「reasoning」の検索結果

Elicit step-by-step reasoning from language models to boost accuracy on math, logic, and multi-step problems.

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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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**WORKFLOW SKILL** — Understand before implementing. USE FOR: any task where the agent might rush to code — forces understanding WHY/WHAT FOR/FOR WHOM before defining a solution, activates planning and reasoning discipline, prevents technical debt from shallow requests. Use to validate intent, plan before coding, reason before acting, challenge assumptions, prevent premature implementation. DO NOT USE FOR: deep multi-technology analysis (use contextação), persisting decisions across tasks (use task-map).

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**WORKFLOW SKILL** — Persist analysis so it survives context compression and chains between tasks. USE FOR: any task whose decisions affect future work — features, refactors, architecture changes, integrations. Produces a lightweight Task Analysis Map that externalizes intent, decisions, and chain context. Use to document decisions, create continuity, prevent context loss, bridge tasks, externalize reasoning. DO NOT USE FOR: quick intent validation (use task-intent), deep structured analysis (use contextação).

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Use this skill whenever the user wants to create or improve a presentation for an academic context — conference papers, seminar talks, thesis defenses, grant briefings, lab meetings, invited lectures, or any presentation where the audience will evaluate reasoning and evidence. Triggers include: 'conference talk', 'seminar slides', 'thesis defense', 'research presentation', 'academic deck', 'academic presentation'. Also triggers when the user asks to 'make slides' in combination with academic content (e.g., 'make slides for my paper on X', 'create a presentation for my dissertation defense', 'build a deck for my grant proposal'). This skill governs CONTENT and STRUCTURE decisions. For the technical work of creating or editing the .pptx file itself, also read the pptx SKILL.md.

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Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when "why" isn't obvious. Use when user says "write a commit", "commit message", "generate commit", "/commit", or invokes /caveman-commit. Auto-triggers when staging changes.

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Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.

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Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-creation.

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Use when designing or reasoning about an Agentforce Grid worksheet — the spreadsheet-like Agentforce workspace where rows are jobs and columns are sequential Data / AI / Action steps that process left to right — to run bulk record updates, generate insights, or test multi-turn AI conversations over CRM or Data Cloud (Data 360) data without Apex or Flow. Trigger keywords: Agentforce Grid, workbook, worksheet, grid column, data column, AI column, action column, bulk AI update, Flex Credits per row, metered compute. NOT for building the agent, its topics, or its actions themselves (use agentforce/* action and topic skills), NOT for single-record prompt design (use the prompt-builder skills), NOT for deployable metadata/Apex (Grid is a no-code Studio tool, not a metadata type), and NOT for AI governance/ethics policy (use admin/ai-ethics-and-governance-requirements).

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Create custom modules for [Harbor Boost](https://github.com/av/harbor/tree/main/boost), an optimizing LLM proxy. Use when building Python modules that intercept/transform LLM chat completions—reasoning chains, prompt injection, structured outputs, artifacts, or custom workflows. Triggers on requests to create Boost modules, extend LLM behavior via proxy, or implement chat completion middleware.

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Expert in proof techniques, mathematical reasoning, and rigorous argumentation for students learning to write proofs

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Adaptive Swift/iOS quiz with two modes. Classic is lighter, Mastery requires mechanism-level reasoning.

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Activate a persistent visible-reasoning mode that requires detailed internalcot CLI notes before substantive tools and answers, throughout the conversation. Use only when the user explicitly invokes $internalcot, says "internalcot on", or asks to enable internalcot mode. Keep it active until the user explicitly turns it off. Do not trigger merely because a user asks to think carefully, reason deeply, or show work.

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UI/UX design intelligence for web, mobile, and desktop. This skill should be used when designing, building, reviewing, or fixing interfaces, including pages, components, design systems, accessibility, interaction, responsive layout, typography, color, charts, and stack-specific UI implementation. Searchable local data: 79 searchable styles (50 active), 192 product palettes and reasoning profiles, 74 font pairings, 119 UX guidelines, 105 icons, 17 GSAP presets, 25 chart types, and 22 stacks.

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Checks async races, thread safety, TOCTOU, deadlocks, blocking I/O, resource contention, cross-process races. Two-layer detection: grep + agent reasoning.

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Manages intent — features, enhancements, bugs and the reasoning behind them — recorded as prose in the knowledge base with a lifecycle. Use when capturing new work, moving work through backlog, refinement or implementation, releasing or cancelling work, or asking what is pending, in flight, or no longer valid.

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Audit and maintain the learnings in `docs/solutions/` against the current codebase — fix drifted paths and links, merge overlapping docs, replace guidance the code no longer supports, delete obsolete docs, reconcile the `CONCEPTS.md` glossary, then report and commit. Also bootstraps `CONCEPTS.md` on request and, when asked, culls docs whose reasoning the code already explains. Use for "refresh the learnings", "audit docs/solutions", "are our solution docs still accurate", "clean up / prune the learnings", "create a CONCEPTS.md", or after a refactor, rename, migration, or dependency upgrade that likely invalidated documented solutions. Not for capturing a new learning — use `learn-with-docs`.

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Capture a solved, verified problem as a durable learning in `docs/solutions/` so the next agent or engineer does not repeat the mistake or redo the investigation, and keep the `CONCEPTS.md` glossary in step. Use after a fix, debugging session, or design decision whose reasoning is not readable from the final code, tests, or existing docs — "document this", "write up what we learned", "capture this learning", "add it to solutions", "that worked, save it for next time". Writes nothing for routine fixes the code already explains. For auditing or cleaning up existing learnings, use `learn-with-docs-refresh`.

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。

Ultra-compressed commit message generator. Cuts noise from commit messages while preserving intent and reasoning. Conventional Commits format. Subject ≤50 chars, body only when "why" isn't obvious. Use when user says "write a commit", "commit message", "generate commit", "/commit", or invokes /caveman-commit. Auto-triggers when staging changes.

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Create or refresh AEO-optimized content that gets cited by AI assistants (Gemini, ChatGPT, Perplexity) using only free tools. Two modes: CREATE new content targeting a specific prompt, or REFRESH existing content to improve AI citation-worthiness. Researches what AI models currently cite, builds a competitive brief, and produces citation-worthy content. Use when a user wants to: write content optimized for AI citations, create articles that show up in AI answers, refresh/update existing content for better AI visibility, build authority content for answer engines, or produce AEO content without paid tools. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only. Pairs with aeo-prompt-research-free (which identifies WHAT to write about; this skill handles HOW to write or refresh it).

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Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explainable recommendations. Use when the user asks for decision support, MCDA, weighted scoring, prioritization, vendor selection, route planning, hiring shortlist ranking, tool comparison, procurement decisions, or auditable agent decision logic.

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Discover which AI prompts and topics matter for a brand's Answer Engine Optimization (AEO) using only free tools. Crawls a website, analyzes the brand's positioning, generates prioritized prompts people ask AI assistants, and audits existing content coverage — all without paid APIs. Use when a user wants to: find what questions people ask AI about their industry, discover AEO opportunities, research prompts for content creation, audit a site's AI visibility, or build an AEO content strategy. No API keys required — uses web_fetch, web_search (free tier), and LLM reasoning only.

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Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.

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Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

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。