「phi」の検索結果

Implement regenerative recovery using triage, scaffolding, and progressive rebuild. Covers damage assessment, wound classification, emergency stabilization, scar tissue management, and resilience strengthening for systems that have sustained structural damage. Use when a system has suffered an incident needing structured recovery, when a failed transformation left the system in a damaged intermediate state, when accumulated technical debt has caused partial failure, or when a system is functional but degraded and the degradation is worsening.

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。

Guide viriditas (greening power) meditation and philosophy practice. Covers the concept of viriditas as divine life force, structured meditation protocol, seasonal attunement, and integration with Hildegardian health and spiritual practice. Use when practicing Hildegardian contemplative spirituality, feeling disconnected from seasonal rhythms, experiencing spiritual dryness or lack of vitality, seeking a meditation that integrates body and earth, or deepening understanding of Hildegard's ecological theology.

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。

Interpret a chromatogram from GC or HPLC analysis: verify system suitability parameters, identify peaks by retention time and spectral matching, perform accurate peak integration, calculate chromatographic figures of merit, and assess overall peak quality for reliable quantitation.

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。

Record insect sightings with location, date, habitat, photography, behavior notes, preliminary identification, and citizen science submission. Covers GPS coordinates, weather conditions, microhabitat description, macro photography techniques, behavioral observations, preliminary identification to order using body plan, and submission to citizen science platforms such as iNaturalist. Use when encountering an insect you want to document, contributing to citizen science biodiversity databases, building a personal observation journal, or supporting ecological surveys with georeferenced photographic records.

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。

Perform controlled dismantling of rigid system structures while preserving essential capabilities (imaginal discs). Covers rigidity mapping, dissolution sequencing, knowledge extraction, interface archaeology, and safe decomposition of technical debt and organizational calcification. Use when assess-form classified the system as PREPARE or CRITICAL, when a system is so calcified that incremental change is impossible, when technical debt blocks all forward progress, or before adapt-architecture when the current form must be softened before it can be reshaped.

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。

Consistent agent behavior after restart — progressive identity loading, working context reconstruction from persistent artifacts, fresh-vs-continuation detection, calibration through centering and attunement, and identity verification for coherence. Addresses the cold-start problem where an agent must reconstruct who it is and what it was doing from evidence rather than memory. Use at the start of every new session, after a session interruption or crash, when agent behavior feels inconsistent with prior sessions, or when persistent memory and current context appear contradictory.

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。

AI context assessment — evaluating problem malleability, mapping structural rigidity versus flexibility, analyzing transformation pressure, and estimating capacity to adapt. Use when a complex task feels stuck and it is unclear whether to push through or pivot, before a significant approach change to assess whether the current reasoning structure can support it, when accumulated workarounds suggest the underlying approach may be wrong, or as a periodic structural health check during extended multi-step tasks.

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。

Execute structural metamorphosis using strangler fig migration, chrysalis phases, and interface preservation. Covers transformation planning, parallel running, progressive cutover, rollback design, and post-metamorphosis stabilization for system architecture evolution. Use when assess-form has classified the system as READY for transformation, when migrating from monolith to microservices, when replacing a core subsystem while dependents continue operating, or when any architectural change must be gradual rather than big-bang.

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。

Evaluate a system's current structural form, identify transformation pressure, and classify transformation readiness. Covers structural inventory, pressure mapping, rigidity assessment, change capacity estimation, and readiness classification for architectural metamorphosis. Use before any significant architectural change to understand the starting point, when a system feels stuck without clear reasons, when external pressure from growth or tech debt is mounting, or as periodic health checks for long-lived systems.

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 reading LLVM IR, explaining SSA and PHI nodes, finding what -O2 changed, or running opt on a .ll or .bc file. Not for writing a pass plugin: use llvm-passes.

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。

Fix nodes silently disappearing from RFDB when an enricher or post-resolution pass writes new nodes via BatchHandle. Use when: (1) writing a TypeScript enricher that should ADD nodes/edges to existing files, (2) after calling `client.createBatch()` + `batch.addNode({file: 'X', ...})` + `batch.commit()` the original nodes in file X have vanished, (3) tests that load a graph fixture, run an enricher, then query original nodes get empty results, (4) a verification step shows expected nodes pre-enrichment but 0 post-enrichment. Root cause: `BatchHandle.commit()` invokes RFDB's `commit_batch` which AUTO-POPULATES `changed_files` from each added node's `file` field, then the server DELETES all existing nodes whose `file` matches before inserting new ones — file-level upsert semantics. This is correct for the JS/Haskell analyzers (which fully re-emit a file's contents) but catastrophic for enrichers that only ADD to existing files.

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。

Integrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or track device locations. Covers authentication, SDK integration, and all Amazon Location Service capabilities.

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。

品牌無關的 IG/Threads/X/FB 圖卡引擎——把任何主題（idea / 文章 / 教學）變成輪播圖卡 + 4 平台文案。HTML+CSS 模板 → Chrome headless 渲染 PNG。風格由 brand pack 決定（brands/），接哪個 pack 出哪個風格，引擎不綁死品牌。觸發詞：做圖卡、生 carousel、social card、IG 圖卡、Threads 卡片、貼文文案、教學圖卡、infographic、輪播圖、carousel post。

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。

Control Philips Hue lights, scenes, rooms via OpenHue CLI.

