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This skill enables cross-model dialogue between Claude and Gemini with shared visual memory. Use when the user wants to generate images, have visual dialogues with AI, create scientific illustrations with continuity, or have multiple AI perspectives respond to the same prompt. Key trigger phrases: "generate an image", "visual dialogue", "ask the daimones", "resonance field", "Minoan tarot", "cross-model", "KV cache", "MESSAGE TO NEXT FRAME".

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Structured code review through the shared OpenClaw agent-skills installation.

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List configured models, guide provider setup, verify local Kimi companion readiness, and manage the PreToolUse hook plus optional review gate. Use when asked about available models, subscription/API-key setup, default selection, or explicitly requested to install, check, enable, disable, or uninstall the integration. Codex and Claude Code share one ~/.kimi-code/config.toml but each own a host-scoped block, so $kimi-setup here does not disturb Claude Code's /kimi:setup (and vice-versa).

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CORE learning workflow — use this whenever the user has a source note open (paper, post, book, lecture, course, clipping) and wants to extract knowledge, understand what they just read, or build it into their vault. Triggers on "process this note", "process this paper", "extract concepts", "what can I learn from this", "atomize this", "add this to my vault", "what's important here", or when the user shares a note and asks for help understanding it. Do NOT wait for an explicit /process command — if the user is engaging with a source note and wants to go deeper, this skill applies.

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Process an online course into detailed vault notes — one per lecture, with slides and transcripts synthesized. Use this skill whenever the user shares a course URL, syllabus page, or lecture playlist and wants structured notes from it. Also triggers on "process this course", "take notes on these lectures", "course notes", any university course page, or when someone pastes a link to a course with multiple lectures/sessions. Even if the user just says "check out this course" or drops a .edu link with a schedule — use this skill.

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Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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Generates a structured Project Brief — a comprehensive markdown document that extracts goals, architecture, knowledge file inventory, Custom Instructions summary, Memory contents, current state, ecosystem position, and key decisions from a Claude Project. Briefs serve as uploadable context documents: add one to any other Project for immediate deep awareness of the source Project's purpose, architecture, and progress. Use when user says "create a brief," "brief this project," "extract project context," "generate a project summary for another project," "I need to share this project's context," "prepare this project for cross-project reference," or "document this project." Also use when the user is preparing to work across Projects and needs portable context. Do NOT use for session handoffs, project audits, or Memory optimization (use rootnode-session-handoff, rootnode-project-audit, or rootnode-memory-optimization respectively, if available).

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 this skill when tool outputs exceed 2000 tokens, tasks span multiple conversation turns, sub-agents need to share state, context window is bloating, plans need to persist across summarization, terminal/log output needs selective querying, or when user mentions "offload context", "dynamic context discovery", "filesystem memory", "scratch pad", "reduce context bloat", or "just-in-time context loading".

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。

Scaffold a new CLI command using Commander.js with options, action handler, three output modes (human-readable, quiet, JSON), and optional ceremony variant. Covers command naming, option design, shared context patterns, error handling, and integration testing. Use when adding a command to an existing Commander.js CLI, designing a new CLI tool from scratch, or standardizing command structure across a multi-command CLI.

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Cross-session shared memory for agents. Use at session start to load the persistent bias-log via `mcp__memex__recent_observations`, mid-session to search prior context with `mcp__memex__search`, and immediately when a bias surfaces to log a new `observation` via `mcp__memex__add`. The six-layer reconstruction trail (raw → embedded → indexed → graphed → curated → reflexive) means memex stores facts AND the agent's own reasoning patterns across sessions. Prefer querying memex before re-deriving prior architectural decisions; re-derivation is itself evidence that the trail is incomplete.

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Apply software pipelining (double-buffering) to a tiled GPU kernel to overlap global memory loads with Tensor Core computation. Covers prologue/loop/epilogue restructuring, LDG-register vs cp.async (LDGSTS) variant selection based on compute/load ratio, shared memory budget verification against architecture-specific occupancy cliffs, and SASS-level verification of load/compute overlap.

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Generate a Quarto-based tour report with embedded maps, daily itineraries, logistics tables, and accommodation/transport details. Produces a self-contained HTML or PDF document suitable for offline use during travel. Use when compiling a planned tour into a shareable document, creating an offline-accessible travel guide, documenting a completed trip with photos and statistics, or producing a professional tour proposal for a group or client.

