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影视编剧 (Scriptwriting / Screenwriting) — 为电影 / 电视 / 流媒体剧集写戏剧蓝本的从业者认知操作系统: 决定「讲什么故事、人物是谁为何转变、场景如何被戏剧化、主题为何重要」的学科 —— 区别于导演 (调度 / 视觉实现)、制片 (融资 / 排期)、小说 (无格式 / 场景 / 视觉动作约束)。覆盖 (a) 故事与结构理论 (亚里士多德《诗学》情节 / 人物 / 思想 + 突转 peripeteia / 发现 anagnorisis + 净化 catharsis; Syd Field 范式 / 三幕 / 情节点; Freytag 金字塔; Egri 前提; McKee《故事》掌控性理念 / 鸿沟 / 价值正负转变 / 激励事件 / 渐进式复杂化; Truby《故事剖析》22 步 + 道德论证; Vogler/Campbell 英雄之旅; Blake Snyder《救猫咪》15 拍节拍表; Dan Harmon 故事环; John Yorke 五幕《进入森林》; Frank Daniel 八序列法; 起承转合东亚戏剧结构), (b) 人物 (想要 vs 需要 / 外在目标 vs 内在缺失, 人物弧光 正 / 负 / 平, flaw-wound-ghost 创伤背景, 主动 vs 被动主角的能动性, Egri 三维人物 生理 / 社会 / 心理, 反派是主角的镜子, 群像与关系网, 人物在压力下的选择中显形), (c) 对白 (潜台词 subtext, 对白即动作与冲突而非信息, 避免太直白 on the nose, 人物独特声口, 信息植入 exposition, Mamet/Sorkin 意图-阻碍式对白, 展示而非告知, 中文剧台词工艺), (d) 场景与序列工艺 (场景是有目标 / 冲突 / 转折的微型故事, 场景价值充能与转折点, 晚进早出, 八序列法, set piece, 转场, 场景头 / 动作行 / 括号提示 格式工艺), (e) 主题与前提 (掌控性理念 / 前提, 主题是被戏剧化的论证而非说教, 道德论证 Truby, 主题统一性, 这到底在讲什么), (f) 类型 (类型惯例是对观众的承诺, Snyder 10 类故事, 颠覆 vs 尊重套路, 类型片 vs 文艺片, 中国类型 主旋律 / 古装 / 年代 / 悬疑 / 甜宠 / 都市 / 军旅 / 历史正剧), (g) 格式与工业流程 (好莱坞剧本格式与 Final Draft, logline 一句话故事, treatment 故事大纲, 节拍表, 分场 / 分集大纲, spec 原创剧本 vs 委托, pitch, coverage, 美剧编剧室与 showrunner 制; 中国 大纲→人物小传→分集大纲→剧本, 长剧集 40 集结构, 网络电影 / 网大, 分账剧, 平台定制剧, 微短剧竖屏短剧), (h) 改稿与开发 (写作就是改写, 改稿 / 润色 / 推倒重写, 处理意见 / 改稿意见, 片方与平台 notes, 杀死你的宝贝, 围读, 开发地狱), (i) 改编 (IP改编 小说 / 漫画 / 游戏改编, 忠于精神而非字句, 压缩, 中国网文 IP 热), (j) 中国影视编剧产业现实 (审查 / 广电总局约束, 备案 / 立项, 平台定制剧 爱优腾芒, 分账, 编剧维权 署名权 / 欠薪 / 枪手, 注水剧, 流量 + 大IP 资本逻辑 vs 原创工艺, 导演中心制 vs 编剧话语权), (k) 编剧职业与工艺生涯 (spec vs 委托, 编剧 vs 编审 / 策划, 单兵 vs 编剧室, 在开发链条中的位置, WGA 与署名仲裁规范, 入行, 中国编剧地位与权益)。跨 电影 / 美剧流媒体 / 中国电影与长剧集。不含 导演 / 摄影 / 制片 / 融资 / 表演 / 电影理论批评为终点 / 泛小说写作 / 纪录片 / 新闻 —— 本 skill 是「戏剧化编剧」工艺: 故事 / 人物 / 场景 / 对白 / 主题 / 结构 / 格式 与围绕它们的产业流程。 (Scriptwriting / Screenwriting for film & TV (影视编剧) — the cognitive operating system of practitioners who write the dramatic blueprint of films, television, and streaming series: the discipline of deciding WHAT story is told, WHO the characters are and why they change, HOW the scenes are dramatized, and WHY it matters thematically — distinct from directing (staging/visual realization), producing (financing/scheduling), and prose fiction (which has no format/scene/visual-action constraints). Covers (a) story & structure theory (Aristotle's Poetics — plot/character/thought + reversal peripeteia/recognition anagnorisis + catharsis; Syd Field's paradigm/three-act/plot points; Freytag's pyramid; Lajos Egri's premise; Robert McKee's Story — controlling idea/the gap/value charge/inciting incident/progressive complications; John Truby's 22 steps & moral argument; Christopher Vogler/Joseph Campbell hero's journey; Blake Snyder's Save the Cat 15-beat sheet; Dan Harmon's story circle; John Yorke's 5-act Into the Woods; Frank Daniel's 8-sequence method; 起承转合 East-Asian dramatic structure), (b) character (want vs need/external goal vs internal lack, character arc positive/negative/flat, 人物弧光, flaw-wound-ghost backstory, agency active vs passive protagonist, Egri's 3-dimensional character physiology/sociology/psychology, antagonist as the protagonist's mirror, ensemble & relationship webs, character revealed through choice under pressure), (c) dialogue (subtext 潜台词, dialogue as action & conflict not information, 'on the nose' avoidance, distinct character voice, exposition handling, Mamet/Sorkin intention-obstacle dialogue, show-don't-tell, 台词 craft in Chinese drama), (d) scene & sequence craft (scene as a mini-story with goal/conflict/turn, scene value charge & turning point, enter-late-leave-early, the 8-sequence method, set pieces, transitions, slug line/action line/parenthetical format craft), (e) theme & premise (controlling idea/premise/前提, theme as dramatized argument not message, moral argument Truby, thematic unity, 'what is this really about'), (f) genre (genre conventions as audience promise, Snyder's 10 story types, subverting vs honoring tropes, 类型片 vs 文艺片, Chinese genres 主旋律/古装/年代/悬疑/甜宠/都市/军旅/历史正剧), (g) format & industry process (Holly

