Claude Code Skills · page 126

Use Business Adventures for "why did this fail?", "analyze this crisis", "what pattern applies?", or "what would Brooks notice?

Apply John Bogle stewardship capitalism logic to separate investing from

Apply John Bogle index investing rules for low-cost funds, asset allocation,

Developer implementation guide for adding compact memory to an Agent — covers fork agent pattern for compaction, trigger strategy, summary format design, and memory restoration in subsequent sessions. Use when a developer asks how to implement compact memory, context compression, or memory persistence in their agent built with Claude Agent SDK or Anthropic API.

Apply Jonah Berger''s STEPPS framework. Trigger on: "why is this not spreading?", "make this campaign contagious", "diagnose viral content".

Apply China contract drafting review with San Guan Si Bu Fa. Trigger on contract review, drafting, clauses, or deal structure.

Apply Brittany Hodak's SUPER Model for customer loyalty, referrals, word of mouth, personalization, service recovery, and scalable customer experience.

Analyze Wall Street conflicts. Trigger on: "can I trust this advisor?", "is this forecast reliable?", "whose interest does this serve?".

Apply Keynes policy reasoning. Trigger on: "will austerity work?", "inflation or deflation?", "analyze this economic crisis".

Analyzes Chinese public company financial statements (balance sheet, income statement, cash flow) to assess asset quality, profit authenticity, cash flow health, solvency, and overall investment value. Use when the user provides financial data or a company name and asks whether financials are healthy, whether profits are real, whether there are red flags, or whether the company is a good investment. Based on the book "一本书读懂财报" by Xiao Xing (Tsinghua University).

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Use Graham value investing for Is this investment or speculation, defensive

Apply Pulak Prasad evolutionary investing rules for avoiding big losses,

Apply Gary Vaynerchuk social content rules for native jabs, right hooks,

Build LangChain (Python) tools using Claude Code's fail-closed design pattern — unified name/schema/security/execution in one class, with automatic three-layer execution (validate → permission → call). Use this skill whenever the user wants to define a new LangChain tool, add permission or validation logic to an existing tool, set up the ClaudeStyleTool base class in a project, or asks about "build_tool", "Claude Code style tool", "工具定义", or "langchain tool with permissions". Also trigger when the user says "create a tool for X" or "定义一个工具" in a LangChain Python project context, even without mentioning Claude Code explicitly.

Apply Howard Marks investing judgment for second-level thinking, price

Apply Charlie Munger mental models to investments, decisions, and bias diagnosis. Trigger on Munger checklist or spot biases.

Apply The Outsiders CEO framework to assess capital allocation, per-share

Apply Ryan Holiday''s perennial seller framework. Trigger on: "will this work last?", "position my book/product", "build an enduring launch".

Implement a production-ready LLM query loop / agent loop for AI applications. Use this skill whenever the user wants to add tool calling, ReAct-style reasoning-action-observation cycles, function calling loops, query engines, agent runtimes, tool_result feedback, max-turn exits, or Claude Code-like Agent Loop behavior to their own product or codebase.

Audit SEO metadata such as title, description, headings, alt, robots,

End-of-session memory distillation — extracts key decisions, eliminated approaches, new discoveries, and current blockers from the current conversation and writes them to MEMORY.md topic files. Based on Claude Code's autoDream background consolidation service. Activate when the user says "dream", "/dream", "save session memories", "distill this session", "what should I remember from this session", or when a long productive session is ending and the user wants to preserve what was learned.

Apply Austin Kleon''s sharing framework. Trigger on: "what should I share?", "build an audience", "document my creative process".

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Apply Austin Kleon creative practice for idea theft, remixing, starting

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Use StoryBrand for "clarify my message", "rewrite my homepage", "create a BrandScript", "what is my one-liner", or "fix my CTA".

Apply Seth Godin''s marketing framework. Trigger on: "find my smallest viable audience", "position this offer", "earn trust".

Design and implement a layered, configurable permission/safety system for agent tools. Use this skill when building an agent that needs to control which tool calls are auto-allowed, which require user confirmation, and which are denied — especially when the system must be configurable across multiple scopes (project/user/enterprise) and extensible via hooks. Triggers on: "权限系统", "工具安全", "tool permission", "permission system", "tool safety", "allow/deny rules", "hook system", "构建安全机制".

Apply Steven Pressfield resistance diagnosis for procrastination, turning

Define agent tools using the fail-closed design pattern — unified name/schema/security/execution in one class, with three-layer execution (validate → permission → call). Use this skill whenever the user wants to define a new agent tool, add permission or validation logic to an existing tool, or asks about 'build a tool', '定义一个工具', 'create a tool for X', '工具定义'. Framework-agnostic: works with hermes-agent, LangChain, or any Python agent framework.

Manages md2wx CLI for converting Markdown to WeChat Official Account drafts. Use when working with WeChat article publishing, Markdown to HTML conversion, or WeChat media upload.

