Makes technical debt visible and decidable — distinguishing real debt from mess, quantifying its cost, and arguing for remediation in business terms. Use this to assess and prioritize debt, decide whether to fix or live with something, justify remediation work to non-engineers, or plan a migration off a system nobody wants to touch.
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。
Debt is a deliberate trade: taking on future cost to move faster now. Most of what gets called debt is not that — it is mess, which was never a decision, or drift, where the world moved and the code did not. The distinction matters because the arguments and remedies differ.
Debt matters proportional to how often you pay it. Ugly code in a module nobody has touched in three years costs nothing; a moderate awkwardness in the file every feature crosses costs continuously.
Measure by contact: change frequency, how long changes there take relative to elsewhere, how often changes there cause incidents, and how many people avoid the area. Overlay change frequency on complexity and the priorities become obvious and defensible — the expensive parts are where both are high, which is rarely where intuition points.
"The code is bad" loses to any feature request. What wins is the rate: this area consumes a disproportionate share of delivery time, causes a disproportionate share of incidents, and the gap widens.
Frame remediation as capacity recovery with a payback period — the same terms as
finance:capital-allocation, which is where any large migration will eventually be judged.
Large rewrites fail at a well-documented rate: they take longer than estimated, deliver no value until the end, and are canceled halfway leaving two systems. Prefer strangling the old system gradually behind a stable interface, so value lands continuously and the work can stop at any point without leaving a mess.
Improve opportunistically where you are already working — the code you are touching anyway is the cheapest code to improve, and it is by definition the code that is being touched.
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.
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。