智能体
Agent 规划、多步流程与编排
ux-behavior-design
geekatron
"Fogg Behavior Model B=MAP bottleneck diagnosis sub-skill for the /user-experience parent skill. Diagnoses why users fail to take desired actions by analyzing the three B=MAP factors (Motivation, Ability, Prompt) and identifying which factor falls below the action threshold. Produces bottleneck diagnoses, factor-level assessments, and intervention recommendations with synthesis confidence gates. Invoke when teams need to understand why users are not completing a specific action, diagnose behavioral bottlenecks, design behavior change interventions, or analyze post-launch user inaction patterns. Invoked by ux-orchestrator during Wave 4 lifecycle-stage routing or when user intent is \"Users not completing action\" during the \"After launch\" stage. Triggers: behavior design, B=MAP, Fogg model, behavior bottleneck, motivation analysis, ability analysis, prompt design, why users don't, user inaction, behavior diagnosis, tiny habits, action threshold."
ux-jtbd
geekatron
"Jobs-to-Be-Done research and analysis sub-skill for the /user-experience parent skill. Conducts JTBD job statement synthesis, switch interview analysis (Moesta/Spiek four forces), outcome-driven innovation (Ulwick ODI), and job mapping for tiny teams (1-5 people). Invoked by ux-orchestrator when users need to understand user motivations, map jobs to be done, identify switch triggers, or produce job maps with outcome expectations. Sub-skill of /user-experience; routed via ux-orchestrator lifecycle-stage triage. Triggers: JTBD, jobs to be done, switch interview, job mapping, user motivation, outcome, hiring criteria, user jobs, switch forces."
ux-kano-model
geekatron
"Kano model feature classification and prioritization sub-skill for the /user-experience parent skill. Classifies product features into Must-be (M), Performance (O), Attractive (A), Indifferent (I), and Reverse (R) categories using the functional/dysfunctional questionnaire pair methodology (Kano et al., 1984). Computes Customer Satisfaction (CS) coefficients (Better/Worse) for priority matrix visualization. Produces feature classification reports, priority matrices, and survey design templates. Sample size awareness: 5-8 respondents yields directional classification only (MEDIUM confidence); 20+ respondents required for statistical classification (Berger et al., 1993). Invoked by ux-orchestrator during Wave 4 lifecycle-stage routing or when user intent is \"Need to prioritize features\" at any lifecycle stage. Triggers: Kano, must-be, attractive, one-dimensional, performance feature, satisfaction, feature classification, delighter, feature prioritization, CS coefficient."
ux-design-sprint
geekatron
"AJ&Smart Design Sprint 2.0 facilitation sub-skill for the /user-experience parent skill. Facilitates a structured four-day rapid prototyping and validation process compressed from the Google Ventures five-day Design Sprint (Knapp, Zeratsky & Kowitz, 2016; Courtney, 2019). Produces sprint artifacts including challenge maps, solution sketches, storyboards, realistic prototypes, and structured user interview findings with synthesis confidence gates. Invoke when teams need to rapidly validate a product concept, solve a critical design challenge through structured prototyping, test ideas with real users before committing to development, or explore solution directions when they do not know what to build. Triggers: design sprint, GV sprint, rapid prototyping, sprint week, map sketch decide test, 4-day sprint, design sprint 2.0, AJ Smart sprint, validate prototype, test with users, sprint facilitation."
worktracker
geekatron
Work item tracking and task management using the Jerry Framework hierarchy (Initiative, Epic, Feature, Story, Task, Enabler, Bug, Impediment). Manages WORKTRACKER.md manifests, tracks progress, and enforces template usage for consistent work decomposition.
saucer-boy-framework-voice
geekatron
"INTERNAL SKILL — auto-loaded for framework output voice quality. Reviews, rewrites, and scores framework output text for persona compliance using the Shane McConkey ethos: joy and excellence as multipliers. Governs quality gate messages, error messages, CLI output, hook text, and framework-generated text. Not user-invocable; loaded automatically when framework output needs voice enforcement."
problem-solving
geekatron
Structured problem-solving framework with specialized agents for research, analysis, architecture decisions, validation, synthesis, reviews, investigations, and reporting. Use when tackling complex problems that need systematic exploration, evidence-based decisions, and persistent artifacts.
