flpbalada
from GitHub
商业与运营
Design and analyze business models using the Business Model Canvas framework. Use when evaluating startups, planning new products, pivoting existing businesses, or understanding how companies create and capture value. --- # Business Model Canvas - Strategic Business Design Visual framework for developing, documenting, and iterating on business models. Created by Alexander Osterwalder, used worldwide by startups and enterprises. ## When to Use This Skill - Evaluating new product or startup ideas - Analyzing competitor business models - Planning business pivots or expansions - Communicating strategy to stakeholders - Identifying gaps in current business model - Due diligence on investments or partnerships ## The Nine Building Blocks ``` ┌─────────────────┬─────────────────┬─────────────────┬─────────────────┬─────────────────┐ │ │ │ │ │ │ │ KEY PARTNERS │ KEY ACTIVITIES │ │ CUSTOMER │ │ │ │ │ VALUE │ RELATIONSHIPS │ CUSTOMER │ │ Who helps us? │ What do we do? │ PROPOSITIONS │ │ SEGMENTS │ │ │ │ │ How do we │ │ │ ├─────────────────┤ What value │ interact? │ Who do we │ │ │ │ do we deliver? │ │ serve? │ │ │ KEY RESOURCES │ ├─────────────────┤ │ │ │ │ │ │ │ │ │ What do we need?│ │ CHANNELS │ │ │ │ │ │ │ │ │ │ │ │ How do we │ │ │ │ │ │ re
- 📁 .github/
- 📁 agents/
- 📁 commands/
- 📄 .gitignore
- 📄 LICENSE
- 📄 README.md
Multi-agent orchestration framework for Claude Code. Automatically delegates tasks to cheaper, faster sub-agents (Haiku 4.5, Sonnet 4.6) while maintaining Opus-level quality through verification. Use when working on any coding task — Hydra activates automatically to route file exploration, test running, documentation, code writing, debugging, security scanning, and git operations to the optimal agent. Saves ~50% on API costs. --- # 🐉 Hydra — Multi-Headed Speculative Execution > *"Cut off one head, two more shall take its place."* > Except here — every head is doing your work faster and cheaper. ## Why Hydra Exists Autoregressive LLM inference is memory-bandwidth bound — the time per token scales with model size regardless of task difficulty. Speculative decoding solves this at the token level by having a small "draft" model propose tokens that a large "target" model verifies in parallel. Hydra applies the same principle at the **task level**. Most coding tasks don't need the full reasoning power of Opus. File searches, simple edits, test runs, documentation, boilerplate code — these are "easy tokens" that a faster model handles just as well. By routing them to Haiku or Sonnet heads and reserving Opus for genuinely hard problems, we get: - **2–3× faster task completion** (Haiku responds ~10× faster than Opus) - **~50% reduction in API costs** (Haiku 4.5 is 5× cheaper per token than Opus 4.6) - **Zero quality loss** on tasks within each model's capability band ## How Hydra Works — The Multi-Head Loop ``` User Request │ ├──────────────────────────────────────────────────────┐ │ │ ▼ ▼ ┌─────────────────────────────┐ ┌──────────────────────────────┐ │ 🧠 ORCHESTRATOR (Opus) │ │ 🟢 hydra-scout │ │ Classifies task │ │ IMMEDIATE pre-dispatch: │ │ Plans waves │ │ "Find fil
bearlyai
from GitHub
数据与AI
Add or update a model in the harness model registry. Use when the user wants to add a new AI model, update model pricing, or change default models for a harness.
ace-step
from GitHub
数据与AI
- 📁 api/
- 📁 getting-started/
- 📁 guides/
- 📄 SKILL.md
ACE-Step documentation and troubleshooting. Use when users ask about installing ACE-Step, GPU configuration, model download, Gradio UI usage, API integration, or troubleshooting issues like VRAM problems, CUDA errors, or model loading failures.
HackSing
from GitHub
数据与AI
Trait-based intelligent model routing — automatically routes messages to the best AI model
AI-Unified-Process
from GitHub
文档与知识管理
Creates entity model documents with Mermaid.js ER diagrams and attribute tables defining entities, relationships, data types, and validation rules. Use when the user asks to "create an entity model", "design a data model", "draw an ERD", "define database schema", "model entities", or mentions entity-relationship diagram, ER diagram, database design, or data modeling. --- # Entity Model ## Instructions Create or update the entity model at `docs/entity_model.md` based on `docs/requirements.md`. The document contains an ER diagram and attribute tables. ## DO NOT - Add attributes/columns to the Mermaid diagram - Write prose descriptions like "Key attributes: name, email..." - Create a "Relationships" table - Skip the attribute tables ## Document Structure ```markdown # Entity Model ## Entity Relationship Diagram ```mermaid erDiagram ROOM_TYPE ||--o{ ROOM : "categorizes" GUEST ||--o{ RESERVATION : "makes" ``` ### ENTITY_NAME One sentence describing the entity. | Attribute | Description | Data Type | Length/Precision | Validation Rules | |-----------|-------------|-----------|------------------|-----------------------| | id | ... | Long | 19 | Primary Key, Sequence | | ... | ... | ... | ... | ... | ## Required Format for Each Entity
HaoxuanLiTHUAI
from GitHub
数据与AI
Guides ACT-R cognitive model construction: chunk types, production rules, subsymbolic parameters, and model validation
shekohex
from GitHub
工具与效率
Interactive model selection workflow with paginated navigation. Use when users want to select a model interactively - guides them through provider selection then model selection using the question tool with pagination support for large lists.
travisennis
from GitHub
开发与编程
- 📄 add-model.js
- 📄 SKILL.md
REQUIRED when adding OpenCode Zen models - provides a script that fetches accurate context, max tokens, pricing, and capabilities from the API. Use this skill whenever the user asks to add, register, or configure a new OpenCode Zen model. Do NOT guess model specifications—always fetch real data from the API.
arctrany
from GitHub
数据与AI
- 📁 assets/
- 📁 references/
- 📁 scripts/
- 📄 SKILL.md
Use this skill when the user asks to design, implement, audit, or evolve model routing policies across multiple providers/models. Triggers: 'Which model should I use for X?', 'Design a routing policy across many models/providers', 'Work/private/sensitive routing policy', 'Coding should prefer domestic models, but escalate on hard tasks', 'How do we handle future model name changes?', 'Create a router script / policy config / fallback chain', 'Audit current model routing', work/private routing, coding vs research routing, provider safety constraints, fallback chains, slot/alias-based routing, or future-proof model name migration. Includes a runnable router and policy validator.
starsimhub
from GitHub
数据与AI
This skill should be used when the user asks about "disease burden", "DALYs", "population at risk", "transmission routes", "epidemic vs endemic", "compartmental model basics", "SI SIS SIR model selection", "incidence vs prevalence", "WAIFW matrix", or needs foundational guidance on setting up a new infectious disease model from scratch.
