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Found 873 Skills
Define ideal customer profiles and buyer personas with structured frameworks. Use when the user asks about ideal customer profile, ICP, buyer persona, target audience definition, customer segmentation, customer research, user interviews, or persona creation. Trigger phrases include "ideal customer profile", "ICP", "buyer persona", "who is my customer", "target audience", "customer profile", "persona", "customer segmentation", "user research", "customer interview", "who should I target".
Provides domain knowledge and guidance for Flare FAssets—wrapped tokens (FXRP, FBTC, etc.), minting, redemption, agents, collateral, and smart contract integration. Use when working with FAssets, FXRP, FBTC, FAssets minting or redemption, Flare DeFi, agent/collateral flows, or Flare Developer Hub FAssets APIs and contracts.
Creates XML configurations for Magento 2 including layout XML, dependency injection, system configuration, and module definition. Use when working with XML configuration files, layout XML, di.xml, or system.xml. Masters XML schema design, validation, and configuration management.
Initialize project with Conductor artifacts (product definition, tech stack, workflow, style guides)
Interactive guide for creating new skills (or updating existing skills) that extend Claude's capabilities. Walks the user through use case definition, frontmatter generation, instruction writing, and validation. Use when users want to create a new skill, build a skill, update an existing skill, or ask "help me make a skill for X". Always clarifies requirements before generating.
DCE-safe require() patterns and edge runtime constraints. Use when writing conditional require() calls, guarding Node-only imports (node:stream etc.), or editing define-env-plugin.ts / app-render / stream-utils for edge builds. Covers if/else branching for webpack DCE, TypeScript definite assignment, the NEXT_RUNTIME vs real feature flag distinction, and forcing flags false for edge in define-env.ts.
How to add or modify Next.js experimental feature flags end-to-end. Use when editing config-shared.ts, config-schema.ts, define-env-plugin.ts, next-server.ts, export/worker.ts, or module.compiled.js. Covers type declaration, zod schema, build-time injection, runtime env plumbing, and the decision between runtime env-var branching vs separate bundle variants.
Build MCP servers in Python with FastMCP. Workflow: define tools and resources, build server, test locally, deploy to FastMCP Cloud or Docker. Use when creating MCP servers, exposing tools/resources/prompts to LLMs, building Claude integrations, or troubleshooting FastMCP module-level server, storage, lifespan, middleware, OAuth, or deployment errors.
Psychological profiling through natural conversation using narrative identity research (McAdams), self-defining memory elicitation (Singer), and Motivational Interviewing (OARS framework). Use when you need to: (1) understand someone's core values and motivations, (2) discover formative memories and life-defining experiences, (3) detect emotional schemas and belief patterns, (4) build psychological profiles through gradual disclosure, (5) conduct user interviews that reveal deep insights, (6) design conversational flows for personal discovery, (7) identify identity themes like redemption and contamination narratives, (8) elicit authentic self-disclosure without interrogation.
Guide for implementing parsers with error recovery for new languages in Biome. Use when creating parsers for JavaScript, CSS, JSON, HTML, GraphQL, or adding new language support. Examples:<example>User needs to add parsing support for a new language</example><example>User wants to implement error recovery in parser</example><example>User is writing grammar definitions in .ungram format</example>
Define, validate, and run lane-style multi-step automation sequences using `asc workflow` and a repo-local `.asc/workflow.json`. Use when migrating from lane-based automation, building enterprise CI flows, or orchestrating multi-command `asc` runs.
Build a structured taxonomy of failure modes from open-coded trace annotations. Use this skill whenever the user has freeform annotations from reviewing LLM traces and wants to cluster them into a coherent, non-overlapping set of binary failure categories (axial coding). Also use when the user mentions "failure modes", "error taxonomy", "axial coding", "cluster annotations", "categorize errors", "failure analysis", or wants to go from raw observation notes to structured evaluation criteria. This skill covers the full pipeline: grouping open codes, defining failure modes, re-labeling traces, and quantifying error rates.