「erd」の検索結果

Use when a task needs the judgment of a livestock geneticist/animal breeder — selecting a sire against EPDs or genomic data, screening a proposed mating for inbreeding risk, deciding AI versus natural service, timing an estrus-synchronization protocol, or defending a breeding-goal tradeoff to a herd owner.

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 a task needs the judgment of an Area, Ethnic, or Cultural Studies Professor — designing a joint-appointment course load across two departments, prepping a tenure or mid-tenure review file for interdisciplinary scholarship, deciding whether a course survives an enrollment or political-pressure threat, evaluating community-engaged research for a merit case, or writing a Title VI/FLAS-adjacent grant narrative.

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 a task needs the judgment of a Farmer, Rancher, or other Agricultural Manager — deciding a planting/breeding mix or acreage shift under price and weather uncertainty, sizing a hedge or crop-insurance coverage level, running break-even and diversification scenarios before a capital or input commitment, or evaluating land/herd management practices against long-term productive capacity.

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Verify that an application works correctly using `stably verify`. Use when an AI agent has made code changes and needs to validate the feature works in a real browser. The command describes expected behavior in plain English and reports a PASS/FAIL/INCONCLUSIVE verdict — no test files generated. Triggers on: "verify this works", "stably verify", "check if this works", "validate my changes", "verify my feature", "does this work", "check the app", "verify the feature".

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。

Pre-publish (or retroactive) content-quality gate implementing Google's official self-assessment questions from the creating-helpful-content doc, plus 2024 spam-policy red flags. Verdict: PUBLISH / REVISE / RETHINK. Use when user says "helpful content", "people-first", "content self-assessment", "is my content helpful", or "content quality gate".

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Generate Mermaid architecture diagrams from your codebase. Produces C4 context and container diagrams, sequence diagrams for key flows, ER diagrams from database schemas, and module dependency graphs. Use when you need architecture diagrams, system diagrams, database ERD, sequence diagrams, dependency visualization, or C4 model diagrams.

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Autonomous requirements analysis agent. Parses a spec file (SPEC.md, requirements doc, or any plain-text specification) and runs a structured pass to surface logical contradictions, ambiguous requirements, and underdefined gaps — before any implementation begins.

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Analyze game code for performance bottlenecks including draw call batching and overdraw, shader complexity and LOD strategy, per-frame GC allocation pressure, object pooling gaps, physics timestep and collision matrix tuning, spatial partitioning for entity queries.

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Create and edit draw.io/diagrams.net diagrams as editable `.drawio` files. Covers architecture, UML/ERD/sequence, BPMN, network, and swimlane views authored from a description or converted from code, IaC, SQL, and API schemas, plus sync, query, test, review, export, and publish of existing diagrams. Use when the user asks for draw.io/diagrams.net or an editable diagram; prefer Mermaid/PlantUML when diagrams-as-code is enough.

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Run an adversarial two-phase code review of a change with TWO independent reviewers — Claude and Codex — who review alone, then cross-examine each other's findings, then Claude synthesizes a single weighted verdict. Use when the user says "/adversarial-review", "adversarial review", "review this with Codex", "get Codex to review", "two-reviewer review", "cross-examine this PR", or wants a second independent model to grade a change before merge.

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Review a Yuzu pull request end to end with a static-first trust gate, isolated dynamic testing, adversarial Kimi/Codex review, a posted verdict, and head-SHA-bound re-review. Use when the user types forms such as "go PR123", "go 123", or "/go PR123"; use `--promote SHA` only after a collaborator accepts the static review of an external fork.

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Diagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).

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Diagnoses GKE nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't use for pod-level application failures (use gke-workload-troubleshooting), autoscaler scale-up/scale-down decisions (use gke-cluster-autoscaler), or non-GKE compute.

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TechLead (Senior Technical Lead) agent. Reads all BA artifacts and produces system architecture, technology stack, ERD, sequence diagrams, and ADRs. Declares Gate 1: Design Freeze. Part of the Virtual Team Skill pipeline.

