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Found 6,460 Skills
Plan Amazon FBA inventory end to end. Calculates reorder timing and quantity, safety stock, week-by-week days of cover, builds a 12-week PO schedule, applies ASIN-level restock limits and seasonal multipliers, watches the IPI score levers, and surfaces stranded and aged inventory. Use when a user asks about restocking, when to reorder, how much to order, running out of stock, the IPI score, restock limits, ASIN restock limit, days of cover, 90-day forecast, restock schedule, long-term storage fees, aged inventory, or stranded inventory. Trigger phrases. "inventory management", "when to reorder", "restock", "safety stock", "IPI score", "stockout", "aged inventory", "long-term storage", "reorder schedule", "forecast", "days of cover", "ASIN restock limit". Works with zero tools. the user provides sales rate and lead times.
Use when writing a new agent skill for the dogfooded-skills library — covers frontmatter spec, section structure, quality criteria, and antipatterns.
Reads a user's Link financial data — transactions, balances, and wallet sources — so agents can answer questions about spending and available source capabilities. Use when the user says "check my balance", "how much did I spend", "show my transactions", "what accounts are connected", "summarize my spending", "recent purchases", or asks about their financial activity, account balances, or linked sources.
Use this skill when a design or idea requires higher confidence, risk reduction, or formal review. This skill orchestrates a structured, sequential multi-agent design review where each agent has a strict, non-overlapping role. It prevents blind spots, false confidence, and premature convergence.
Create AGENTS.md files for project-specific inline rules. Use when adding small, project-specific instructions that should be committed in repos.
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Converting markdown plans into beads (tasks with dependencies) and polishing them until they're implementation-ready. The bridge between planning and agent swarm execution. Includes exact prompts used.
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection. Use this skill when deciding team size, selecting agent types, or configuring team presets for multi-agent workflows.
Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
Complete guide to using @openserv-labs/client for managing agents, workflows, triggers, and tasks on the OpenServ Platform. Covers provisioning, authentication, x402 payments, ERC-8004 on-chain identity, and the full Platform API. IMPORTANT - Always read the companion skill openserv-agent-sdk alongside this skill, as both packages are required to build any agent. Read reference.md for the full API reference.