'bun' 검색 결과

Own whether a multi-document product-architecture binder is internally consistent, complete against its stated customer/contingency/architecture coverage, and honest about which older product ambitions it has absorbed, superseded, deferred, contradicted, orphaned, or rejected. Use when running a binder Architect of Record pass, before an implementation chain cites a binder chapter as ready, when a proof-gate owner is missing, or when reconciling the binder against the older ambition corpus (website, plans, examples, ADRs). NOT for auditing a single Claude Skill bundle's structure or frontmatter (use skill-hygiene), rendering a single-PM accept/reject verdict on one finished deliverable (use port-daddy-user-surrogate-pm-review), or sequencing/stewarding a roadmap's sidequests once a gap or operator decision has been surfaced (use legible-roadmap-with-sidequests).

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Produces a narrow-interval quality signal for a WinDAGs skill that a third party can verify without inspecting model weights or method internals. The mechanism is attribution-kNN over a tamper-evident outcome log (task→skills→accept/reject store), combined with a conformal-prediction calibration certificate that provides a formal coverage guarantee. Thompson/Beta sampling is explicitly rejected as a category error: it produces a selection signal for stationary i.i.d. rewards, not an attestation signal for context-dependent LLM output quality. The resulting bundle — outcome-log Merkle root + conformal threshold + optional TEE guardrail signature — constitutes a "narrow-interval trust" credential: verifiable, bounded, and internals-opaque.

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Verifies build output integrity after code changes. Runs builds, validates artifact structure, checks for regressions in bundle size, asset references, and static export completeness. The last line of defense before a bad deploy ships.

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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next/dynamic code splitting and build-graph cost; first stop for build failures. Use before lazy-loading or deferring a heavy component, adding a dynamic import, React.lazy, or ssr: false; on 'window is not defined' or a hydration mismatch; shrinking the bundle; or on 'build failed', OOM, SIGKILL, a slower build, or 'Collecting page data'.

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Interacting with Mediabunny

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Interacting with Mediabunny

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Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.

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Deploy applications and websites to Vercel using the bundled `scripts/deploy.sh` claimable-preview flow. Use when the user asks to deploy to Vercel, wants a preview URL, or says to push a project live on Vercel.

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Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit/inpaint/mask, background removal or replacement, transparent background, product shots, concept art, covers, or batch variants); run the bundled CLI (`scripts/image_gen.py`) and require `OPENAI_API_KEY` for live calls.

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Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.

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Development commands for OneKey monorepo. Use when running dev servers, building apps, linting, testing, or troubleshooting build issues. Triggers on yarn, dev, build, lint, test, desktop, mobile, web, extension, ios, android, compile, bundle.

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Performance optimization guidelines for OneKey React/React Native applications. Use when optimizing app performance, fixing UI freezes/lag, reducing re-renders, handling concurrent operations, or analyzing performance bottlenecks. Triggers on performance, optimization, optimize, slow, lag, freeze, hang, jank, stutter, memory, leak, concurrent, batching, batch, memoization, memo, bridge, windowSize, contentVisibility, FlashList, re-render, fps, tti, bundle.

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Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency.

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

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Maxim L3 pack. Bundles: FedRAMP + NIST 800-53 + Section 508. Primary framework: NIST 800-53 (federal security controls).

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Maxim L2 pack. Bundles: L1.1 AI Governance + L1.2 MemPalace Pro + L1.3 Proactive Watch. Primary framework: Fogg Behavior Model.

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Maxim L2 pack. Bundles: L1.1 + L1.2 + L1.3 + L1.4 + L1.5 (five L1 packs). Primary framework: COM-B (Michie et al.).

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Maxim L2 pack. Bundles: All six L1 packs. Primary framework: Behavioral Moat Framing Doctrine (ADR-007).

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Maxim L2 pack. Bundles: L1.5 Brand & Design Pro + L1.6 Behavioral Intelligence + L1.2 MemPalace Pro. Primary framework: EAST (Easy, Attractive, Social, Timely).

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Maxim L3 pack. Bundles: Attorney-client privilege + ethics walls + conflict-of-interest patterns. Primary framework: ABA Model Rules of Professional Conduct.

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Maxim L3 pack. Bundles: HIPAA + ISO 13485 + ISO 14971 + FHIR/HL7. Primary framework: ISO 14971 (medical-device risk management).

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Maxim L3 pack. Bundles: PCI-DSS + FINTRAC + SOX + FDX. Primary framework: PCI-DSS v4.0 (Payment Card Industry).

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