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Deploy a self-hosted SearXNG meta search engine via Docker Compose. Covers settings.yml configuration, engine selection, result proxying, Nginx frontend, persistence, and updates. Use when setting up a private search engine without tracking, aggregating results from multiple providers, running a shared search instance for a team or organisation, or replacing reliance on a single search provider.

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Generate and verify a hiking gear checklist optimized for season, duration, difficulty, and group size with weight management. Covers the ten essentials, layering systems, navigation tools, emergency kit, and group gear distribution. Use when preparing for a day hike or multi-day trekking tour, packing for a group and distributing shared gear, adapting a standard gear list to specific conditions, reviewing gear before departure, or managing pack weight for long or technical routes.

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Conduct a blameless post-mortem analysis after an incident. Build timeline reconstruction, identify contributing factors, and generate actionable improvements. Focus on systemic issues rather than individual blame. Use after any production incident or service degradation, following a near-miss, when investigating recurring issues, or to share systemic learnings across teams.

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Apply collective intelligence coordination patterns — stigmergy, local rules, and quorum sensing — to organize distributed systems, teams, or workflows without centralized control. Covers signal design, agent autonomy boundaries, emergent behavior cultivation, and feedback loop tuning. Use when designing distributed systems without a coordination bottleneck, organizing teams that must self-coordinate, building event-driven architectures with shared state communication, or replacing fragile centralized orchestration with resilient emergent coordination.

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Compute and analyze gematria (Hebrew numerical values) using standard, ordinal, and reduced methods. Covers word-to-number conversion, isopsephy comparisons, and interpretive frameworks. Use when computing the numerical value of a Hebrew word or phrase, comparing two words for shared gematria values, studying a biblical verse or divine name for numerical correspondences, or connecting a numerical result to its position on the Tree of Life.

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Shared conventions for every SDLC development-phase architect agent: hard rules, code quality bar, workflow steps (superpowers invocation, spec reading, codebase exploration, verification), and the report/compact-summary contract. Architects load this skill first, then apply their stack-specific instructions on top.

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State management decision tree for React SPAs: useState/useReducer, Context API, Zustand, Jotai, Redux Toolkit, TanStack Query, SWR. Detect what's installed and apply matching patterns. Use this skill to: - Pick the right state tool for the data shape (local / shared UI / server / form). - Use Context correctly (and know when to switch to Zustand/Jotai). - Implement a Zustand or Jotai store. - Set up TanStack Query for server state with caching, pagination, mutations. - Avoid common mistakes (overusing Redux, prop drilling, forgetting query invalidation). Do NOT use this skill for: - General hook conventions (see react-conventions). - Form state specifically (see react-forms). - Routing state (see react-routing). - Testing stores/queries (see react-testing).

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Parallel SDLC batch runner. Analyzes a list of independent task descriptions, groups them by heuristic file-conflict prediction, dispatches one worktree-isolated background agent per task (each running the full pipeline-orchestrator: BA → Dev → QA → Sec → Docs), and aggregates results into a single batch summary. Use when: - `/sdlc:batch` invokes this skill after parsing its arguments (Step 2 of commands/batch.md) - You have 2+ independent feature/fix descriptions to run through the SDLC pipeline in parallel Do NOT use for: - A single task (use pipeline-orchestrator directly via /sdlc:start) - Tasks that must share a single working tree (this skill isolates each task in its own git worktree)

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Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.

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。

pytest testing patterns for any Python project: test structure, fixtures, parametrize, conftest.py, monkeypatch, tmp_path, markers, unittest.mock (MagicMock, patch), pytest-cov coverage, and best practices. Stack-agnostic — referenced by every Python plugin in the marketplace. Use this skill to: - Organise tests with conftest.py shared fixtures and a clear test/src layout. - Write parametrized tests to cover multiple inputs without duplication. - Mock external dependencies (HTTP, DB, filesystem) with unittest.mock and monkeypatch. - Measure and enforce code coverage with pytest-cov. Do NOT use this skill for: - Framework-specific test types (Django TestCase / WebTestCase, FastAPI TestClient, Flask test client — see framework plugin skills). - Language idioms — see python-foundation:python-conventions.

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Use when debugging shared library load failures, setting RPATH or RUNPATH, applying soname versioning, writing dlopen plugins, or intercepting with LD_PRELOAD. Not for static archives: use binutils.

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Use when analyzing warp divergence, memory coalescing, shared memory bank conflicts, cache behavior, atomics, or occupancy tradeoffs on NVIDIA and AMD GPUs. Not for tool commands: use cuda-profiling.

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。