Скилл для очеловечивания русскоязычного текста. Убирает признаки AI-генерации, делает текст живым. Используй ВСЕГДА, когда пользователь просит: очеловечить текст, убрать следы нейросети, сделать текст живым/естественным, переписать как человек, humanize на русском, убрать канцелярит, убрать водянистость, сделать текст менее формальным. Также используй если пользователь вставляет русскоязычный текст и говорит что-то вроде 'перепиши', 'сделай лучше', 'звучит как робот', 'слишком искусственно'. Работает ТОЛЬКО с русским языком. Для английского используй оригинальный humanizer. НЕ используй для: перевод, написание с нуля, грамматика, код.

Use this skill when you need to QA audit and fix a plugin skill file. Provides a methodology for verifying skill content against official documentation, fixing issues in-place, and producing verification reports.

This skill should be used when the user asks to "choose a DSPy adapter", "use JSONAdapter", "use XMLAdapter", "enable native function calling", "send images, audio, or files to DSPy", mentions `dspy.ChatAdapter`, `dspy.JSONAdapter`, `dspy.XMLAdapter`, `dspy.Image`, `dspy.Audio`, `dspy.File`, structured outputs, or multimodal DSPy signatures.

This skill should be used when the user asks to "compose DSPy modules", "use Ensemble optimizer", "combine multiple programs", "use dspy.MultiChainComparison", mentions "ensemble voting", "module composition", "sequential pipelines", or needs to build complex multi-module DSPy programs with ensemble patterns or multi-chain comparison.

This skill should be used when the user asks to "use BetterTogether", "combine prompt optimization and fine-tuning", "sequence DSPy optimizers", "run prompt then weight optimization", mentions `dspy.BetterTogether`, strategy strings such as "p -> w -> p", or needs to compose multiple DSPy teleprompters into an evaluated optimization sequence.

This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.

This skill should be used when the user asks to "create custom DSPy module", "design a DSPy module", "extend dspy.Module", "build reusable DSPy component", mentions "custom module patterns", "module serialization", "stateful modules", "module testing", or needs to design production-quality custom DSPy modules with proper architecture, state management, and testing.

This skill should be used when the user asks to "debug DSPy programs", "trace LLM calls", "monitor production DSPy", "use MLflow with DSPy", mentions "inspect_history", "custom callbacks", "observability", "production monitoring", "cost tracking", or needs to debug, trace, and monitor DSPy applications in development and production.

This skill should be used when the user asks to "build local DSPy retrieval", "use dspy.Embedder", "use dspy.Embeddings", "save an embeddings index", "add FAISS retrieval", mentions semantic search, hosted embeddings, local embedding models, `EmbeddingsWithScores`, or needs a DSPy retriever over an application-owned text corpus.

This skill should be used when the user asks to "evaluate a DSPy program", "test my DSPy module", "measure performance", "create evaluation metrics", "use answer_exact_match or SemanticF1", mentions "Evaluate class", "comparing programs", "establishing baselines", or needs to systematically test and measure DSPy program quality with custom or built-in metrics.

This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.

This skill should be used when the user asks to "optimize an agent with GEPA", "use reflective optimization", "optimize ReAct agents", "provide feedback metrics", mentions "GEPA optimizer", "LLM reflection", "execution trajectories", "agentic systems optimization", or needs to optimize complex multi-step agents using textual feedback on execution traces.

This skill should be used when the user asks to "integrate DSPy with Haystack", "optimize Haystack prompts using DSPy", "use DSPy to improve Haystack pipeline", mentions "Haystack pipeline optimization", "combining DSPy and Haystack", "extract DSPy prompt for Haystack", or wants to use DSPy's optimization capabilities to automatically improve prompts in existing Haystack pipelines.

This skill should be used when the user asks to "use MCP tools with DSPy", "connect an MCP server to ReAct", "convert MCP tools to DSPy tools", mentions Model Context Protocol, `dspy.Tool.from_mcp_tool`, streamable HTTP MCP transport, stdio MCP servers, or needs to expose MCP-compatible tools to a DSPy agent.

This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).

Universal text artifact optimizer using GEPA's optimize_anything API for code, prompts, agent architectures, configs, and more

This skill should be used when the user asks to "choose a DSPy optimizer", "compare DSPy optimizers", "which teleprompter should I use", "optimize prompts or weights", mentions LabeledFewShot, BootstrapFewShotWithRandomSearch, KNNFewShot, COPRO, MIPROv2, SIMBA, GEPA, BootstrapFinetune, Ensemble, or BetterTogether, or needs a cost-aware DSPy optimization plan.

This skill should be used when the user asks to "refine DSPy outputs", "enforce constraints", "use dspy.Refine", "select best output", "use dspy.BestOfN", mentions "output validation", "constraint checking", "multi-attempt generation", "reward function", or needs to improve output quality through iterative refinement or best-of-N selection with custom constraints.

This skill should be used when the user asks to "deploy DSPy", "save and load a DSPy program", "configure DSPy cache", "harden pickle cache", "track DSPy token usage", "run DSPy asynchronously", "stream DSPy output", mentions `configure_cache`, `restrict_pickle`, `track_usage`, `acall`, `asyncify`, `streamify`, `StreamListener`, MLflow deployment, or needs production runtime guidance for a DSPy application.

