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概要と使いどころ
Structures earnout and contingent payment mechanisms with milestone definitions, measurement periods, and payout scenarios. Use when modeling earnouts, designing milestone-based payments, or valuing contingent consideration.
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Tracks margin requirements with initial/variation margin, portfolio margin optimization, and collateral eligibility analysis. Use when managing margin, optimizing collateral, or analyzing portfolio margin impact.
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。
Coordinates competitive auction workflows from teaser distribution through definitive agreement execution. Use when running sell-side processes, managing bid rounds, or tracking buyer engagement.
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。
Constructs multi-factor models with value, momentum, quality, size, and volatility factor definitions and portfolio construction rules. Use when building factor models, designing systematic strategies, or constructing factor portfolios.
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。
Evaluates competition law exposure with market definition, HHI analysis, and remedy estimation for proposed transactions. Use when assessing merger clearance risk, analyzing market concentration, or preparing HSR filings.
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。
Evaluates financial and incurrence covenant packages with headroom analysis, definition review, and covenant-lite comparison. Use when analyzing loan covenants, negotiating covenant levels, or assessing borrower flexibility.
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。
Initiate a session handoff to transfer context and pending actions to a new terminal session. Executing this skill safely drains tasks and launches a new cross-platform window natively.
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。
Unified skill that guides spec creation through structured, interactive process.
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。
Autonomous fixed-budget ML experiment loop — setup, iterative hypothesis testing, git-based keep/discard tracking, and indefinite autonomous execution. Implements the karpathy/autoresearch protocol.
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 you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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。
Specifies the preferred syntax for asynchronous operations using async/await and onMount for component initialization. This results in cleaner and more readable asynchronous code.
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。
Populate domain specs (glossary, entity definitions, use-cases) from SPEC.md content during implementation.
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。
Scaffolds CI/CD pipeline definitions using GitHub Actions for build, test, lint, and deploy workflows.
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。
Component type definitions, discovery questions, and settings validation for fullstack-typescript projects.
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。
Component type definitions, settings schemas, directory patterns, and validation rules for the fullstack-typescript tech pack.
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。
Declare variables in smallest possible scope, initialize close to first use, minimize span and live time
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。
Instantiate a phase artefact (requirements-spec / design-spec / test-spec) from the Programme bundle's template into docs/specs/<feature-id>/. Refuses to overwrite existing artefacts.
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。
Build or update a CONTEXT.md domain glossary for the current project. Establishes shared vocabulary with term definitions, avoid-aliases, and relationships so agents use consistent, concise language. This skill should be used when starting a new project, when domain terminology is inconsistent across sessions, when the user says "build a glossary", "define our terms", "shared vocabulary", or "domain language", or when an existing CONTEXT.md needs updating after new domain concepts emerge.
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 creating, auditing, or maintaining AGENTS.md files and Codex CLI configuration for any project—including initializing AGENTS.md for cross-agent compatibility (Codex, Cursor, Copilot, Devin, Jules, Amp, Gemini CLI), generating config.toml or .rules files, scaffolding .agents/skills/, converting CLAUDE.md to AGENTS.md, or auditing existing agent configs for bloat and staleness. Complementary to codex-orchestrator (which executes subagents; this skill creates the config files they consume).
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。
Create, audit, and maintain CLAUDE.md documentation files that configure Claude Code for projects. Use this skill when (1) initializing a new project with Claude Code configuration, (2) reviewing or improving existing CLAUDE.md files, (3) organizing project instructions using progressive disclosure patterns, (4) converting repeated instructions into permanent documentation, or (5) setting up agent_docs/ structures for larger codebases. Handles the WHAT/WHY/HOW framework, conciseness optimization, and file import patterns.
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。
Eight tested identity approaches for Claude prompts, each shaping depth, vocabulary, reasoning style, and what Claude treats as obvious vs. requiring explanation. Use when the user wants a specific identity template — retrieving, reviewing, customizing, or building a role definition. Trigger on: "give me the Strategic Advisor identity," "show me the identity template for," "I need a Technical Architect role," "build a custom role for," "show me all available identities." Also use when reviewing a prompt's identity layer or when output lacks domain-appropriate depth. Provides 8 tested approaches across strategy, technical, research, communications, and operations domains plus a template for building custom identities. If the user is unsure which identity fits their task, use rootnode-block-selection first if available. Do NOT use for evaluating complete prompts — use rootnode-prompt-validation if available. Do NOT use for full prompt assembly — use rootnode-prompt-compilation 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。
Compute relative and absolute binding free energies with the Open Free Energy toolkit — the rigorous alchemical alternative to docking scores when a congeneric series needs reliable potency ranking. Use this skill to plan a perturbation network over a ligand set, choose atom mappings, run hybrid-topology or separated-topology protocols, and analyse the result — per-edge ΔΔG with uncertainty, cycle-closure error, and mean unsigned error against measured affinities. Also trigger on OpenFE, alchemical transformation, thermodynamic cycle, RBFE, ABFE, SepTop, lambda window, MBAR, cycle closure, or perturbation map.
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Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.
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。
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.
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。