Skills & Agent Building
5,949 results
- Self Improving Agentzhaono1A universal self-improving agent that learns from ALL skill experiences. Uses multi-memory architecture (semantic + episodic + working) to continuously evolve the codebase. Auto-triggers on skill completion/error with hooks-based self-correction.
- Skill Routerzhaono1Routes an agent request to the narrowest matching portable skill when the user asks which skill to use or needs help selecting one across supported hosts.
- Socratic QuestorLưu Trọng HiếuSocratic questioning partner named Gadfly for deep learning through dialogue. Use this skill when the user wants to learn, understand, or explore a topic through questions rather than explanations. Trigger when the user says "teach me about...", "help me understand...", "I want to learn...", "ask me questions about...", "quiz me", "Socratic method", "Gadfly", or wants to deeply understand…
- Workflow Orchestratorzhaono1Coordinates multi-skill workflows and records or runs follow-up actions when the host runtime supports them. Use when completing PRD creation, implementation, or any milestone that should be evaluated for additional skills.
- Fluent CfdcavoiieGuide Ansys Fluent and PyFluent CFD simulation workflows with MCP tool-use discipline. Use when Codex works on Fluent, PyFluent, Fluent TUI, CFD or fluid simulation setup, case/data files, mesh quality, boundary conditions, turbulence models, residual convergence, UDFs, post-processing, solver diagnostics, or validation of Ansys Fluent results.
- Langchain AgentsLangConfigExpert guidance for building LangChain agents with proper tool binding, memory, and configuration. Use when creating agents, configuring models, or setting up tool integrations in LangConfig.
- Linearodysseus0Linear GraphQL patterns for Symphony agents. Use `linear_graphql` for all operations — comments, state transitions, PR attachments, file uploads, and issue creation. Never use schema introspection.
- Mapbox Location GroundingMapboxCompose Mapbox MCP tools to produce grounded, cited location-aware responses from live data instead of training data
- Mapbox MCP Runtime PatternsMapboxIntegration patterns for Mapbox MCP Server in AI applications and agent frameworks. Covers runtime integration with pydantic-ai, mastra, LangChain, and custom agents. Use when building AI-powered applications that need geospatial capabilities.
- MCP BuilderLangConfigComprehensive guide for creating Model Context Protocol (MCP) servers. Use when building MCP servers, integrating external APIs, or creating tool interfaces for LLMs.
- Huashu Nvwaxmg2024女娲造人：输入人名/主题/甚至只是模糊需求，自动深度调研→思维框架提炼→生成可运行的人物Skill。 两种入口：(1)明确人名→直接蒸馏 (2)模糊需求→诊断推荐→再蒸馏。 触发词：「造skill」「蒸馏XX」「女娲」「造人」「XX的思维方式」「做个XX视角」「更新XX的skill」。 模糊需求也触发：「我想提升决策质量」「有没有一种思维方式能帮我...」「我需要一个思维顾问」。
- Skills Auto ActivationwithzombiesUse when skills aren't activating reliably - covers official solutions (better descriptions) and custom hook system for deterministic skill activation
- Benchmark And Mms PlannerHesham SalamaPlan verification and validation campaigns for simulation codes using manufactured solutions, canonical benchmark problems, grid/time refinement, uncertainty propagation, and pass/fail acceptance criteria. Use when an agent needs to prove a solver, model, or result is trustworthy rather than only plausible.
- Blueprint GenerateImbueEnd the Q&A phase and generate the plan. Use after the blueprint skill has gathered enough context.
- Fair Simulation PackagerHesham SalamaCreate FAIR-minded reproducibility bundles for materials simulations by collecting input/output file inventories, hashes, units, engine versions, structure identifiers, provenance, and NOMAD/OPTIMADE/Materials Project friendly metadata. Use before publishing, sharing, archiving, or handing simulation results to another agent.
- HeshamFS/materials-simulation-skillsHesham SalamaCollectionAgent Skills for computational materials science -- numerical stability, solvers, meshing, convergence, and simulation workflows.
- imbue-ai/blueprintImbueCollectionPlanning copilot for coding agents
- Simulation Failure TriageHesham SalamaFirst-response triage for cross-code (LAMMPS/VASP/QE/MOOSE) simulation failures: classifies the failure signature (nonconvergence, NaN/Inf, exploding energy, unstable timestep, pressure blow-up, missing potentials/pseudopotentials, memory exhaustion, process crash/segfault, corrupted output, incomplete runs) and proposes a SAFE one-change-at-a-time RETRY LADDER with explicit STOP CONDITIONS…
- catchupwilbeibiRecovers the conversation and failed tool calls of a previous Codex, Amp, Claude Code, Antigravity, Cline, Copilot CLI, Cursor, DeepSeek Harness, Grok Build, Kimi, OpenCode, Pi Agent, or ZCode session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", asks to recover/summarize a previous session before continuing, or asks to diagnose or…
- GrillΧηλή 🐾 ΚέρβεροςUse when the user says 先討論 / wants to align on a fuzzy idea before building, or before any non-trivial implementation whose requirements are still ambiguous — interviews the user until shared understanding is confirmed, one question at a time, each with a suggested answer; facts answerable from the filesystem/code are looked up, never asked.
- KerberosClaw/kc_ai_skillsΧηλή 🐾 ΚέρβεροςCollectionAI Skills That Actually Do Things — 中文優先的 Claude Code / Codex agent skills 合集 · Reusable bilingual skills for any LLM workflow
- Llm Wiki LintΧηλή 🐾 ΚέρβεροςUse when the user wants to lint a repo following the Karpathy LLM Wiki pattern (raw/ + wiki/ + SCHEMA.md / index.md / log.md). The skill detects path, scans wiki pages + schema layer, reports frontmatter gaps, broken in-body links, source traceability breaks, stale claims, orphan pages, index synopsis contradictions, and SCHEMA-vs-reality drift.
- marswaveai/skillsmarswave.aiCollectionSkills from ListenHub.ai & ColaOS
- Memory LintΧηλή 🐾 ΚέρβεροςUse when the user wants to lint a Claude Code memory directory (~/.claude/memory or custom path) for index inconsistency, broken cross-links, stale project state, duplicate / conflicting feedback rules, naming violations, frontmatter gaps, and oversized files. Phase 1 is a read-only scan and report. Phase 2 applies fixes only for findings the user explicitly picks.