm handoff", "customers repeat themselves after transfer", "ping-pong between teams", "what should agents include when transferring", handoff quality, or re-ask rate after escalation.

Build fully customizable, agent-ready design systems with Astryx — Meta's production design system now open source. Ships 150+ React components built on StyleX with zero styling lock-in, component swizzling, brand theming, dark mode, and CLI tooling. Use when building component libraries, design systems, UI applications, design tokens, or when teams need consistent accessible components that AI agents can understand and extend. Triggers on: astryx, design system, component library, design tokens, react components, accessible components, astryx design, stylesheets, theme customization, component composition.

Use to find where support can drive adoption, referral and retention without turning into a sales channel, and to measure whether it worked. Trigger for "can support drive growth", "support-led growth programme", "should agents mention other features", advocacy and referral from support, adoption nudges in support conversations, or a support team being given a revenue target.

Use to analyse average handle time, resolution time or time-in-queue without being misled by the skew, and to find where time actually goes. Trigger for "why is our AHT increasing", "our call handling times are up", "which contact reasons take longest", "how long do tickets take", handle time by agent or team, or an AHT target being set.

Turn a piece of analysis into the thing that ships it. Takes the output of a read (an account verdict, a usage story, a ranked list) and drafts the CRM update, the Slack post, the follow-up email, and the next touch, draft-first and fully logged. Built for any GTM team, customizable to your CRM, your channel tool, and your outreach stack. Trigger on "draft the CRM update for this", "post this to a channel", "write a follow-up to this person", "update the qualification fields on this account", "send this recommendation to a teammate", "draft an outreach sequence", or any write, send, or notify step at the end of a workflow.

Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, acceptance-criteria testability review for agentic Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.

Build and maintain a structured personal-context portfolio for the project owner — identity, working style, goals, team, decision patterns. Includes both an interview protocol for bootstrapping and observable-signal patterns for incremental refinement. Use to bootstrap an owner profile (interview), to refine an existing profile (target one file), or to incrementally update the profile based on observed patterns from a normal session (the agent watches for signals and proposes additions when evidence accrues).

Never show the user a first draft. Run every writing task through a self-critique loop — draft, attack the draft as the harshest reviewer in the room, rewrite, and repeat until a full review pass finds zero flags — then return only the final plus a change log. Use for any writing the user actually cares about: emails, LinkedIn posts, newsletters, docs, announcements, client messages. Trigger whenever the user says 'run the loop', 'self-critique this', 'make it bulletproof', 'don't give me a first draft', 'be brutal', or hands over a task where quality matters more than speed. This is a single-agent loop; for the three-agent version use the-team.

Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.

AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for