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概要と使いどころ
Run a read-only adversarial Kimi challenge review that questions assumptions, design choices, and tradeoffs. Use when the user wants pushback on whether the approach is right, not a defect-only review.
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Independent re-derivation gate for proposed changes during autonomous execution. Evaluates a change (code diff, config edit, engine evolution, schema change) against the work's authority matrix and a 4-check protocol: invariant compliance, scope authorization, detection narrowness, and regression risk. Returns structured JSON with pass/fail per check, an overall verdict (APPROVE / REQUEST_CHANGES / REJECT), and blockers. Profile-driven thresholds (strict for unattended runs, lenient for desk supervision). Use when an autonomous agent proposes an engine change, when reviewing a Claude Code-authored modification before merge, or when an unattended profile requires independent re-derivation. Trigger on: "review/approve this proposed change," "critic-gate this," "is this change safe," "is this safe to merge," "should I let this land," "run the critic." Do NOT use for handoff-readiness checks (use rootnode-handoff-trigger-check), general code review, or design-time correctness. Authority matrix required.
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Evaluates whether work-in-design is ready to hand off from a chat design conversation to autonomous execution (Claude Code, n8n, agent runtime). Runs a 7-condition gate: spec stability, verification surface, invariants, pump-primer, work decomposition, rollback cost, token budget headroom. Three invocation modes — deliberate (caller provides work_context, returns JSON verdict), proactive sensing (Claude detects handoff signals in design conversation and offers the check), conversational walkthrough (Claude walks the 7 conditions as discussion topics). Returns structured JSON in all modes. Use when designing a Claude Code deployment, scoping an autonomous run, or deciding next move. Trigger on: "ready to hand off," "ready for execution," "is this CC-ready," "handoff check," "walk me through readiness," "are we ready to ship this," "should I just hand this off," or proactive detection. Do NOT use for evaluating completed work, runtime selection, or prompt quality. Profile-agnostic.
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Guides selection of identity, reasoning, and output approaches for Claude prompts based on task characteristics. Trigger on: "help me choose an approach," "which approach fits this task," "recommend a prompt pattern," "compare reasoning methods," "map this task to the right approach," "what combination of approaches," "which identity fits," "which reasoning method." Also trigger on symptom-phrased: "my prompt feels generic," "I don't know which approach to use," "my output lacks domain depth." Covers decision-tree logic across 8 identity approaches, 18 reasoning variants, and 10 output formats. After selection, use the relevant catalog skill if available to retrieve full templates. Activate whenever approach-selection across multiple categories is the primary decision. Do NOT use when the user already names a specific approach to retrieve (use the relevant catalog skill directly if available) or for evaluating existing prompts (use rootnode-prompt-validation 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。
Generate production-ready React/Next.js code from Figma designs. Instructs proper Figma MCP tool usage, component reuse strategies, and variant mapping. **CRITICAL: ALWAYS use this skill when user provides a Figma link or mentions implementing/redesigning from Figma design.** Never attempt manual implementation without invoking this skill first.
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Number antibody variable domains, annotate CDRs, and assess developability from sequence. Use this skill to apply IMGT, Kabat, Chothia, Martin, or AHo numbering with ANARCI, delimit CDRs and framework regions, scan for chemical liabilities (N-glycosylation sequons, deamidation NG, isomerisation DG, oxidation, unpaired cysteine, fragmentation), compute pI, net charge, extinction coefficient and hydrophobicity, and plan humanisation by CDR grafting. Also trigger on antibody, nanobody, VHH, scFv, Fab, CDR, framework, ANARCI, abnumber, IgBLAST, OAS, SAbDab, humanization, Vernier residues, or developability.
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Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.
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。
Adversarial, security-first review of a pull request from an EXTERNAL or untrusted contributor — a fork PR, a first-time or unknown author, anyone without write access — before a maintainer merges it. Use this whenever you're reviewing, triaging, or deciding whether to merge a PR opened against tbh-meter by someone outside the maintainer team: 'review PR #123', 'someone opened a PR — is it safe to merge?', 'check this contributor's changes', 'can we take this fork PR?', 'is this contribution legit?'. This is supply-chain DEFENSE, not ordinary code review: every merged PR auto-updates onto every user's machine and ships a memory-reading .exe plus an unsandboxed Electron app, so the author and the PR description are UNTRUSTED and the diff is the only evidence. It posts its verdict as a real GitHub review — an inline comment pinned to each problematic line (via the pulls/reviews API), not just one summary comment. NOT for your own or a fellow maintainer's branch (use self-review for that), and prefer this over the generic review flow for anything coming from outside the team.
