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概要と使いどころ
Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.
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。
Position an AI product when everyone claims AI and skeptics call it "just a wrapper." Find the real wedge (data, workflow, trust, domain), make reliability the differentiator, answer "won't the big labs just build this," and stop leading with "AI-powered." Use when your AI or dev tool blends into a sea of similar demos, buyers doubt the accuracy, or you can't say why you win when the model is a commodity.
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。
Define a real ICP and the developer personas in the sale. Use when the user says the product is "for developers," can't name who would say no, or is marketing to whoever holds the budget instead of who actually adopts.
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。
Break a work-items.md file (produced by /plan-work-items) into independently-grabbable GitHub issues, one per slice, in each slice's target repo. Use when you want to turn a work-items file into GitHub issues, publish work items as issue tickets, or create implementation tickets that can be worked on and tracked on GitHub. Does not produce the work-items file itself — use plan-work-items to break a plan into work items first. Does not review code or post pull request comments — use post-code-review-to-pr for that.
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。
Creates and maintains project documentation for features, systems, and components. Use when documenting how a feature, system, or component works — including writing, updating, or organizing docs. Does not scan or detect the project's technology stack — use project-discovery for repository analysis and config detection. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not create or update coding standards — use coding-standard instead. Does not generate PR descriptions — use update-pr-description for that. Does not produce runbooks for operational scenarios — use runbook for that. Does not rewrite existing prose for readability — use edit-for-readability for that. Does not produce an ephemeral, understand-now overview of code or a PR — use code-overview for that.
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。
Surfaces Han's shared Human-Readable Output Standard — the readability rule and the writing-voice profile — into the calling skill's own context, so the caller drafts in voice and runs its self-check against the current standard sourced from one canonical copy. Use when a prose-producing skill needs the shared readability standard available in context before it drafts. Governs the shape of a written deliverable, where explanation-guidance governs what a run says to a person in a turn. Runs in the caller's context and hands control straight back; it does not produce a deliverable of its own, rewrite anything, or judge the caller's work. Does not run the adversarial rewrite pass — dispatch the readability-editor agent for that, or use edit-for-readability to rewrite an existing target. Does not cover explaining technical work to a reader who will not implement it — use explanation-guidance for that.
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。
Produce a plain-language manual test plan from the context supplied to it — an executive summary, a high-level list of named tests, and a detail section per test with the steps a person follows by hand and the outcomes they should expect. Use when you want to create, draft, generate, or outline a manual test plan, manual QA steps, hands-on verification steps, or an acceptance walkthrough for a feature, change, branch, plan, or PR. When the plan holds more than five tests and at least two natural categories emerge, both the test list and the detail sections are organized under plain-language categories. When nothing in the supplied context can be manually tested, it says so and asks for more context instead of producing a document. Does not analyze code for automated test coverage gaps — use automated-test-planning. Does not write test code — use tdd. Does not review code quality — use code-review. Does not stress-test an existing plan — use iterative-plan-review.
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。
Discovers the core attributes of the current code repository and its projects — languages, frameworks, tooling, and where things live — and writes a concise reference section directly into the project's AGENTS.md or CLAUDE.md for other skills, agents, and hooks to consume. Use when scanning, analyzing, or detecting the project's technology stack, build tools, or repository structure. Does not create or update project documentation — use project-documentation for writing feature or system docs.
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。
Break a work-items.md file (produced by /plan-work-items) into independently-grabbable Jira tickets, one per slice, in a single Jira project. Use when you want to turn a work-items file into Jira tickets, publish work items as Jira issues, or create implementation tickets that can be worked on and tracked in Jira. Requires a configured Atlassian MCP server. Does not produce the work-items file itself — use plan-work-items to break a plan into work items first. Does not post to GitHub — use work-items-to-issues for GitHub issues.
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 the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.
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 a human-readable, progressive-disclosure overview of unfamiliar code or a pull request's changes — why it exists (the real problem it solves or goal it serves for the business or a user), and from there what it does, how it flows, and where to start — so you can get up to speed before working on or reviewing it. Use when you want to understand, get oriented in, make sense of, explain, or get up to speed on a chunk of code, a file, a directory, a symbol, or a PR's changes. Writes the overview to a scratch file and changes no code. Does not review code quality or raise findings — use code-review for auditing changes or post-code-review-to-pr for posting them. Does not produce durable feature or system documentation — use project-documentation. Does not assess architecture or structural risk — use architectural-analysis. Does not diagnose bugs or root-cause failures — use investigate. Does not pace a person through the code one step at a time in conversation — use code-walkthrough.
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。
Cut a Han release: update CHANGELOG.md with the changes since the last release, bump and tag every plugin that changed as {plugin-name}--v{version} so a version-constrained dependency can resolve, and publish a GitHub release crediting every merged pull request and closed issue to the people behind it. Use when releasing, cutting a release, shipping a new Han version, publishing release notes, or tagging a version. Always stops for approval before creating any tag, because a pushed tag is never moved. Requires the gh CLI, jq, the claude CLI, and a clean git checkout. This is a repository-maintenance skill for the Han repo itself, not a general review or PR skill — use code-review for local review, post-code-review-to-pr to post a PR review, and update-pr-description for PR bodies.
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。
Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.
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。
Guides the Art. 36 prior consultation process when a DPIA indicates high residual risk that cannot be mitigated. Covers required documentation per Art. 36(3), the 8-week DPA response timeline, outcome management, and interaction protocols with supervisory authorities. Keywords: prior consultation, Art. 36, supervisory authority, DPA, high residual risk, DPIA escalation, consultation documentation.
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。
Conducts retention impact assessments for new processing activities to determine appropriate data retention periods. Covers regulatory requirements scanning, proportionality review, purpose-based retention determination, and retention period documentation aligned with GDPR Article 5(1)(e) and Article 25 data protection by design. Activate for retention assessment, new processing retention, retention period determination queries.
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。
Conducts Data Protection Impact Assessments for biometric identification and authentication systems under GDPR Article 35 and Article 9 special category rules. Covers facial recognition, fingerprint, iris scanning, voice recognition, and behavioural biometrics. Applies EDPB Guidelines 3/2019, CNIL Reglement Type Biometrie, and ISO/IEC 24745 biometric template protection. Keywords: DPIA biometric, facial recognition, fingerprint, Art. 35, Art. 9, biometric template, special category.
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 biometric data processing for employee timekeeping and access control under Art. 9 GDPR special category rules. Covers fingerprint, facial recognition, iris scanning, and voice recognition. Applies necessity tests, evaluates less intrusive alternatives, and implements employee objection procedures. Keywords: biometric data, Art. 9, fingerprint, facial recognition, access control, timekeeping, special category.
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。
Implementation guide for ePrivacy Directive compliant double opt-in email consent. Covers confirmation email workflow design, token expiration handling, record-keeping requirements, suppression list management, and integration with CAN-SPAM Act and CASL requirements for multi-jurisdiction compliance.
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。
Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
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。
Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.
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。
Run protected AAS maintainer sweeps, PR merge batches, canonical sync, Core preview checks, and scripted releases. Use for repository maintenance, main alignment, CLI/MCP/Workbench changes, or release work; not ordinary contribution tasks.
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 spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.
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 service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
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。
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
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。