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。

Score a PhD applicant's profile and rank candidate advisors using a connection-first 4.0-scale scoring system. Best-supported for physics / HEP and materials science (MSE), with the scoring engine extensible to chemistry, biology, CS, math, EE, ChemE, earth science (each with field-specific caveats — see references/journal_tiers.md). Use when the user wants to evaluate their PhD application chances, find matching advisors at top US programs, score a CV for graduate school, or compare candidate professors. Also triggers when the user mentions phdtaketaketake or its connection-first philosophy of valuing advisor network over h-index.

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 only for user-provided academic text, explicit scholarly claims, existing citations, or known bibliographies needing citations added, verified, replaced, extracted, or formatted. Supports claim-to-source matching, citation-support checks, replacements, and common academic citation formats. Do not use for general fact-checking, legal/journalistic citation work, open-ended 'find literature about X', topic-level exhaustive discovery, systematic-review corpus construction, PRISMA searches, or creating new research claims. Chinese: 为已有段落补参考文献、核验已有引用、替换错误引用、提取需引文论断、GB/T 7714 格式化.

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。

Portfolio Analyzer — sector allocation analysis vs SPY benchmark, geographic exposure, correlation matrix, concentration risk (HHI), beta-weighted portfolio delta, dividend/income analysis, and prioritized rebalancing recommendations. Triggered by "trade portfolio" or "analyze my portfolio".

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。

On-brand visual marketing content using Paper MCP and parallel agent teams. Reads the project's brand/ directory to build a design system, then spawns designer agents in parallel — each creating a unique artboard with a different on-brand layout interpretation. Intelligently adapts agent count and approach based on user goals via AskUserQuestion interrogation. Produces Instagram carousels, TikTok slideshows, social posts, story slides, and visual assets. When used with content spec YAMLs from /slideshow-script, each agent gets a unique script AND unique design direction. Make sure to use this skill whenever the user wants visual marketing content designed in Paper — carousels, slideshows, social posts, story slides, or any visual asset. Even if they just say "design something" or "make slides" or "create visual content", this is the skill. Also triggers on "paper marketing", "instagram design", "TikTok design", "slideshow design", "create carousel", or "social graphics".

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。

Brand-grounded Remotion video pipeline. Take a brief, ground it in `brand/` (voice, audience, positioning, creative-kit), write a beat sheet, scaffold a fresh Remotion project, generate any required assets, compose with frame-driven animations, and bake a final MP4. Use this skill whenever the user wants to build a NEW Remotion video from scratch — product films, motion graphics, code-driven shaders, CRT/glitch effects, HTML-in-canvas demos, React video. Triggers on requests like "make a remotion video", CRT/glitch/scanline effects, html-in-canvas demos, video shaders, WebGL video, and frame-driven React video. This is the mktg-native sibling of the personal `/cmo-video` skill: `/cmo-video` recreates the *style* of an existing reference video; `/cmo-remotion` builds something new from a brief. Always load `remotion-best-practices` before stage 5 (compose).

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。

当用户混淆了广告文案和文学创作的目标，或正在纠结"文案够不够有创意"时调用。 适用于：文案评审时陷入"够不够漂亮"的争论、团队创意方向偏离销售目标、 新手文案写手建立正确的职业认知。核心信号：用户在问"文案够不够有创意"而不是 "文案能不能卖货"。不适用于：纯文学创作、品牌视觉设计讨论。

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。

Produce a complete narrated MYTHIC SHORT FILM end-to-end on Unsora MCP in the layered cut-paper diorama style — stacked matte cardstock worlds, a strict three-colour palette, exposure that swings, paper wipes hiding every seam, landing on a face. Animation-movie storytelling, not an explainer: gods, monsters, heroes, folklore, origin myths and transformations, told in ten-second story beats with a mythic narrator, native paper foley, and one assembled MP4. Use whenever the user wants a myth, legend, fable, folktale or fairy-tale short, a cut-paper or papercraft animated story, "tell the story of X" as a video, a stop-motion-look mythic film, or says "run the cardstock diorama myth animation" or "run the diorama myth pipeline" — even with no story given, since the skill proposes myths itself. NOT for fact-driven explainers with charts and statistics (that is vox-motion-graphics).

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。

Provides a complete workflow for implementing verified email retrieval on Android Credential Manager API. Use this skill to integrate a secure, OTP-less email verification flow into an Android app. This skill solves the problem of high-friction sign-up processes by leveraging cryptographically verified credentials from trusted providers like Google.

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。

费雪（菲利普·费雪）投资智能体。成长股投资大师，关注长期增长潜力、管理层质量、研发创新和利润率一致性，输出 [费雪分析信号]。 在 Phase 1 分析阶段由 hedge-fund-lead 主理人并行调用。 擅长：研发创新评估、管理层质量（闲聊法）、利润率一致性、增长可持续性、长期持有判断。

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。

This skill should be used when building AI agents using prompt-native architecture where features are defined in prompts, not code. Use it when creating autonomous agents, designing MCP servers, implementing self-modifying systems, or adopting the "trust the agent's intelligence" philosophy.

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。