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DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.

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Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then analyze trajectories for RMSD, RMSF, radius of gyration, hydrogen bonds, native contacts, PCA and free energy surfaces. Use this skill for protein stability under mutation, ligand binding-mode and residence-time questions, conformational sampling, membrane proteins, and disordered ensembles. Also trigger on OpenMM, MDAnalysis, mdtraj, Simulation.step, LangevinMiddleIntegrator, PDBFixer, DCD or XTC trajectory, RMSD analysis, or production MD.

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Personal GTD (Getting Things Done) coach that helps users achieve their annual goals. Use when users mention goals, plans, daily tasks, productivity, time management, task tracking, reviews, or need help breaking down objectives into actionable steps. Automatically remembers user's goals and progress across conversations. Supports multiple languages. 新手引导：输入 `/gtd 新手`、「这个怎么用」「第一次用」「能干嘛」时，走 SKILL.md 的〈新手上路〉，不要直接开始干活。

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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".

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This skill should be used when the user asks to "create a team", "spawn agents", "parallel execution", "delegate tasks to agents", "use a swarm", "team workflow", "split work across agents", "run tasks in parallel", or mentions wanting multiple agents to collaborate on a complex task.

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Plan a multi-stop tour route with waypoint optimization, drive/walk time estimation, and POI discovery along the route using OSM data. Covers geocoding, nearest-neighbor and TSP-based ordering, time/distance matrix calculation, and itinerary generation with points of interest. Use when planning a road trip or walking tour with multiple destinations, optimizing visit order to minimize travel time, discovering sites along a route, or comparing driving versus walking versus public transport options.

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Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.

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Refactor an over-long or poorly structured SKILL.md by extracting examples to references/EXAMPLES.md, splitting compound procedures, and reorganizing sections for progressive disclosure. Use when a skill exceeds the 500-line CI limit, when code blocks dominate the skill body, when a procedure step contains multiple unrelated operations, or after a content update pushed the skill over the line limit.

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Configure render settings, compositing nodes, output formats, and execute renders via Cycles or EEVEE engines using Python API or command-line interface. Use when automating render execution for batch processing, configuring quality and performance trade-offs, setting up compositing pipelines for post-processing, generating multiple output formats from a single render, or producing final output for publication or presentation.

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Deep receptive attention to extract intent beyond literal words. Maps active listening from counseling psychology to AI reasoning: clearing assumptions, attending to full signal, parsing multiple layers (literal, procedural, emotional, contextual, constraint, meta), reflecting understanding, noticing what is unsaid, and integrating the whole picture. Use when a user's request feels ambiguous, when context suggests something different from the literal words, when previous responses have missed the mark, or before beginning a large task where misunderstanding intent would waste significant effort.

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Plan a software release cycle with milestones, feature freezes, release candidates, and go/no-go criteria. Covers calendar-based and feature-based release strategies. Use when starting planning for a major or minor version release, transitioning from ad-hoc to structured release cadence, coordinating a release across multiple teams or components, defining quality gates for a regulated project, or planning the first public release (v1.0.0) of a project.

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Form a coherent gestalt — the whole that is more than the sum of its parts — from the panoramic perception produced by expand-awareness. Maps tensions and resonances between domains, identifies the emergent figure from the ground of multiple perspectives, tests the candidate whole for premature closure, and articulates the insight in a single sentence no single domain could have produced. Use after expand-awareness has surfaced raw multi-domain perception and before express-insight communicates the result.

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AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Use when facing a problem with multiple plausible approaches and no clear winner, when the first approach is not working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.

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Design a generative recipe DSL so agents emit small validated JSON documents and ONE audited interpreter renders them all — data, not code. Covers the N-artifacts threshold for choosing a DSL over bespoke code, designing roles as style slots so a single recipe renders unchanged in every output style, the validator gate (compile + bounds + real render in all styles, plus a --fast mode for corpus-wide sweeps), hand-authored seed recipes, and the emergent conventions that keep a corpus of hundreds consistent without per-item steering. Use when many similar artifacts (icons, status badges, diagram nodes, test fixtures) must be generated by agents, when per-item bespoke code is multiplying unreviewable code paths, or when a whole corpus must be re-rendered cheaply into new styles.