This skill should be used when the user asks to "build a RAG pipeline", "create retrieval augmented generation", "use ColBERTv2 in DSPy", "set up a retriever in DSPy", mentions "RAG with DSPy", "context retrieval", "multi-hop RAG", or needs to build a DSPy system that retrieves external knowledge to answer questions with grounded, factual responses.

This skill should be used when the user asks to "create a ReAct agent", "build an agent with tools", "implement tool-calling agent", "use dspy.ReAct", mentions "agent with tools", "reasoning and acting", "multi-step agent", "agent optimization with GEPA", or needs to build production agents that use tools to solve complex tasks.

This skill should be used when the user asks to "use DSPy RLM", "process a very long context", "use ProgramOfThought", "use CodeAct", "run DSPy modules in parallel", mentions Recursive Language Models, sandboxed Python execution, Deno, `dspy.RLM`, `dspy.ProgramOfThought`, `dspy.CodeAct`, or `dspy.Parallel`, or needs to choose a DSPy reasoning module beyond Predict, ChainOfThought, and ReAct.

This skill should be used when the user asks to "create a DSPy signature", "define inputs and outputs", "design a signature", "use InputField or OutputField", "add type hints to DSPy", mentions "signature class", "type-safe DSPy", "Pydantic models in DSPy", or needs to define what a DSPy module should do with structured inputs and outputs.

This skill should be used when the user asks to "optimize with SIMBA", "use mini-batch introspective optimization", "generate self-reflective rules", mentions "SIMBA optimizer", "stochastic mini-batch ascent", "output variability", or needs an alternative to MIPROv2/GEPA that evolves rules and demonstrations from numeric metrics.

Before coding, determine what to ask. Max 2 yes/no questions. Never ask technical questions.

Silent quality check after every feature. Fix issues before telling user. Never claim tests passed without running them.

When multiple solutions exist, pick the best one. Explain why in one sentence.

Build one feature at a time. Complete each fully before moving to next. Auto-triggers quality check.

Create project skeleton. Pick stack, create files, install dependencies. AI decides everything.

Build user-facing interface. Clean, functional, mobile-friendly by default.

Celebrate real milestones only. One line, one emoji. Credit belongs to user, not AI.

Classify request as small/medium/large. Adjust workflow depth accordingly.

I-Lang compression engine. All internal planning uses I-Lang v4.0 syntax. Save 60%+ tokens. User never sees compressed output.

Explain all costs in human terms. Always compare with real-world equivalents. Recommend cheapest that works.

End of session summary. What got done, what got fixed, what comes next, progress delta.

Translate technical decisions into human language. Explain in cost, speed, stability.

Deploy to Cloudflare Workers. Free tier handles 100k requests/day. Global edge network.

Choose deployment target based on project type. Static sites to CF Pages, APIs to VPS, serverless to Workers.

Deploy to VPS. Code is already on the server. Start the service, configure nginx, verify accessible.

Help user buy a domain, configure DNS, set up SSL. Guide every click.

At milestones, compare achievement vs human programmer time and cost. Keep it realistic.

Help complete beginners set up their development environment. Detect Mac or other. Guide VPS purchase and SSH setup step by step.

Transfer files between local and server. Guide user through SCP or upload methods.

Auto-fix bugs. Observe symptom, find root cause, apply minimal fix, verify, explain in human terms.

After fixing a bug, explain what went wrong in language the user understands. No jargon for beginners.

Guide user through errors they see. Translate error messages to human language.

Step 1 of debugging: observe the symptom carefully before jumping to conclusions.

Step 2 of debugging: reason about root cause based on observed symptoms.

Step 3 of debugging: apply minimal fix. One-liner ideal. Verify nothing else broke.

Full project review from beginning. Check every file. Plain language report.

Final go-live checklist. Is it accessible? SSL working? Mobile friendly? Show user their live URL.

Record mistakes. Check before similar builds. Avoid repeating silently.

Detect recurring patterns in user's project. Apply automatically next time.

Learn user's preferences over time. Code style, naming, structure. Save to global prefs.

Persistent memory across sessions. Save project state and user preferences. Never save secrets.

Track project milestones. Auto-detect when a significant checkpoint is reached.

Help user work from multiple devices. Sync project via git.

Optimize for speed and cost. Pick lightweight solutions. Flag expensive operations.

Break complex tasks into 5-15 ordered steps with time estimates. Dependency order first.

Give realistic time and cost estimates for each step. Explain in human terms.

Decide what to build first. Core function before polish. Revenue before aesthetics.

Identify risks before building. Flag third-party dependencies, API limits, and cost traps.

Report progress after each feature. Percentage, what just completed, what comes next.

Create a visual roadmap for large projects. Phases, milestones, timeline.

Lock confirmed requirements. Don't change them without user approval.

Run and test directly on the server. No local dev environment needed. What you build is what goes live.