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Reviews a GitHub PR with a single shallow correctness/security/breaking/AI-slop checklist and emits a verdict; optionally posts it via gh pr review. Use to review an external or teammate PR. NOT for deep pre-landing audit of your own diff (see mk:review); NOT for responding to received review comments (see mk:respond-pr).

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Bulk JIRA operations on 10+ issues via the jira-as wrapper: bulk transition / assign / set-priority / clone / delete. Dry-run is MANDATORY first. Triggers: 'bulk update N issues', 'mass transition', 'transition 50+ issues to Done', 'bulk-clone', 'bulk-delete'. NOT for single-issue ops (mk:jira-issue / mk:jira-lifecycle).

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JIRA search + filter management via the jira-as wrapper. Triggers: 'search jira', 'find issues where X', 'jql for ...', 'export search results', 'manage saved filters'. JQL injection-safe via the shared sanitizer. NOT for single-issue CRUD (mk:jira-issue); NOT for bulk write ops (mk:jira-bulk).

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Heuristic story-point estimation for a single Jira ticket via the jira-estimator agent. Read-only. Triggers: 'estimate KEY', 'story points for KEY', 'how complex is KEY'. NOT for complexity-only analysis (mk:jira-evaluator); NOT for full RCA (mk:jira-analyst).

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Pack an EXTERNAL repository into a single AI-friendly file (markdown/xml/json). Use for third-party library analysis, security audits, or handoff to external LLMs. Do NOT use to pack the current project for inbound context — the host runtime already reads files lazily. Triggers: 'pack this repo', 'snapshot of X', 'export codebase', 'repomix'.

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Confluence CQL search + space list + saved-filter + export via the confluence-as wrapper. Triggers: 'search confluence', 'find pages where X', 'cql for ...', 'export search results', 'list spaces', 'manage saved filters'. CQL injection-safe via the shared sanitizer. NOT for single-page CRUD (mk:confluence-page); NOT for bulk write ops (mk:confluence-bulk).

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Bulk Confluence operations on 10+ pages via the confluence-as wrapper: bulk-label / bulk-move / bulk-delete. Dry-run is MANDATORY first. Triggers: 'bulk update N pages', 'mass label', 'delete all pages matching CQL', 'bulk-move pages to PARENT'. NOT for single-page ops (mk:confluence-page); NOT for comment/attachment ops (mk:confluence-collaborate).

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Detects task agent and complexity tier at Phase 0 orient. Invoked first in every workflow. Provider adapters select any model/runtime policy. NOT for runtime agent routing inside a single skill (see orchestration-rules.md).

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Use when running an autonomous multi-hour build of a green-field product — orchestrates planner → contract → generator ⇄ evaluator loop with adaptive scaffolding density per model tier. Triggers on /mk:autobuild, "build me a kanban app", "build a retro game maker", "autonomous build", or any green-field product spec. NOT for scoped single-task work (see mk:cook); NOT for initial project scaffolding only (see mk:bootstrap).

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Use when creating a new project from scratch — orchestrates the full journey from idea to running code. Research → design → plan → scaffold → implement → docs. Explicit invocation only — never auto-activates. CLI = project infrastructure (.claude/), bootstrap = application code + full pipeline. NOT for autonomous multi-hour builds of specified products (see mk:autobuild); NOT for single-task feature work on existing projects (see mk:cook).

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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).

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Spin up a coordinated multi-agent swarm (hierarchical or mesh topology) to work a complex, multi-file task on a ruOS desktop. Use when a task needs 3+ agents working in parallel with anti-drift coordination; skip for single-file edits.

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Generate multiple angles and shot types of a single product for ecommerce-ready image sets. Produces a coherent set (hero 3/4, front, side, back, detail macro, scale, packaging, in-hand) keeping product identity consistent across shots. Use when the user says "different angles", "product shots", "ecommerce image set", "shot list", "PDP gallery", or needs a multi-view set of one product. Part of the Creative Production set.

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Reflect subskill for simmer. Records iteration results in trajectory table, tracks best candidate, handles regression rollback, and passes ASI forward to the next round. Supports both single-file and workspace modes. Do not invoke directly — called by simmer orchestrator after each judge round.

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Judge board subskill for simmer. Dispatches a panel of judges with different lenses, runs one deliberation round where they challenge each other's scores, then synthesizes consensus scores + single ASI. Drop-in replacement for simmer-judge that produces identical output format. Do not invoke directly — dispatched by the simmer orchestrator when JUDGE_MODE is board.

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Generator subskill for simmer. Produces an improved version of the artifact based on the judge's ASI feedback. Handles both single-file and workspace targets. Do not invoke directly — dispatched as a subagent by the simmer orchestrator.

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Use when user says "simmer this", "refine this", "hone this", "iterate on this", or asks to improve a specific artifact over multiple rounds. Runs an iterative refinement loop with investigation-first judges that read the code, understand the problem, and propose evidence-based improvements. Auto-selects single judge or multi-judge board based on complexity. Works on any artifact type: documents, prompts, specs, emails, creative writing, API designs, pipelines, codebases. Supports multi-file workspace targets, runnable evaluators, and open-ended optimization (model selection, pipeline topology, prompt tuning).

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Research complex questions and synthesize relevant evidence into a clear, cited answer. Useful for competitive and market analysis, company research, prospect discovery, enrichment, historical comparisons, and evaluating conflicting claims. Adapt the depth and format to the question; use when a focused search or single-page read is insufficient.

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Migrates React projects and components from Radix UI to Base UI. Use when asked to migrate from radix, move to base-ui, convert radix primitives, or switch a shadcn project's base library. Handles single components ("migrate accordion") and whole projects.

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Resolve a verified cluster of Rembric issues from causal root to evidence-gated closure. Use after systemic triage identifies a shared root, or when the user asks to attack issues at the root, create a mechanism map, plan a deletion-first fix, or close a fixed/stale issue cluster. Use `diagnose` for a single defect whose reproduction is not yet established.

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The Rembric TUI installer contract — the single, canonical install/setup/upgrade/uninstall path (`apps/plugin/install.sh`, fronted by the repo-root `install.sh` shim). Apply when touching `install.sh`, the root shim, any per-client `install.sh`/`uninstall.sh`, `marketplace.json`, or distribution docs (README, `docs/agents.md`, plugin/client READMEs). Covers the orchestrator model, what must not break, and where the source of truth lives. For running the validation suite, use `rembric-tui-installer-e2e`.

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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.

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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.

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。