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Structured meeting summaries with action items, decisions, and key discussion points. Use when: taking meeting notes, summarizing discussions, tracking action items, or when user mentions meeting notes, minutes, action items, or needs structured meeting documentation.

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Next-action selector for the Suede pack: inspects current repo, terminal, plan, or handoff state read-only and returns one recommended next move plus a short, self-contained copy/paste prompt for it. Use when the user asks 'what's next', 'what should I do next', 'recommend the next move', 'give me the prompt', 'expand prompt', or 'make it granular', especially after a review, audit, plan, or stalled task. NOT FOR: executing the recommended action without the user's separate authorization, or coordinating a multi-lane build across specialists (use suede-agent-teams).

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Inspect, stream, and explicitly adjust live Android UI tweak values or invoke app-owned actions through Snap-O. Use when a request mentions Snap-O Tweaks, tweak-enabled Android apps, runtime UI parameters, Compose tweak or action controls, tweak discovery, previously adjusted or currently inactive tweaks, applying all user-made tweak changes, typed values, defaults or resets, tweak batches, explicitly registered actions, the Tweaks REST/SSE API, or selecting between CLI, macOS tool, in-app overlay, and custom tweak interfaces.

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Neo4j Java Driver v6 — driver lifecycle, Maven/Gradle setup, executableQuery, executeRead/Write managed transactions, explicit transactions, async/reactive patterns, error handling, data type mapping, connection pool tuning, causal consistency/bookmarks. Use when writing Java or Kotlin code that connects to Neo4j via GraphDatabase.driver, executableQuery, SessionConfig, executeRead, executeWrite, or TransactionCallback. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver version upgrades — use neo4j-migration-skill. Does NOT cover Spring Data Neo4j (@Node, Neo4jRepository) — use neo4j-spring-data-skill.

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Covers the Neo4j Go Driver v6 — driver lifecycle, ExecuteQuery, managed and explicit transactions, session config, error handling, data type mapping, and connection tuning. Use when writing Go code that connects to Neo4j, setting up NewDriver or ExecuteQuery, debugging sessions/transactions/result handling, or working with neo4j-go-driver v5→v6 migration. Triggers on NewDriver, ExecuteQuery, SessionConfig, ManagedTransaction, neo4j-go-driver. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver version migration steps — use neo4j-migration-skill.

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Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry.

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Map control, influence, and concern after systems analysis. Use when students need wise agency without being made responsible for everything.

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Apply evidence-first engineering discipline to any agent-built software project: fact discovery before action, evidence-calibrated status reporting, explicit change boundaries and stage gates, controlled confirmation for risky or external actions, sensitive-information redaction for anything shared publicly, and resumable continuity after interruptions. Use when users ask an agent to take over an existing codebase, deliver a feature across stages, verify whether work is actually complete, judge release readiness, or keep conclusions honest about what is proven versus assumed. This foundation skill is domain-neutral; vertical suites (for example mini-program engineering) build on it by adding domain facts and platform rules.

