Ships changes safely and often — pipelines, deployment strategies, feature flags, rollback, and database changes. Use this to design a deployment pipeline, reduce release risk, roll out a risky change gradually, plan a schema migration, or work out why releases are infrequent and frightening.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Release risk is dominated by batch size. Large infrequent releases are dangerous because many changes land at once and nobody can tell which one broke it — so teams release less often, which makes each release larger. The loop is the problem.
Deploying code and exposing behavior to users are different acts, and coupling them forces every deployment to be a business decision.
Decouple with flags: deploy continuously, expose deliberately. This makes rollback a configuration change rather than a redeployment, which is the difference between seconds and minutes at the worst possible time.
Flags are inventory and rot. Give each an owner and a removal date; a codebase full of stale flags has combinatorial states nobody has tested.
Automate everything between commit and production, and let the pipeline reject. Manual steps get skipped under pressure, which is exactly when they matter.
Order gates fast-to-slow so failure is cheap: lint and unit tests, then integration, then anything requiring a deployed environment. A pipeline slow enough to be circumvented is worse than a fast one with fewer checks, because it will be circumvented.
Build once and promote the same artifact through environments. Rebuilding per environment means the thing you tested is not the thing you shipped.
Expose to a small population first and watch real signals before widening. Canary or percentage rollout turns a total failure into a contained one.
Define the abort condition before starting, with a threshold and a named decision-maker. Under pressure, and with the change fresh, the instinct is always to wait a little longer and see.
Code rolls back; data does not. Make schema changes backward-compatible and multi-step: add the new structure, write to both, migrate, switch reads, then remove the old — with the application tolerant of both shapes throughout.
Test the migration against production-scale data. A migration that is instant on a development dataset can lock a large table for a length of time nobody modeled.
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
Pipelines: GitHub Actions, GitLab CI, CircleCI, Buildkite, Jenkins, and similar.
Continuous delivery and progressive rollout: Argo CD, Flux, Spinnaker, and similar; feature flags for decoupling deploy from release — LaunchDarkly, Unleash, Split, and similar.
Schema migrations: Flyway, Liquibase, Alembic, and similar. Whichever you use, the property that matters is that migrations are versioned, ordered, and applied by the pipeline rather than by a person with a database client.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
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。
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
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。
Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
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。
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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。
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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。
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
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。