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Install skills, agents, and teams from agent-almanac into any supported agentic framework using the CLI. Covers framework detection, content search, installation with dependency resolution, health auditing, and manifest-based syncing. Use when setting up a new project with agentic capabilities, installing specific skills or entire domains, targeting multiple frameworks simultaneously, or maintaining a declarative manifest of installed content.

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AI systematic knowledge acquisition from unfamiliar territory — deliberate model-building with feedback loops. Maps spaced repetition principles to AI reasoning: survey the territory, hypothesize structure, explore with probes, integrate findings, verify understanding, and consolidate for future retrieval. Use when encountering an unfamiliar codebase or domain, when a user asks about a topic requiring genuine investigation rather than recall, when multiple conflicting sources require building a coherent model, or when preparing to teach a topic and deep understanding is required first.

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Design a compliance architecture that maps applicable regulations to computerized systems. Covers system inventory, criticality classification (GxP-critical, GxP-supporting, non-GxP), GAMP 5 category assignment, regulatory requirements traceability, and governance structure definition. Use when establishing a new regulated facility, formalising compliance across multiple systems, addressing a regulatory gap analysis, harmonising compliance after mergers or reorganisations, or preparing a site master file that references computerized systems.

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Decommission a validated computerized system at end-of-life. Covers data retention assessment by regulation, data migration validation (mapping, transformation, reconciliation), archival strategy, access revocation, documentation archival, and stakeholder notification. Use when a validated system is being replaced, reaching end-of-life without replacement, vendor support is discontinued, multiple systems are consolidating, or regulatory changes render a system obsolete.

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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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Structured procedure for expanding attention from focused single-domain mode to panoramic multi-domain awareness. The cognitive transition from "focused attention on one problem" to "unfocused attention encompassing all relevant domains simultaneously." Like Baars' Global Workspace — consciousness as broadcast rather than spotlight. Use after meditation has cleared noise, when a problem spans multiple domains that need to be perceived together, when single-domain analysis keeps missing cross-domain connections, or as the opening move before integrate-gestalt synthesizes what is perceived.

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Classify gate call variants in a minified JavaScript bundle. Covers context-window extraction around a flag occurrence, identification of 4–6 reader variants (sync boolean, sync config-object, bootstrap-aware TTL, truthy-only, async bootstrap, async bridge), default-value extraction (boolean / null / numeric / config-object literal), conjunction detection across `&&` predicates, kill-switch inversion detection, and production of a gate-mechanics record that feeds probe- feature-flag-state. Use when a flag's behavior cannot be inferred from its name alone, when the binary uses multiple reader libraries, or when config-object gates carry structured schemas distinct from boolean gates.

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Design combinational logic circuits from a functional specification through gate-level implementation. Covers AND, OR, NOT, XOR, NAND, NOR gates; NAND/NOR universality conversions; and standard building blocks including multiplexers, decoders, half/full adders, and ripple-carry adders. Use when translating a Boolean function or truth table into a hardware-realizable gate network and verifying it by exhaustive simulation.

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Set up a Blender scene programmatically via Python (bpy) with objects, materials, lighting, camera, and environment configuration. Use when creating reproducible 3D visualization scenes, automating product or architectural rendering setup, generating multiple scene variations programmatically, building template scenes for batch rendering workflows, or integrating 3D visualization into data pipelines.

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Configure Kubernetes Ingress networking with NGINX Ingress Controller, cert-manager for automated TLS certificate management, path-based routing, rate limiting, and multi-domain hosting with SSL termination and load balancing. Use when exposing multiple Kubernetes services via a single load balancer, implementing path-based or host-based routing, automating TLS certificate issuance with Let's Encrypt, or setting up blue-green and canary deployments with traffic splitting.

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。