「decide」の検索結果

Enhancement-overlay decision protocol for STREAMING the output of long-running LLM / agent runs from the *backend*, not just wiring a typing animation in the UI. Activates when a coder agent must stream final tokens to a chat client, surface intermediate agent steps (which tool, which node, partial reasoning), emit custom tool-progress events, choose a transport (SSE vs WebSocket), or decide what to do when the client disconnects mid-stream. The langchain / langgraph skills mention stream modes but stop at "you can stream"; this skill encodes *what to stream, over what transport, and how to fail safely*.

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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Project-kickoff rubric for the self-host vs managed-cloud decision — when is running your own inference engine / LLM platform worth the ops cost vs paying per-token for a managed API? Decide on two axes — VOLUME (a cost-crossover slider) and COMPLIANCE (a hard gate). Use at kickoff when choosing where to run inference, or when cost / data-residency pressure forces a re-evaluation.

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Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.

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Triage a single security finding — from a scanner, audit, advisory, or report — to a defensible disposition with a mitigation plan, false-positive justification, or accepted-risk writeup. Use when the user mentions 'triage this finding,' 'is this a real vulnerability,' 'mitigation plan,' 'false positive,' 'accept this risk,' 'compensating controls,' 'risk justification,' 'security ticket,' 'CVSS this,' 'should we fix this,' 'disposition,' 'sign off on,' or has a single security finding and needs to decide what to do.

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Presents options for merge, PR, or cleanup. Use when work is complete, tests pass, and you must decide how to integrate.

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Understand what you mean before anything is built. Takes anything — a one-line idea, notes, a spec, or a mix — reads the project first, asks only what is yours to decide, and writes your intent down for you to approve. Use when the user invokes the `ready` skill. Requires git.

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Triage the improvements backlog at .turbo/improvements.md: merge duplicate entries, state the user story or simplification behind each one, and decide with the user which to keep and which to drop. Use when the user asks to "triage improvements", "triage the backlog", "clean up the backlog", "clean up improvements", "groom the backlog", "prune the improvements backlog", or "go through the backlog with me".

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Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".

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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

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QA engineering workflows on top of Playwright. Use when a QA engineer asks to design test cases from a ticket, PRD or acceptance criteria, decide what to automate, write a bug report from a failing test or trace, convert manual test cases (TestRail, Xray, CSV) into Playwright specs with case-ID traceability, or review Playwright test code and pull requests.

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Use when about to take any outward or offensive action (request, payload, persistence, lateral movement, exfil, or feeding captured traffic to the model) — to decide detection footprint, cleanup, and secret redaction first

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Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the `cacheComponents` flag, work through a flood of blocking-prerender / instant validation errors, run the `cache-components-instant-false` codemod, or decide between opting routes out with `export const instant = false` and fixing them in place.

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Use when analyzing an existing TypeScript or JavaScript codebase to decide where and how to introduce Inngest. Covers repository discovery, framework and package detection, finding durability gaps in HTTP handlers, webhooks, cron jobs, queues, long-running jobs, AI agents, Agent Evals, polling loops, eval loops, and side-effect-heavy code, then producing and implementing an incremental integration plan.

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Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.

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Creates new Architecture Decision Record (ADR) documents for significant architectural changes using a consistent template and repository-aware naming and storage guidance. Use when a user or agent decides on an architectural change, needs to document technical rationale, or wants to add a new ADR to the project history.

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When the user needs to decide what to build, cut, and defer for a first release or minimum viable version of a product or feature.

日本語の概要は準備中です。原文の説明を表示しています。

Use when an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

日本語の概要は準備中です。原文の説明を表示しています。

Decides hand-vs-compiler for loop transforms (unrolling, SIMD, fusion, branchless). Use when reviewing/authoring a hot loop or tempted to hand-optimize one.

日本語の概要は準備中です。原文の説明を表示しています。

Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.

日本語の概要は準備中です。原文の説明を表示しています。

Design and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.

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Argues the strongest honest case against a decision, plan, belief, hire, pricing move, strategy, or piece of work the user is leaning toward. Steelmans the other side, ranks at most three objections by consequence, runs a short pre-mortem, credits what the user got right, and ends with what would have to be true for their choice to work. Calibrated and collegial, never combative; says plainly when the position is strong. Use whenever the user says "play devil's advocate", "argue the other side", "poke holes in this", "what am I missing", "am I making a mistake", "talk me out of it", "stress-test this idea", "tell me why this is a bad idea", or asks for pushback on something they have mostly decided. Supports gentle, standard, and hard intensity. Not for interrogating requirements before a build (grill-the-brief) or staging a multi-persona debate (debate-simulator).

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Decide when to delegate to Honey's read-only subagents (hive-scout, hive-reviewer) instead of working inline, so the expensive tokens — large file reads and review passes injected back into the orchestrator's context — come back compressed (Honey Lever 3). Use when a task is search-heavy or review-heavy, spans many files, or you want to keep the orchestrator's context small. Not for trivial one-file work.

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What this skill does and when to use it. Claude reads this to decide relevance. Include keywords users would naturally say.

日本語の概要は準備中です。原文の説明を表示しています。