「cis」の検索結果

Organic story anatomy from John Truby's The Anatomy of Story (有机结构/设计原则/22步骤/道德议题/四角对立/幽灵/场景编排) — the designing principle (story process plus original execution), the seven key steps as the organic spine, all twenty-two steps with the middle-act machinery the beat sheets lack (ghost, fake-ally opponent, plan, opponent's drive, ally's attack, fake defeat, three revelation-and-decision pairs, audience revelation, visit to death, moral decision), four-corner opposition, the moral-argument chain with the hero-opponent power-balance rule, scene weave with structure-step tags and storyline numbers, and the accordion rule (never fewer than seven steps). Kept side by side with the Field/Snyder page maps. Use when the middle act sags into unconnected episodes, when the premise works but the story feels mechanical, when one antagonist cannot carry the theme, when the theme stays stated instead of acted, or when a BS2 board needs an organic second pass.

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Scaffold a graded problem set with sections, problems, worked solutions, and short "why this matters" explainers across analytical, empirical, and coding types. Use when user says "make a problem set on X", "scaffold exercises for this lecture", "create practice problems", "generate homework with a solution key", "build a graded assignment on topic Y". Emits a clean student set plus a separate solution key — NOT for grading submissions or auto-checking student answers.

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Distill the current conversation into a structured note (decisions made, open questions, file pointers with line numbers, next 1–3 actions) and save to `quality_reports/session_logs/` before auto-compression. Differs from `/checkpoint` (explicit stop-point snapshot) and from auto-compaction (which truncates rather than distills). Use when context is approaching auto-compact threshold, when a long pipeline has accumulated many decisions, or when the user says "compress", "distil this session", "before we hit auto-compact", "structured handoff before context resets".

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Add a Jev-powered decision and release gate after an office assistant generates a report, proposal, spreadsheet, document, or slide deck. Use for claim-to-evidence checks, narrow requirement decisions, risk triage, and human-review routing; do not use it to generate the deliverable or claim that Jev browsed or verified a URL.

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Health-check your meeting knowledge for contradictions, stale commitments, and decision conflicts. Use when the user asks "any conflicts in my meetings", "check for stale action items", "lint my meetings", "consistency check", "are there contradictions", or wants to audit their decision history.

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Build a production behavior-tree runtime (Blackboard, action/condition leaves, sequence/selector/parallel composites, decorators) and a Utility AI system (response curves — linear, exponential, sigmoid, quadratic — considerations, and action evaluators), plus hybrid BT-drives-Utility agents. Use when implementing a reusable behavior-tree or utility-based decision system, or tuning enemy/NPC decisions beyond a simple FSM, or when the user mentions behavior tree, blackboard, decorator, selector, sequence, tick status, utility AI, response/scoring curve, or consideration. For choosing between FSM/BT/steering or for pathfinding, use game-ai; for Unreal's BehaviorTree/Blackboard assets, use unreal-behavior-trees.

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Run /architect when choosing between approaches, designing a feature or page, picking a tech stack, or when /develop says a decision is owed, anytime a load bearing technical decision is unmade. Asks deep questions, recommends an answer, and writes a build spec to docs/specs/. Owns all spec files.

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Decode a counteroffer after you resign — what the raise, promotion promise, or title bump really signals, the statistics-informed risks of staying, and a clear-eyed decision framework. Use when asked my company countered my resignation, should I accept a counteroffer, they offered me more to stay, or decode this retention offer. Produces a component-by-component decode with 🔴🟡🟢 severity, the questions that expose which promises are real, and the stay/go decision sheet.

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Write a one-page briefing note that gets a busy principal up to speed fast. Use when asked to brief a minister/executive/official, prepare a briefing note or read-ahead, or summarize an issue for a decision or meeting. Produces a tight, single-page note: purpose, background, key facts/considerations, and a recommendation or the decision sought — scannable in two minutes.

