「context」の検索結果

Broad, pre-curation evidence reconnaissance for a declared Gaia skill, intake batch, upstream repository, or proposed generic capability. Run before choosing a discovery strategy or mapping topology, to give the curator independent research leads and counterevidence. Context only: never scores, grades, ratifies, ingests, or mutates the registry. Later ev-discovery and ev-pipeline phases validate/promote any selected leads.

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Use when designing or changing a component's public API, its composition, or its state model in libs/ui-react or libs/ui-rnative — layering (core vs internal vs primitives), BaseProps/Props splits, converting a component to compound / changing its composition with createSafeContext, controlled/uncontrolled state, prop-naming conventions, and cross-platform API parity. Load this before shaping a new component, changing its props, or refactoring its composition.

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This repo is indexed by graft/. For ANY task here, whether understanding how something works, finding where code lives, tracing what calls a symbol or what a change breaks, or scoping an edit, get your context from graft before grepping or reading source files.

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Skippable Phase 0 of the Gaia evidence verification pipeline. Use only on declared need for higher-quality Stage-2 evidence: a promotion candidate, or a skill that gaia-meta-sweep flagged as under-evidenced. Given a skill id, source repo/context, and needed evidence types (benchmark-result, arxiv, peer-review, social-signal), it runs Firecrawl searches, scrapes top hits to markdown, and appends discovered sources into Phase 1 inputs. Missing benchmark evidence is recorded as benchmark-source candidates first, not direct scoring rows. Trigger phrases: "discover evidence", "find new evidence", "Phase 0", "run ev-discovery", "search for benchmarks/papers", "under-evidenced", "promotion candidate needs stronger proof". This is the ONLY ev-* skill that searches the web for NEW evidence; other ev-* skills aggregate or verify known sources. Requires Firecrawl to be usable (`firecrawl --status` authenticated OR FIRECRAWL_API_KEY set); skips gracefully when neither holds.

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Firecrawl gives AI agents and apps fast, reliable web context with strong search, scraping, and interaction tools. One install command sets up three skill segments: live CLI tools, app-integration build skills, and outcome-focused workflow skills. Route the reader to the right usage path after install.

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UI design and implementation for real product surfaces with taste-first critique, reference routing, project-specific design briefs, variation comparison, and implementation verification. Use when Codex should redesign, build, polish, or critique frontend UI by reading project context, exploring taste, routing through `references/catalog.md`, synthesizing `PROJECT_DESIGN.md` or `design.md`, using investment gates to avoid wrong taste/reference/scope, implementing in the existing stack, and evaluating whether the result is actually better. Best for app screens, dashboards, landing pages, query tools, product surfaces, and design explorations; not for backend-only work or tiny cosmetic edits.

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Visual design verification loop for matching a goal PNG to a live render. Use when the user shares a design mock / goal image and asks to match the UI to it, asks to "iterate on this design" or "match this 1:1", or calls out visual drift in a previous UI iteration. Provides histogram color sampling, shape inspection (radius/border/shadow/bg-mode/fill-vs-context), computed-style checking on the live render, snap, and side-by-side diff. After every UI change the assistant MUST snap the live render and Read the snap with the Read tool before reporting the change done — skipping that is the

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Audit a prompt the user is about to send to Claude (or another coding AI) for completeness and effectiveness. Use this skill whenever the user shares a prompt and asks for feedback — phrases like "is this prompt good", "audit my prompt", "review this prompt", "will Claude understand this", "improve this prompt", "is this enough context". Also trigger proactively when you notice the user is about to dispatch a vague, under-specified, or over-constrained prompt for non-trivial work. Checks the 6 essential elements (goal, scope, existing patterns, constraints, verification, done definition), flags what's missing, explains why each gap matters, and produces an improved rewrite. Treat this skill as the litmus test for "Be Claude's PM" — if the prompt fails the audit, the work that follows will probably fail too.

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Pulp timeline transport, immutable compiled playback programs, bounded arrangement audio rendering, block-level publication latches, stable shells, and ProcessContext projection.

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Design focus modes that actually protect attention: Do Not Disturb schedules, app filtering, notification tiers, and context-specific setups. Use when interruptions fragment your day or 'do not disturb' never stays on.

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Setting up application error tracking — SDK integration, grouping, and context — works with any error-monitoring platform.

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Teach models new tasks from a handful of examples — prompting strategies, example selection, and in-context learning mechanics.

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Build with DeepSeek Coder — open code models with strong reasoning and long-context code understanding.

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Domain-Driven Design: bounded contexts, ubiquitous language, aggregates, and tactical patterns. Use when modeling complex domains or structuring domain logic.

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Keep long conversations coherent — summarization checkpoints, entity tracking, topic stacks, and graceful context handoffs. Use when dialogues run long or span sessions and the agent keeps losing the thread.

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CircleCI guidance — config design, orbs, caching, workflows, contexts, and pipeline optimization.

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Build with Meta's Code Llama — open code models for generation, infilling, and long-context code tasks.

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Design memory for AI agents — short-term context, long-term stores, retrieval strategies, and memory-augmented reasoning. Use when an agent must remember facts, learn preferences, or stay coherent across sessions.

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Production-grade TypeScript reference for React & Next.js frontend development. Covers type narrowing, component Props, generic hooks, discriminated unions, as const, satisfies, Zod validation, TanStack Query, server/client boundaries, forms, state management, performance, accessibility, debugging, and code review. Use when the user works with TypeScript in React or Next.js: type errors, Props design, generics, API typing, SSR/CSR boundaries, hydration issues, form validation, state management, performance, or code review. Also use for "how should I type this?", "why does this type error happen?", or any architectural decision involving TypeScript in a frontend context.

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Use when a project contains docs/memory/, or when the user asks to remember, retrieve, update, compact, forget, or audit project memory. MemoryCustodian manages local plain-text project memory with minimal context loading.

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Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER PROMPT.

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Convert current conversation context into a structured Product Requirements Document (PRD) and optionally post it to GitHub Issues. Use before /supergraph:plan when requirements came from a conversation, user story, or feature discussion rather than a formal spec.

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Generate ScriptableObject-based systems including data containers, event channels, and runtime sets. Use when creating game data (weapons, enemies, items, abilities), cross-system events, or live object tracking. Triggers on requests like "create weapon data", "add an event system", "make a runtime set", "decouple these systems", "create item database", "add a damage event", "track active enemies", "create a config SO", "add game settings data", or any task involving ScriptableObject data containers, event-driven communication, or runtime object tracking. Reads ProjectConfig.yaml -> architecture_pattern to determine SO-first vs DI-first context.

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Generates a new sprint plan or updates an existing one based on the current milestone, completed work, and available capacity. Pulls context from production documents and design backlogs.

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