「gate」の検索結果

Run the current session as a senior developer (Opus) leading a configurable junior fleet. Decomposes requests into scoped tasks, dispatches junior-developer subagents in parallel, optionally runs an architect plan-review gate before dispatch, autonomously resolves escalations, optionally dispatches a doc-writer second wave after juniors complete, then integrates and gates with /test + /governance. Use when the user says "/dev-team", "run the dev team", "delegate this to the juniors", "act as the senior dev", or wants a task built by a senior-led fleet.

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Run the Yuzu full governance pipeline on a commit range. Produces Gate 1 Change Summary, then orchestrates Gates 2-6 as parallel agent fan-outs with pre-written prompt preambles. Use when the user says "/governance <range>" or "full governance" or asks to run the multi-gate review pipeline on a PR/commit set.

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Run dialyzer over the Yuzu Erlang gateway. Sources the Erlang toolchain and runs rebar3 dialyzer. Use after any .erl change in gateway/ — CLAUDE.md mandates dialyzer be clean before commit because compilation success does not imply type correctness.

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Run dialyzer over the Yuzu Erlang gateway. Sources the Erlang toolchain and runs rebar3 dialyzer. Use after any .erl change in gateway/ — AGENTS.md mandates dialyzer be clean before commit because compilation success does not imply type correctness.

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。

High Availability control plane for Yuzu — the canonical entry point for any work on ADR-2002 (active-active server tier, gateway-fronted routing, Postgres-backed coordination seam, HA Postgres storage). Use when the user says "/ha", "/high-availability", "/ha-matrix", asks to plan or implement an HA workstream (WS-0…WS-14), asks "what's our HA gap" or "is it safe to run a second replica yet", asks to audit HA readiness, or starts work touching the coordination seam (leader election / fenced epochs / transactional outbox), gateway intra-cluster routing, shared `AgentRegistry` presence, PKI/CA HA, or the `deploy/docker/*ha*` / `scripts/ha/` surface. The skill bundles the two-axis model, the safe-to-scale gate, the delivery matrix (current-state + reviewers + status), the per-workstream workflow, and the load order for the routed reference docs.

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Use this skill to create, deactivate, freeze, or manage Salesforce users, assign user licenses and feature licenses, configure profiles and roles, set login hours and IP restrictions, and set up delegated administration. Triggers: 'add a new user in Salesforce', 'create a user and assign a licence', adding a new user, deactivating a departing employee, license assignment, freezing a user account, delegated admin setup. NOT for designing the permission set / PSG model — use admin/permission-set-architecture. NOT for who can see which records — use admin/sharing-and-visibility. Also covers: bulk user loads by CSV, Bulk API 2.0 upsert on Username, the UserLogin freeze object, licence and permission-set-licence reclamation, Role metadata deployment, and the offboarding checklist as SOQL.

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Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.

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Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

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Design an Azure AI gateway layer for centralized model routing, policy enforcement, and operational control. Use this skill when users ask about centralized AI traffic management, model governance, rate limiting, or gateway patterns.

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Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to implementation-loop with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to implementation-loop. Requires implementation-loop and an authenticated backend CLI to execute.

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Delegate implementation work to a dedicated implementer subagent, then review its diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants a separate model to write code, mentions handing off / delegating implementation, asks to work through a plan or spec unit-by-unit with a subagent doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer self-reports success, must not run the full test suite by default, must not touch git, and its report is a claim rather than evidence. Cursor port of implementation-loop. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the cursor-engineering-mode skill (it ships in this same plugin).

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Delegate implementation to Codex, grok, or cursor-agent, then review the diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants Codex, the grok 后端, or cursor-agent to write code, mentions handing off / delegating implementation to one of those backends, asks to work through a plan or spec unit-by-unit with an implementation backend doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer runs in the real environment behind backend-specific git and publication boundaries, must not be pointed at a full test suite by default, and its self-report is a claim rather than evidence. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the engineering-mode skill when it is installed.

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。

Own goal-first engineering outcomes: investigate, find root cause, design, plan, then delegate implementation unit-by-unit to cursor-implementation-loop, this plugin's kernel skill, with verification and honest reporting. Use to fix, build, or own an outcome end to end; investigate-and-fix; "make X work"; or plan-then-execute in one request. A request with an approved plan or equivalently precise spec that needs nothing beyond implementation belongs directly to cursor-implementation-loop. Requires this plugin's cursor-implementation-loop skill; they ship together.

