Results for “glossary”

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term, helping users name effects for prompting AI or designers.

This skill should be used when the user asks to "write an SEO article", "create a glossary post", "write a comparison guide", "write X vs Y article", "create educational content for SEO", "write a complete guide to X", or needs to create search-optimized educational content that balances SEO requirements with opinionated, engaging writing.

DeepL API Documentation API skill. Use when working with DeepL API Documentation for translate, document, glossary-language-pairs. Covers 30 endpoints.

Translates text between languages with three modes: quick, normal, and refined. Supports custom glossaries, audience targeting, and style presets.

Police the project's vocabulary — bust vague terms, keep the CONTEXT.md glossary sharp, and lock in decisions worth remembering as ADRs. Use when the user debates naming, says "what should we call this", asks to pin down terminology, wants a decision recorded, or when another skill (hot-seat, whiteboard) surfaces a decision that clears the ADR bar. Just reading the glossary for vocabulary is NOT this skill — trigger only when the words or decisions are being changed.

Use OpenMetadata UI for data discovery, governance, and collaboration. Use when searching for data assets, managing descriptions and ownership, creating glossary terms, setting up data contracts, or tracking data insights and KPIs.

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one. Source: github.com/emilkowalski/skills.

Look up word definitions, pronunciations, parts of speech, and usage examples from the Free Dictionary API.

Stress-tests a plan against the project's domain model and documented decisions, sharpening terminology and updating DOMAIN.md and ADRs inline as decisions crystallize.

Semantic-search personal knowledge (memory, plans, handoffs, skills, Codex rules) via the local RAG index at ~/.claude/rag-index/. Use when a query is fuzzy or cross-file ("how did we fix X", "what did we decide about Y", "which skill handles Z"). Complements grep (exact) and Serena (code symbols). If the user asks a recall question that doesn't map to a specific known file, reach here first.

Look up the precise term for a web animation or motion effect from a vague description, such as 'the bouncy thing when a popover opens' → 'Pop in'.

Perform semantic search, find similar content, and conduct structured research using the Exa API.

Use when a project's durable context needs to be seeded or refreshed — creating or updating docs/product/context.md (overview + digest + source index + glossary + Know/Don't-Know ledger + design context + technical context) in the target repo; when a project has no context layer yet (Stage-0 setup); when domain terminology is still forming or has drifted; after a meeting, new evidence batch, or scope change that outdates the current context. Also use to ingest new docs/product/input/ batches into the digest. Do not use for repo scaffolding or CI setup, for writing the discovery plan, PRD, or epics (use discovery-plan, prd-writer, epics), or for creating or revising Jobs to be Done — this skill records finished JTBDs by id and marks missing ones TBD; jobs-to-be-done owns minting them.

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.

Maintains a project's domain vocabulary and records architecturally-significant decisions as they're made — a CONTEXT.md glossary and gated ADRs, not passive documentation read once. Use when terminology conflicts or turns fuzzy, when making a decision that's hard to reverse/surprising/a real trade-off, or when a stated domain rule doesn't match what the code actually does.

Create a structured H2/H3 outline with BLUF openers and MECE coverage, bound by the research dossier's beat spec. Triggered after /research and /brand-reference.

Tier vocabulary from a text or topic into everyday, academic, and technical categories with teaching priorities. Use when pre-teaching vocabulary or identifying language barriers in a text.

Tell the agent to zoom out and give broader context or a higher-level perspective. Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.

Extract a learned skill from the current conversation

Finds architectural friction in a codebase and proposes deep-module refactors to improve testability and AI-navigability, informed by the domain glossary and ADRs.

Semantic search, similar content discovery, and structured research using Exa API

GLIP: Grounded Language-Image Pre-training

Guides fuzzers with domain-specific tokens to reach deeper code paths in parsers, protocol handlers, and file format processors.

Build and sharpen a project's domain model — a CONTEXT.md glossary and ubiquitous language. Use when pinning down terminology, or the agent "uses the wrong words". Repo decision-memory system (INDEX.md, rejected alternatives) → docs-adr.

Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.

Search local documents, files, notes, and knowledge bases. Index directories, search with BM25/vector/hybrid, get AI answers with citations. Use when user wants to search files, find documents, query notes, look up information in local folders, index a directory, set up document search, build a knowledge base, needs RAG/semantic search, or wants to start a local web UI for their docs.

Distills complex content into concise, structured summaries with key takeaway bullets and a bottom-line conclusion.

Semantic NLP-based code search using neural embeddings and hybrid ranking

Use when symbolic mathematics must cross into NumPy vector evaluation or SciPy numerical algorithms: lambdify contracts, domains, dtypes, parameters, residuals, tolerances, and symbolic-versus-numeric verification. Do not use for work confined entirely to one of those libraries.

Forces a second, harder escalation pass using the original glazer prompt words verbatim, pushing for a sharper replacement thesis rather than more polish.