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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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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Provision and manage cloud infrastructure using Terraform with HCL modules, remote state backends, workspaces, and plan/apply workflow. Implement infrastructure as code patterns with variable management, output values, and state locking for team collaboration. Use when provisioning new cloud infrastructure, migrating from ClickOps or CloudFormation to declarative IaC, managing multi-environment infrastructure, versioning infrastructure changes alongside application code, or enforcing standards through reusable modules.
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Plan garden activities using solar, lunar, and biodynamic calendars. Covers USDA hardiness zones, frost date calculation, equinox/solstice anchoring, synodic lunar cycle (waxing/waning), ascending/descending moon, Maria Thun biodynamic calendar (root/leaf/flower/fruit days), succession planting schedules, and seasonal task planning. Use when planning a new growing season and needing a planting schedule, integrating lunar or biodynamic timing into garden practice, calculating frost dates and planting windows for a specific zone, setting up succession planting for continuous harvest, or conducting end-of-season review.
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Conduct structured insect behavior observations using sampling protocols, ethogram categories, event recording, interaction logging, environmental context, and summary analysis. Covers focal animal sampling, scan sampling, all-occurrences sampling, and instantaneous sampling methods. Defines a standard insect ethogram with locomotion, feeding, grooming, mating, defense, communication, and rest categories. Includes timestamped event recording, intraspecific and interspecific interaction logging, environmental covariate documentation, and time budget analysis. Use when studying insect behavior for ecological research, documenting behavioral repertoires for a species, observing pollinator activity or predator-prey dynamics, or supporting conservation assessments with behavioral data.
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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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Fit cognitive drift-diffusion models (Ratcliff DDM) to reaction time and accuracy data with parameter estimation (drift rate, boundary separation, non-decision time), model comparison, and parameter recovery validation. Use when modeling binary decision-making with reaction time data, estimating cognitive parameters from experimental data, comparing sequential sampling model variants, or decomposing speed-accuracy tradeoff effects into latent cognitive components.
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。
Formulate a quantum mechanics or quantum chemistry problem with proper mathematical framework including Hilbert space, operators, boundary conditions, and approximation method selection. Use when setting up a quantum mechanics problem for analytic or numerical solution, formulating a quantum chemistry calculation, translating a physical scenario into the Schrodinger or Dirac formalism, or choosing between perturbation theory, variational methods, DFT, and exact diagonalization.
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Evaluate and simplify Boolean expressions using truth tables, algebraic laws (De Morgan, distributive, absorption, idempotent, consensus), and Karnaugh maps for up to six variables. Use when you need to reduce a Boolean expression to its minimal sum-of-products or product-of-sums form, verify logical equivalence between two expressions, or prepare a minimized function for gate-level 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。
Design terminal output for a CLI tool with chalk colors, Unicode glyphs, multiple verbosity levels (human, verbose, quiet, JSON), and consistent voice rules. Covers color palette selection, status indicator design, reporter function architecture, ceremony/narrative output variants, and cross-terminal compatibility. Use when building a new CLI reporter module, adding warm narrative output to an existing tool, standardizing output across multiple commands, or designing machine-readable JSON alongside human-readable text.
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。
Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.
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。
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.
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。
Compose 2D graphics programmatically using SVG generation, diagram layout algorithms, image compositing, and batch processing workflows. Use when generating diagrams, flowcharts, or infographics programmatically, creating reproducible scientific figures, automating production of badges or visual assets, building custom chart types not in standard libraries, or batch generating graphics with parameter variations.
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。
Remove dead code, unused imports, fix lint warnings, and normalize formatting across a codebase without changing business logic or architecture. Use when lint warnings have piled up during rapid development, unused imports and variables clutter files, dead code paths were never removed, formatting is inconsistent, or static analysis tools report fixable hygiene issues.
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。
Author a new agent-almanac workflow — a self-contained `.mjs` orchestration script run by Claude Code's Workflow tool. Covers choosing a workflow over a team, copying the template, the triple-name discovery contract, the pure-literal meta and sidecar frontmatter, building the body with the injected primitives, the advisory/implementing capability contract, the adversarial-verification fail-safes, validation, and manual installation. Use when you have a repeatable, parameterized procedure that coordinates several agents and want it captured as a reusable, auditable artifact whose control flow is fixed in 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。
Create production-ready Grafana dashboards with reusable panels, template variables, annotations, and provisioning for version-controlled dashboard deployment. Use when creating visual representations of Prometheus, Loki, or other data source metrics, building operational dashboards for SRE teams, migrating from manual dashboard creation to version-controlled provisioning, or establishing executive-level SLO compliance reporting.
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。