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Pre-ticket Fibonacci sizing for a paste-mode batch of user stories. Default mode is advisory-only (writes a Story Sizing Report). Opt-in `--auto-create` delegates ticket creation to mk:jira-issue + mk:jira-collaborate with mandatory dry-run + single batch confirmation gate. NOT for single-ticket estimation (mk:jira-estimator); NOT for sprint capacity (mk:planning-engine).

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Read-first Figma gateway via Figma MCP: analyze designs, implement Figma-to-code for 1–3 screens, extract design tokens, produce a Figma Evidence Packet for planning handoff, screenshot fallback. Advanced operations (Code Connect, canvas writes, design-system/library patterns) are gated references loaded only on explicit intent. Triggers: 'figma', 'design link', 'implement this design', 'design tokens', 'produce a Figma Evidence Packet'.

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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).

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Shared in-workflow quality gates for code/PR skills — the gate model, trust tiers, and the three primitives (evidence-before-done, independent judge, anti-gaming). DO NOT TRIGGER directly — referenced by consumer skills via relative path.

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Use when the user asks for a phase-gated refactoring or redesign loop - session-blind workers map and trace the area, empirical probes precede the design, an approval gate guards implementation, and a conformance audit re-checks the branch after any design change. State persists in .episteme/DESIGN-<slug>.md. Requires the refactor agent roles registered by scripts/install-codex.sh. Keywords: refactor loop, redesign, design gate, enumerate before design, conformance audit.

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Run before building anything new — a pre-build gate that decides whether an idea deserves its own tool, judged from both the agent lens (legible, drivable, automatable) and the human lens (will I touch it, do I want to maintain it). Trigger on "should I build X", "starting a new project / app / tool / repo", "is this worth making", "want to spin up", or any new-build intent — even if the word "checklist" never appears. Outputs a backed GATE.md with checkmarks and a one-line verdict note in the project root docs. Do NOT load for small config tweaks, one-off scripts, bug fixes on existing things, or anything that is clearly a feature of something you already run.

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Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, co...

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Spearphishing targets specific individuals using personalized, researched content that bypasses generic spam filters. Email security gateways (SEGs) like Microsoft Defender for Office 365, Proofpoint,

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I/O + 계산 혼재 또는 Mock stub + 계산 혼재 메서드를 Functional Core / Imperative Shell(빵속빵, Impure-Pure-Impure Sandwich)로 분리. "순수 함수 분리", "I/O와 계산 나눠", "mock 없이 테스트하게", "functional core", "/segregate-functional-core" 요청 시 사용. 단, 값 반환과 부수효과 분리(CQS)만이면 /separate-query-modifier가 적합. /segregate-functional-core [commit-ref]로 호출.

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Runs twice in a PFD to P&ID job. Part 1, before gate G3, validates the discipline proposals for provenance, UNKNOWN handling, conflicts, prohibited content and cross-section consistency and returns results and blocking items. Part 2, before gate G5, assembles the review package, with job header, gate record, every register and proposal, consolidated UNKNOWN list, feedback log and closing statement. Use when the user asks to "validate the enrichment", "run validation before G3", "assemble the review package" or "build the G5 package". Do not use for the readiness map, use pid-tool-readiness-mapping instead; do not use to produce a missing discipline section, use the discipline skill that owns it. Drafts for human review; never approves, authorises or signs off.

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GitHub Actions の CI 失敗を調査するスキル。失敗した run / PR / job の URL（例: https://github.com/OWNER/REPO/actions/runs/RUNID、…/pull/PR、…/job/JOBID）や run_id・PR 番号を貼られて「このCIが失敗している、原因を調査して」「ビルド/テストがコケた、なぜ落ちたか調べて」「workflow が failed、修正案を出して」「job のログを見て」と頼まれたら必ず使う。WebFetch では github.com のログは取れない（PreToolUse hook が差し戻し、HTML しか返らない）ため、gh（`gh run view --log-failed` / `gh run view --job` / `gh pr checks` / `gh api`）で失敗ジョブ・ステップと失敗ステップのログだけを決定論スクリプトで収集し、失敗原因を特定して修正案を提示する。GitHub Actions 以外（GitLab CI 等）や、URL がログではなくソース閲覧目的のときは対象外。GitHub(gh) 前提。`/gh-ci-investigate` で明示起動も可。