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Convert a meeting into async work that actually decides — the doc-plus-deadline format that replaces the room, the comment-window rules, the decision-closure step that async usually fumbles, and the honest test for what still needs synchronous. Use when asked can this meeting be async, replace our status meeting with a doc, run this decision without a call, or async isn't working for us. Produces the conversion design, the async artifact format, the closure protocol, and the still-needs-a-room list.

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Write formal board meeting minutes from an agenda, notes, transcript, or discussion summary. Use when asked to draft board minutes, governance minutes, meeting minutes for a board, or a formal record of decisions and actions. Produces structured board minutes with attendees, agenda items, resolutions, decisions, action register, and approval-ready wording.

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Write a board pre-read that's sent before the meeting so the meeting is about decisions, not status. Use when asked to prepare a board pre-read, a board update/package, or pre-meeting materials for a board. Produces a board pre-read — a TL;DR, the metrics dashboard vs. plan, what's working / what's not, the decisions and asks for the board, and risks — designed to be read in advance.

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Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.

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Run a club, PTA, or association AGM that finishes on time and holds up later — the notice and agenda done right, a quorum plan, minutes that capture decisions not conversations, elections without awkwardness, and the follow-up that makes decisions real. Use when a volunteer says 'I have to run the AGM', 'what goes in the agenda', 'nobody comes to our meetings', or 'our elections are a mess'. Produces the notice, agenda, chair's script, minutes template, and quorum rescue plan.

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Help users master the art of making high-stakes, irreversible decisions by using structured frameworks to reduce cognitive bias and maintain organizational momentum.

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Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

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Audit the choices an implementing agent made, not its diff — a pure decision audit that traces the session's history into a choices ledger, changes no code, and never blocks an unsupervised run. Working code still embeds architecture the user never chose; surface it because future work inherits it. Use when the user wants to review the decisions the AI made on their behalf, before merging or committing AI-implemented work, when integrating a delegated subagent's pass, or when a fix "works" but might be a point fix.

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Use when the user asks what they are not seeing, what they forgot, what could go wrong, or what an expert would say about a plan, decision, document, code change, or setup they already have. Triggers: "what am I missing", "what don't I know", "poke holes in this", "what would an expert ask", "что я упускаю", "что я не вижу", or /clueless-blindspots. One-shot review; does not change the always-on level.

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Register a deferred decision in the debt registry. Trigger by judgment, not a marker scan, whenever a future reader would ask "why this way?": an unmade decision, stub, loosened type, bypassed check, swallowed error, a default picked "for now", or a TODO/FIXME/HACK/XXX marker. Trigger immediately whenever you defer work, or when the user invokes $add. Over-register freely; the developer drops with "drop A", "drop A,C", or "drop all".

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Use when calibrating expectations for the fast, decisive American Economic Review: Insights (AER: Insights) review process — conditional-accept-or-reject decisions, the single-round model, and pre-empting the objections a short paper cannot afford. Explains the process and stress-tests the paper; it does not write the response letter (see aeri-rebuttal).

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Use when articulating why an Agricultural Systems (AgSy) result matters — its relevance for farm design, management, decision support, or policy. AgSy values systems analysis that informs a decision, so this is what separates an AgSy paper from a methods demo. It frames implications honestly within the model's scope; it does not over-claim or invent impact.

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Use when writing the response to an Agricultural Systems (AgSy) revision decision. AgSy review is single anonymized with at least two expert systems reviewers and an editor decision, and revisions often turn on model evaluation, sensitivity/uncertainty, and trade-off framing. The response must convert each reviewer without alienating the editor. Structures the response letter; it does not fabricate new results.

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Use when polishing the prose of an Advanced Materials manuscript for Wiley house style, concision, precise materials nomenclature and units, and readability across materials subfields. Polishes language; does not restructure the advance or manage the length budget.

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Use when you need to understand the Academy of Management Review (AMR) developmental, multi-round review and interpret a decision letter focused on theoretical novelty and logical soundness. Explains the process and decision types; it does NOT draft the response document (that is amr-rebuttal).

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