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Remix v2 data loading and mutations. Use when writing loaders, actions, deferred data, revalidation logic, or pending state. Triggers on loader, action, useLoaderData, useActionData, json(), defer(), <Await>, shouldRevalidate, useRevalidator, useNavigation, useTransition (v1 holdover).

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集約とトランザクション境界の関係を明確化し、複数集約を単一トランザクションに含めるアンチパターンを 検出・是正する。集約は強い整合性境界であり、ユースケースで複数集約を更新する場合は結果整合性を 使うべきという原則を適用する。コードレビュー、ユースケース設計、リファクタリング時に トランザクション境界の問題を検出する場合に使用。 対象言語: 言語非依存（Java, Kotlin, Scala, TypeScript, Go, Rust, Python等すべて）。 トリガー：「複数集約を同じトランザクションで更新している」「ユースケースに@Transactionalがある」 「集約間の整合性をどう取るか」「Sagaパターンを使うべきか」「トランザクション境界の設計」 「1トランザクション1集約」「結果整合性の実装」「集約をまたぐトランザクション」 といったトランザクション境界関連リクエストで起動。

Provides CI/CD pipeline best practices for GitHub Actions, deployment strategies, and pipeline optimization. Use when setting up pipelines, configuring GitHub Actions, managing deployments, or when user mentions 'CI', 'CD', 'pipeline', 'GitHub Actions', 'deploy', 'workflow', 'build'.

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Chemical equilibrium, chemical kinetics, and electrochemistry — the quantitative core of physical chemistry. Covers equilibrium constants (Keq, Kc, Kp), Le Chatelier's principle, ICE tables, buffers and the Henderson-Hasselbalch equation, solubility product (Ksp) and the common-ion effect, rate laws, reaction order, integrated rate laws, half-life, the Arrhenius equation and activation energy, catalysis, redox reactions, galvanic and electrolytic cells, standard cell potentials, and the Nernst equation. Use when solving quantitative problems about how far a reaction goes (equilibrium), how fast it goes (kinetics), or how it converts chemical energy to electrical energy (electrochemistry).

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Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project the action or artifact each one implies, and check whether they diverge: if two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.

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Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Use when asked to review a Maa project from initial commit through a target commit, map `action: Custom` pipeline nodes to `@AgentServer.custom_action(...)` implementations, find pipeline/custom/option patterns or breakages, and produce a report plus skill-improvement recommendations.

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Test and validate MaaFramework Pipeline JSON, recognition nodes, action nodes, CustomAction/CustomRecognition wiring, resource loading, and end-to-end task behavior. Use when running `run_pipeline`, checking OCR/TemplateMatch/ColorMatch ROI, validating `context.run_task()` results, auditing `action: Custom` or `recognition: Custom` registrations, or choosing the right resource-check command.

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Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.

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Run a focused security pass on GitHub Actions workflows before merge so token misuse, dangerous permissions, and unpinned actions are caught early.

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Load maintained action manuals for fragile websites so agents can execute known flows more reliably than generic browser prompting.

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act is an open-source CLI tool that runs GitHub Actions workflows locally using Docker, enabling fast feedback on workflow changes without pushing to GitHub. It is a standard tool for local Actions development and testing.

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Detect when agents are stuck in repetitive action loops using a rolling 20-action window with escalating nudges — replan after 3 similar actions, explore after 5, force-done after 8.

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Audits Claude Code repositories. Two-phase workflow — Phase 1 sweeps 14 hygiene categories (permissions, hooks, CLAUDE.md bloat, Skills hygiene, process-abstraction, plus structural) and an optional 7-layer leak check. Phase 2 executes [APPROVED] markers from the report file. Use for repo cleanup, pre-distribution sanity, post-feature hygiene, environment audit, process-abstraction discovery. Trigger on "audit my CC repo," "sweep my repository for hygiene," "check this CC environment," "find process abstraction candidates," "review my CLAUDE.md," "clean up my .claude directory," "pre-distribution hygiene check," "find layer leaks in my CC setup." Routes structural findings to rootnode-cc-design REMEDIATE. Composes with rootnode-critic-gate. CC-only — does not scan Claude Projects. Do NOT use for building a Skill (use rootnode-skill-builder), compiling a prompt (use rootnode-prompt-compilation), scoring a single prompt (use rootnode-prompt-validation), or auditing a Project (use rootnode-project-audit).

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Redact reverse-engineering findings for public disclosure while preserving methodology, generalizable patterns, and teaching value. Covers the private-vs-public repo split, deny-list pattern maintenance, orphan-commit publish pattern that prevents `git log` leaks, category-based redaction calibration (methodology/pattern/version-finding/internal), and the `check-redaction.sh`-style CI gate that blocks merges when a deny-listed pattern appears. Use when publishing findings about a CLI harness you don't own, when preparing upstream proposals to an unrelated project, or when archiving a private research repo for public reference.

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Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.

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