758 件 ・ 関連度順
概要と使いどころ
Capture durable project or workflow knowledge discovered during work. Use when a mistake, hidden constraint, repeated question, or changed behavior reveals a documentation gap. To root-cause one specific mistake the agent just made and let the user pick fixes, use `learn` (`/learn`) instead.
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Refine a draft prompt before sending to a coding agent: decomposes goal/context/constraints, flags ambiguity, emits a model-agnostic rewrite. NOT for prompts from scratch (mk:brainstorming).
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 refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).
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。
Turn a raw idea into ONE honest recommendation: interview one question at a time until the problem, requirements, goals, non-goals, and constraints are CONFIRMED, then deliver a single verdict — do, don't, cheaper alternatives, trade-offs, ordered checklist, success metrics. Required: the user wants exactly ONE recommendation — not options, not a debate — and their framing is open to challenge (advise may answer a different question than the one asked). Triggers on "what should I do about X", "advise me on X", "am I approaching this right", "is this the right move". Do NOT use to be interrogated with no recommendation (→ mk:grill) — advise also interviews one question at a time, but only to reach a verdict. Do NOT use when the framing is settled and only the options are open (→ mk:brainstorming). Do NOT use to decide whether something is worth building (→ mk:office-hours). Do NOT use when you want several perspectives to argue it out (→ mk:party).
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 answering factual or explanatory questions about the current project — codebase, architecture, conventions, workflows, constraints — with minimal cited evidence. Triggers on "how does X work here", "what does X mean", "why is X structured this way", "is this claim true in the repo", "explain X with sources". NOT for brainstorming or comparing approaches (mk:brainstorming), trade-off decisions (mk:party), debugging/root cause (mk:investigate), code review (mk:review), plans (mk:plan-creator), security audits (mk:cso), visual explanations/diagrams (mk:preview --explain), test-coverage gaps (mk:nyquist), library docs (mk:docs-finder), or workflow navigation (mk:help).
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。
Safety constraints and quality gates for pipeline and workflow execution
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。
Clarify the brief BEFORE any design work to avoid building the wrong thing. Interrogates goal, user, constraints, success criteria, and scope, then writes a short context doc the other design skills consume. Use when the user says "let's design X", "build a UI for Y", "get context", "clarify the brief", or kicks off any product-design task without a clear spec. First stage of the Product Design set — highest leverage for reducing rework.
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 splitting a domain Skill into Phase-Aware File Structure (SKILL.md / design.md / constraints.md). Defines the splitting criteria, procedures, sync procedures, and validation rules. Invoked by /skill-format command or manually when restructuring domain Skills.
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。
Hypertree decomposition approach to planning problems for reduced computational complexity
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。
Wave-based parallel scheduling for DAG execution. Manages execution order, resource allocation, and parallelism constraints. Activate on 'schedule dag', 'execution waves', 'parallel scheduling', 'task queue', 'resource allocation'. NOT for building DAGs (use dag-graph-builder) or actual execution (use dag-parallel-executor).
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。
Analyze hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.
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 applying Cloudflare D1 migrations, fighting Supabase migration history vs SQL execution, choosing direct psql over CLI, designing idempotent migrations, debugging schema drift between local and remote, or recovering after a half-applied migration. Triggers: supabase migration repair instructions appearing, table missing after "applied" status, "Tenant or user not found" when running psql, --remote vs --local D1 confusion, NOT NULL on a populated column, foreign key constraint failures, drift between staging and prod schemas. NOT for Mongo/document migrations, ORM-managed migrations specifically (Prisma/Drizzle have their own conventions), or pure data backfills.
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。
Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.
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Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
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。
Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
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。
Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
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 and manage database migrations with reversible changes, proper naming conventions, and zero-downtime deployment strategies. Use this skill when creating database migration files, modifying schema, adding or removing tables/columns, managing indexes, or handling data migrations. Apply when working with migration files (e.g., db/migrate/, migrations/, alembic/, sequelize migrations), schema changes, database versioning, rollback implementations, or when you need to ensure backwards compatibility during deployments. Use for any task involving database structure changes, index creation, constraint modifications, or data transformation scripts.
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This skill determines whether a patient using a hybrid closed-loop insulin pump can continue this therapy upon hospital admission. It is triggered by questions such as "Can hybrid closed-loop pump be continued given hospital constraints?"
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。
Draft a Workflow Phase 1 intake document capturing a new request — the problem, the desired outcome, constraints, and testable acceptance criteria. Use when a user brings a new feature, change, or investigation that doesn't yet have a spec. The output lives at `docs/intake/<slug>.md` and feeds `/scout`, `/research`, and `/spec` downstream.
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Workflow Phase 2 — Codebase Scouting and Constraint Discovery. Maps the relevant slice of the codebase for a given task. Produces a scout report at `docs/scout/<slug>.md` naming the files, modules, and patterns the proposed work touches or constrains. Report decisions execute in main context with full conversation visibility; gathering MAY be delegated to read-only advisory subagents (seed.md §4.2-A).
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。
Continuously bridges DESIGN.md into the active planning phase. When the user is working on a UI phase, reads the project's DESIGN.md, extracts the relevant token slice, and appends it to findings.md under "## Design Context" so the agent sees design constraints in its attention window. Never edits DESIGN.md, never edits task_plan.md. Idles when DESIGN.md is absent.
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 for strategic thinking, or when the user must choose a direction under constraints: "where should we focus?", "how do we win?", "roadmap for next year", "what should we not do?". Ends in explicit bets and risks. Skip for step-by-step 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。
Generate project ideas via creative constraints.
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。
Plan multi-waypoint journeys with route optimization, feasibility analysis, and time budget constraints.
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。