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Phase 7 of the SRS workflow. Runs srs_validator.py --dir on the generated SRS files, reports errors and warnings, auto-fixes ERRORs, and outputs a COMPLIANT / PARTIALLY COMPLIANT / NON-COMPLIANT verdict.

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Phase 7 of the SRS workflow. Runs srs_validator.py --dir on the generated SRS files, reports errors and warnings, auto-fixes ERRORs, and outputs a COMPLIANT / PARTIALLY COMPLIANT / NON-COMPLIANT verdict.

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 a BA or admin needs to document the Salesforce data model: creating field inventories, object relationship maps, ER diagrams, or analyzing field usage across objects. Triggers: 'data dictionary', 'document our data model', 'object relationship map', 'field inventory', 'ER diagram for Salesforce'. NOT for designing the model or choosing lookup vs master-detail — use data/data-model-design-patterns. NOT for generating the Mermaid or PlantUML diagram — use architect/salesforce-erd-and-diagramming. More trigger keywords: field inventory CSV, describe API, getGlobalDescribe, DescribeFieldResult, sObject describe, schema snapshot, schema drift, blank field descriptions, undocumented fields, field ownership, data dictionary review cadence.

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, mock up, build and review terminal UIs — full-screen TUIs, interactive CLIs and pickers, and plugin panels or popups inside a host (herdr, tmux, Neovim, k9s, lazygit). Use when designing, reviewing (from code, a live capture or a screenshot) or redesigning a TUI's layout, keybindings, states or colors, including requests that name a known TUI (lazygit, k9s, btop, yazi, helix, fzf); writing terminal mockups; building or fixing one in Ink, Textual, Bubble Tea, Ratatui or gum; mapping a color scheme (Catppuccin, Dracula, Nord, Gruvbox, Tokyo Night, Rosé Pine…) onto a TUI's theme; fixing misaligned glyphs, flicker, NO_COLOR, exit codes, signals, pipes or a broken terminal after exit; suspend or $EDITOR handoff; or profiling a slow TUI. Not for web or native GUIs, terminal-emulator, editor or shell-prompt themes.

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Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.

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Adaptive learning for moflo agents via ReasoningBank: trajectory storage, verdict judgment, memory distillation, consolidation, and MMR retrieval. Use when building agents that should improve from experience across runs.

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Verify-before-done — exercise the current change end-to-end against its acceptance criteria (the SDD plan's, or the ticket's) and report a per-criterion PASS/FAIL verdict, then record the outcome to memory. This is the concrete action that satisfies moflo's verify-before-done gate (`gates.verify_before_done` / `/flo -v` / `/flo -sd`). Use before `gh pr create` when the change must be proven to work, not just tested in passing. Verifies only — it does not fix; on FAIL it reports the gaps and stops.

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MUST USE for ANY work on .py .pyi .rs .ts .tsx .mts .cts .go files. One philosophy: strict types, modern stacks (Pydantic v2 / serde+thiserror / Zod / gin+sqlc+pgx+slog), modern toolchains (uv+basedpyright+ruff / cargo+clippy+miri / Bun+Biome+tsc / gofumpt+golangci-lint v2+nilaway+go-race), parse-don't-validate, exhaustive match, typed errors, no any/unwrap/panic, 250 LOC ceiling, TDD. Routes to references/{python,rust,typescript,rust-ub,go}/. Triggers: write/edit Python/Rust/TypeScript/Go code, new project, gin server, bubbletea TUI, CJK IME, connect-go RPC, sqlc pgx, branded ids, exhaustive match, unsafe Rust, miri, oversized file, refactor, TDD, e2e test, arena, allocator, bumpalo, const fn, const generics, comptime, zero-alloc, bitfield, repr, scopeguard, errdefer, Zig-like, zerocopy, packed struct.

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Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.

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Adversarial deliberation process for making decisions. Spawns advocate agents for each option who argue their cases before a judge (you) who renders a verdict.

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。