diataxis
geekatron
"Four-quadrant documentation framework. Produces tutorials (learning by doing), how-to guides (goal-oriented tasks), reference documentation (authoritative description), and explanation (conceptual understanding) using the Diataxis methodology. Invoke when creating new documentation, auditing existing docs for quadrant mixing, or classifying documentation requests. Triggers: documentation, tutorial, how-to, howto, reference docs, explanation, diataxis, write docs, write documentation, write tutorial, create documentation, classify documentation, audit documentation, user guide, getting started, quickstart, API docs, developer guide, quadrant, doc type, how-to guide."
deft-directive-swarm
deftai
Parallel local agent orchestration. Use when running multiple agents on story-level xBRIEFs simultaneously — to scan active/ for allocatable work, set up isolated worktrees, launch agents with proven prompts, monitor progress, handle stalled review cycles, and close out PRs cleanly.
sandbox-agent
rivet-dev
"Deploy, configure, and integrate Sandbox Agent - a universal API for orchestrating AI coding agents (Claude Code, Codex, OpenCode, Amp) in sandboxed environments. Use when setting up sandbox-agent server locally or in cloud sandboxes (E2B, Daytona, Docker), creating and managing agent sessions via SDK or API, streaming agent events and handling human-in-the-loop interactions, building chat UIs for coding agents, or understanding the universal schema for agent responses."
housekeeping
bout3fiddy
Repository housekeeping workflows for AGENTS/CLAUDE architecture, progressive disclosure, and migration of legacy monolithic instruction files.
Armorer Skill
ArmorerLabs
Armorer-Guard is external. Call it through the configured external boundary; do not vendor it into this repository.
pomasa
eXtremeProgramming-cn
Generate declarative multi-agent systems (MAS) using POMASA pattern language. Use when building agent pipelines, orchestrating multiple AI agents, or creating research automation workflows. Supports patterns like Prompt-Defined Agent, Orchestrated Pipeline, Filesystem Data Bus, and Verifiable Data Lineage.
fal-workflow
fal-ai-community
Generate production-ready fal.ai workflow JSON files. Use when user requests "create workflow", "chain models", "multi-step generation", "image to video pipeline", or complex AI generation pipelines.
session-info
colonyops
This skill should be used when the user asks "what's my session ID?", "show my inbox topic", "get session info", "what session am I in?", "my agent ID", or needs to retrieve current hive session details for messaging coordination or debugging.
publish
colonyops
This skill should be used when the user asks to "send message to agent X", "publish to topic", "broadcast to all agents", "notify other sessions", "tell agent Y that...", or needs to send inter-agent messages, hand off work, or broadcast notifications across hive sessions.
letta-development-guide
letta-ai
Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
config
colonyops
This skill should be used when the user asks to "configure hive", "setup hive for my workflow", "customize session spawn", "add tmux integration", "create custom keybindings", "add user commands", or needs guidance on hive configuration, rules, spawn commands, terminal integration, or keybindings.
letta-fleet-management
letta-ai
Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, and folders.
inbox
colonyops
This skill should be used when the user asks to "check my inbox", "read my messages", "any unread messages?", "check for new messages", "see my inbox", or needs to read inter-agent messages from other hive sessions. Provides guidance on reading, filtering, and managing inbox messages.
wait
colonyops
This skill should be used when the user asks to "wait for message from agent X", "block until response", "wait for handoff", "synchronize with other agents", "wait for acknowledgment", or needs to block execution until messages arrive on specific topics with configurable timeout.
research-web
doodledood
'Deep web research with parallel investigators, multi-wave exploration, and structured synthesis. Spawns multiple web-researcher agents to explore different facets of a topic simultaneously, launches additional waves when gaps are identified, then synthesizes findings. Use when asked to research, investigate, compare options, find best practices, or gather comprehensive information from the web.\n\nThoroughness: quick for factual lookups medium for focused topics thorough for comparisons/evaluations (waves continue while critical gaps remain) very-thorough for comprehensive research (waves continue until satisficed). Auto-selects if not specified.'
beads
metalagman
Use this skill to manage work in Beads (bd), a git-backed issue tracker for AI agents, including issue lifecycle, dependencies, sync, and agent hooks.
alibabacloud-data-agent-skill
aliyun
Invoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".