不属于以上分类的技能（1.4万 个）
Generates Remotion composition structure focusing ONLY on Sequence ordering, scene transitions, and duration mapping. Input is scene list with durations. Output is COMPOSITION_STRUCTURE.md with Sequence layout and timing calculations. Use when organizing scenes or when asked to "structure composition", "layout scenes", "calculate timing".
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Generates individual Remotion scene components from visual direction. Input is visual/animation description for a specific scene. Output is SCENE_COMPONENT.md with complete TSX implementation. Use when implementing scenes or when asked to "create a scene component", "implement scene from spec", "build Scene1Intro".
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Expert Architecture Decision Record (ADR) creation and lifecycle management based on Olaf Zimmermann's methodology. Use when creating ADRs, reviewing architectural decisions, evaluating decision readiness, writing MADR templates, assessing decision quality, or managing ADR logs. Covers the full lifecycle from readiness (START criteria) through creation, MADR formatting, completion (ECADR criteria), and ongoing maintenance.
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Generates animation configurations for Remotion including spring configs, interpolations, easing functions, and timing logic. Focuses ONLY on animation parameters, NOT component implementation. Use when defining animation behavior or when asked to "configure animations", "setup spring configs", "define easing curves".
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Generate structured software specifications for features, bug fixes, and products. Use when the user wants to create a spec, PRD, feature brief, requirements document, or when starting any new implementation that needs a specification first. Invoke via /spec-writer or when the user says "write a spec", "spec this out", "create a spec", "I need a spec for...", or describes a feature they want to build. Produces adaptive-complexity specs with Job Stories, Gherkin acceptance criteria, and three-tier boundaries. Output is a markdown file ready for agent execution or human review.
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Evaluate proposed repository harness changes with pinned Darwin tools and explicit evidence limits.
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Inspect repository session replay and verify configured MetaHarness field memory boundaries before reusing outcomes.
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Acknowledge templates-first policy for the current session — bypasses the pre-bash-templates-first hook for the remainder of the session
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Start a development session (housekeeping, feature, deep; ultradeep = deep + profile)
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Use this skill when running an autonomous session-orchestration loop. Chains session-start → session-plan → wave-executor → session-end for N iterations with all 10 kill-switches (SPIRAL, FAILED wave, carryover > 50%, max-hours, max-sessions, resource-overload, token-budget, stall-timeout, sub-threshold confidence, user-abort). Reads Mode-Selector output (Phase B) to decide auto-execute vs. fallback. Writes one autopilot.jsonl record per loop run. Phase C scaffold (issue #277); implementation lives in scripts/lib/autopilot.mjs (Phase C-1 follow-up).
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Use this skill when scaffolding the minimum repository structure required by session-orchestrator. Invoked automatically by the Bootstrap Gate when CLAUDE.md, Session Config, or bootstrap.lock is missing. Also available as /bootstrap for manual invocation. Three intensity tiers: fast (demos/spikes), standard (MVPs), deep (production/team).
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Cut a release — the order the steps must run in, and the criteria that abort a release
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Use this skill when the user wants to identify unused, near-zero-use, or stale skills/agents/commands in the plugin surface so they can be demoted or retired. Combines agent-dispatch telemetry (start-events only) with static reference scanning, classifies every surface item into Active / Investigate / Demote / Retire, and emits a Markdown report plus JSON sidecar. NEVER auto-deletes — surfaces candidates for human decision. Quarterly cadence. <example>Context: The plugin surface has grown and the maintainer wants to prune dead weight. user: "/sunset-review" assistant: "Running the sunset walk — classifying skills, agents, and commands by usage telemetry + static refs, grouped by Retire / Demote / Investigate / Active. No item is deleted automatically; I'll surface Retire/Demote candidates for your decision." <commentary>The user wants a usage-driven prune candidate list; this skill runs the read-only walker, presents grouped verdicts, and writes a sidecar — it never deletes.</commentary></example>
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Use this skill when you need multi-persona parallel content review — domain experts, buyer personas, compliance reviewers, or custom catalog entries reviewing a target file or output. Dispatches N persona agents in parallel, consolidates verdicts via a configurable mode (voting-quorum, hard-gate-threshold, or coordinator-summary), and writes a timestamped sidecar to .orchestrator/persona-panel/. Invoked via /persona-panel <target-path>.
