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Found 12,029 Skills
Salesforce Flex Credit estimation for Agentforce and Data Cloud workloads. TRIGGER when: user needs cost projections, scenario planning, budget sizing, or architecture tradeoff analysis for Agentforce prompts/actions, Data Cloud meters, or monthly Flex Credit usage. DO NOT TRIGGER when: user is building Agentforce metadata or .agent files themselves (use sf-ai-agentforce or sf-ai-agentscript), implementing Data Cloud assets (use sf-datacloud-*), or asking for contract-specific commercial approval that depends on non-public pricing terms.
Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for", "read the source of", "how does X work internally", "get the implementation of", "opensrc path", or any task requiring access to dependency source code beyond types and docs.
Integrate Polpo AI agents into any TypeScript/JavaScript application using @polpo-ai/sdk. Use when the user wants to add AI agent chat, completions API, streaming SSE, session management, memory, webhooks, or any Polpo API integration into their code. Triggers on "polpo", "agent chat", "completions API", "polpo sdk", "@polpo-ai/sdk", "AI agent integration".
Standard Gear/Vara Sails builder pack for AI agents. Use when building or extending a Sails app on Vara or Gear. NOT for Vara.eth, ethexe, non-Sails programs, or generic protocol research.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, adaptive memory, dynamic memory, or memory benchmarks (LoCoMo, LongMemEval). A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of durable agent knowledge and cross-session persistence.
The foundational context engineering skill — start here when exploring the discipline. This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Also activates when the user mentions "context engineering" or "context-engineering" for foundational understanding of AI agent context systems.
Comprehensive React/TypeScript frontend code review with optional parallel agents
Comprehensive Elixir/Phoenix code review with optional parallel agents
Artifact status + multi-phase orchestration. Scan what exists, check freshness, compose and track complex workflows across sessions. Not for skill routing (the agent does that proactively).
Mechanize Pattern 15 — the seven-pass adversarial review protocol for academic manuscripts. Spawns 7 forked subagents in parallel (abstract, intro, methods, results, robustness, prose, citations), then synthesizes a prioritized revision checklist. Use for submission-ready or R&R-stage papers where single-pass review isn't enough.
Update repo documentation and agent-facing guidance such as AGENTS.md, README.md, docs/, specs, plans, and runbooks. Use when code, skill, or infrastructure changes risk doc drift or when documentation needs cleanup or restructuring. Do not use for code review, runtime verification, or `agent-readiness` setup.
Extract full context of the last task from the most recent parent session shown in the session lineage. Strategically uses sub-agents to avoid bloating your own context.