1,927 件 ・ 関連度順
概要と使いどころ
Analyzes harness trace logs to surface recurring failure patterns and suggest harness improvements. Scatter-gather across last N runs with a mandatory HITL gate before any change is applied.
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。
Systematic root-cause debugging that produces a diagnostic report (mk:fix applies the fix; mk:build-fix owns compile failures). Use for 'debug this', 'why is this broken', 'root cause'.
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。
Diagnoses and fixes bugs, type/lint errors, CI failures, and runtime errors, root-cause first. NOT for investigation without a fix (mk:investigate) or build-only compile errors (mk:build-fix).
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。
Resolve an in-progress git merge or rebase conflict end to end: read the conflict state, recover each side's original intent, resolve every hunk, run the project's checks, and prepare the merge/rebase for an explicitly approved finish. Use when asked to "resolve merge conflicts", "fix this rebase", "finish the merge", or when `git status` shows conflicted (unmerged) paths. NOT for the full ship pipeline that merges the base branch first (see mk:ship); NOT for debugging a runtime bug or test failure unrelated to a conflict (see mk:investigate / mk:fix).
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 and implement backend service changes: API contracts, service boundaries, auth integration, reliability and delivery checks. Use for backend/API handlers, services, webhooks, jobs, or backend architecture changes. Routes schema/migrations to mk:database; security review to the existing security workflow.
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。
Systematic debugging with root cause investigation. Produces a diagnostic report; mk:fix owns remediation and mk:build-fix owns compile/build failures. Use when asked to "debug this", "fix this bug", "why is this broken", "investigate this error", or "root cause analysis". Proactively suggest when the user reports errors, unexpected behavior, or is troubleshooting why something stopped working. NOT for applying fixes without investigation (see mk:fix); NOT for step-by-step evidence-based reasoning (see mk:sequential-thinking).
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。
Diagnoses and fixes bugs, type errors, lint failures, CI/CD issues, and runtime errors via root-cause-first investigation. Use for defect remediation. NOT for investigation without a fix (see mk:investigate); NOT for build-only compilation errors (see mk:build-fix).
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。
DraftForge PR lifecycle workflow. Use when the user runs /pr, opens or pushes a pull request, or asks to monitor CI checks and Copilot review on a branch. Creates the PR (or reuses an existing one), polls GitHub Actions and Copilot up to ~15 min, then analyses CI failures and Copilot comments and reports findings grouped by file and skill.
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 encountering any bug, test failure, or unexpected behavior. Enforces a strict reproduce-first, root-cause-first, failing-test-first debugging workflow before fixing.
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。
YAML-based DAG workflow engine with topological execution and failure strategies
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。
Find UX pains and opportunities fast through competitor scans, pattern research, and web search. Surfaces how others solve the same flow, common failure modes, and conventions users already expect. Use when the user says "research this UX", "how do others do X", "competitor scan", "find UX patterns", "what's the convention for Y", or before ideating a flow. Part of the Product Design set. Uses web search/fetch tools.
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 encountering any bug, test failure, or unexpected behavior, before proposing fixes
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。
Safely localize unknown Unity games, especially Japanese-to-Chinese projects. Use to discover Unity version/layout/backend, migrate translations across game versions, extract occurrence-mapped text from serialized assets, embedded CSV, bytecode, UI, Localization StringTables, Utage, or middleware, bridge rows through AiNiee-style translation, validate and transactionally write approved candidates, audit residual and runtime-only text or AutoTranslator diffs, diagnose TMP fonts and Addressables/IL2CPP failures, locate saves, or acceptance-test SHA-verified patches.
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。
Applies insights from MetaGPT paper on how structured communication, role specialization, and SOPs prevent coordination failures in multi-agent LLM systems
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 deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.
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 introducing a feature flag system, choosing between release flags / experiment flags / operational flags / kill switches / permission flags, designing percentage rollouts, building kill-switch + circuit-breaker patterns, structuring evaluation contexts (user IDs, plan tier, region), or building flag-cleanup discipline. Triggers: LaunchDarkly / GrowthBook / Unleash / Flagsmith / OpenFeature, percentage ramp, sticky bucketing, kill switch wired to alerting, "we still have flags from 2 years ago", flag debt, OpenFeature provider, evaluation context, default value on provider failure. NOT for A/B test statistical analysis (separate skill), traffic-split routing at the edge, build-time bundler defines, or environment-based config.
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。
### 1. The Fault/Error/Failure Hierarchy NOT for unrelated tasks outside this domain.
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。
Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.
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。
Modifies DAG structure during execution in response to failures, new requirements, or runtime discoveries. Supports node insertion, removal, and dependency rewiring. Activate on 'replan dag', 'modify workflow', 'add node', 'remove node', 'dynamic modification'. NOT for initial DAG building (use dag-graph-builder) or scheduling (use dag-task-scheduler).
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。
Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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。
Choose multi-agent architectures using environment-first analysis, coordination pressure, commitment tuning, and knowledge levels. Use for autonomy design, protocol choice, and coordination failures. NOT for single-agent planning, centralized schedulers, or prompt-only swarms.
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。
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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。
Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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。