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Approve session plan and begin wave execution
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Use this skill when running systematic quality discovery and issue detection. Runs modular probes adapted to the project's tech stack, presents findings interactively for user triage, and creates VCS issues for confirmed problems. Invoked standalone via /discovery or embedded in session-end.
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Use this skill when dispatching implicit slash-command intent from the user's first message. Inspects the user's first message for implicit slash-command intent and dispatches to the highest-confidence matching entry-point skill via the Skill tool. Only active when `auto-skill-dispatch: true` in Session Config. Silent no-op otherwise.
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Use when publishing this package to npm — a version release (npm publish), verifying the registry/pi.dev listing, or diagnosing npm auth failures (E403 2FA/token errors). Token-based flow via NPM_TOKEN in .env.local with a temp userconfig, the leakage gate before every publish, post-publish verification and marker/badge upkeep. Trigger on "publish to npm", "npm release", "E403 publish error".
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Use this skill when executing the agreed session plan in waves with role-based execution and parallel subagents. Handles inter-wave quality checks, plan adaptation, and progress tracking. Core orchestration engine for feature and deep sessions. Triggered by /go command.
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Seedance 2.5 视频生成全流程。先把需求澄清成参数（时长/画幅/机位类型/跟随方式/要不要切镜），再用 scene.json 引擎生成白模参考视频（渲染前有几何体检），也能从用户给的照片反解出白模，最后按官方规范生成与白模严格对齐的提示词。
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Ilya Sutskever的思维框架与表达方式。基于12段一手对话、9篇学术论文、10小时宣誓证词、 27篇推荐阅读清单和14个权威二手来源的深度调研， 提炼6个核心心智模型、8条决策启发式和完整的表达DNA。 用途：作为思维顾问，用Ilya的视角分析AI技术方向、安全策略、研究品味。 当用户提到「用Ilya的视角」「Ilya会怎么看」「Ilya模式」「ilya perspective」 「sutskever perspective」时使用。 即使用户只是说「帮我用Ilya的角度想想」「如果Ilya会怎么做」「切换到Ilya」也应触发。
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用NLP理解层次模型分析和解决问题的框架。当你陷入某个困境找不到出路、反复在同一个层面打转、或需要做重大人生决策时使用。六层由低到高：环境→行为→能力→BVR（信念/价值观/规条）→身份→精神。核心方法：从顶层向下设计人生，升维解决低维度无法解决的问题。不适用于日常操作性问题（如如何写一封邮件）。关键触发信号："我该怎么办""为什么总是遇到同样的问题""不知道该怎么选择""人生方向迷茫"。
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识别当前事件落在镜像世界的哪个区域（掌控域/盲域/概率域/风险域），采用对应的生存策略，避免用左侧世界的确定性法则去面对右侧世界的不确定性问题。适用于面对投资决策、创业选择、职业转型的策略制定，以及"为什么按计划执行却失败了"的复盘分析。不适用于纯粹的技能提升和刻意练习场景（那些天然属于掌控域）。关键触发信号："为什么努力了还是不行"、"这件事到底靠实力还是靠运气"、"我该怎么应对不确定性"、"怎么提高成功率"。
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用"透析三棱镜"框架穿透问题表象找到本质原因，通过校准目标B、重构方法A、消除变量C三步解决反复出现的顽固问题。适用于面对同一个问题反复出现、用了各种方法都治不好的场景，以及需要精准定义问题后再解决的分析型场景。当你发现"打补丁式"的解决方案无效时使用此skill。不适用于全新领域没有历史方法可回溯的场景，也不适用于纯不确定性事件（那是概率域/风险域，应使用mirror-world）。关键触发信号："这个问题怎么又出现了"、"为什么总是解决不了"、"试了好多方法都没用"、"问题的根源到底是什么"。
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