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概要と使いどころ
Testing patterns for FastAPI with pytest-asyncio, httpx AsyncClient, fixtures, and test data factories. Use when writing tests, setting up test infrastructure, or improving coverage in a FastAPI project.
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Testing patterns for Micronaut/Kotlin backend including repository tests, integration tests, and test data builders. Use when writing tests, setting up test infrastructure, or improving coverage.
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。
Skill to query and update a mistake notebook to memorize mistake. Use this skill when: 1. Encountered a severe problem, needs to recall for historical problem solving experience 2. After fixed a severe mistake with a clear solution valuable to be recorded 3. User requested 'Do not make this mistake again'
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。
Show images in terminal using the Kitty image protocol. TRIGGER when show/view images in terminal, display plot results, or after saving any plot or image file to disk.
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 customer success and retention expertise combining onboarding, health scoring, churn prevention, retention strategies, and LTV maximization. Use when designing customer onboarding flows, reducing churn, building health scores, creating retention systems, designing upsells, or improving customer lifetime value. Covers proactive engagement, cancel flows, dunning, re-engagement, and the full customer lifecycle.
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。
LinkedIn profile optimization and content creation. Use when auditing or optimizing a LinkedIn profile, writing LinkedIn posts, developing thought leadership content, building personal brand on LinkedIn, analyzing SSI score, improving profile visibility, or crafting hooks and engagement strategy for LinkedIn.
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。
Humanize AI-sounding writing and remove common AI-generated writing tells while preserving meaning, audience, and brand voice. Use when editing text to sound more natural, less generic, less promotional, less formulaic, or closer to the user's own writing. Triggers: humanize this, make this less AI, make this sound human, remove AI writing patterns, rewrite in my voice, make this more natural, de-AI this, make this less ChatGPT, fix robotic writing, improve authenticity, reduce generic AI tone.
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。
Expert framework for assessing and achieving product-market fit. Combines PMF measurement methodologies, Sean Ellis survey, retention analysis, segment-specific PMF, and post-PMF scaling strategy. Use when determining if product has PMF, measuring user engagement, running PMF surveys, or deciding whether to scale or keep iterating.
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。
Review any client-facing message or material (emails, letters, newsletters, social posts, website copy, presentations, performance reports, proposals) for compliance with SEC rules that govern investment advisers — the Marketing Rule (206(4)-1), fiduciary duty / antifraud provisions (206), Books and Records (204-2), and Reg BI where broker-dealer activity applies. Produces a pass/flag/fail markup, required disclosures, a books-and-records scratch-pad entry, and an archiving handoff reminder. Triggers on "compliance check", "/compliance", "is this compliant", "review this before I send it", "can I say this to a client", "check this email/post/deck". Use it whenever an advisor asks to check, review, or look over anything before it goes to a client or prospect — "check this before I send it", "look this over before it goes out" — even a routine note, because deciding that a message needs no changes is part of the review, not a reason to skip it.
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。
Onboarding-and-switching playbook for SMB Product-Builder products. Defines the first-run experience that turns a prospect leaving an incumbent (ServiceTitan/Toast/Mindbody/Shopify/QuickBooks) into an activated user — import-first onboarding, the activation milestone, sample-data fallback, and the time-to-first-value target. Applied by migration-import-engineer and architect/pm so onboarding is designed as a funnel, not an afterthought. Our whole wedge is "low switching cost"; this skill makes that real on day one.
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。
Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires a configured Blender MCP backend (official or ahujasid) and Python with opencv-python, numpy, scipy 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。
Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires the Blender MCP addon to be running (port 9876) and Python with opencv-python, numpy, scipy 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。
Guides incident response from detection through postmortem. Use when designing on-call runbooks, triaging production incidents, writing status updates, or improving MTTD and MTTR.
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。
Proves protocols and agent workflows with TLA+, P, Alloy, Z3, Dafny, or Lean. Use when proving protocols correct, writing TLA+ specs, or verifying LLM proofs.
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。
Refactors code while preserving behavior. Use when reducing technical debt, planning codemods, applying strangler migrations, or tightening CI guardrails around risky changes.
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。
Audits SaaS, cloud, payment, and AI spend. Use when analyzing bills, right-sizing plans, sizing Savings Plans or CUDs, cutting payment-processing fees, or LLM self-host breakeven.
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。
Configures and hardens Claude Code and Codex MCP servers. Use when connecting databases, APIs, SaaS, building servers, or serving a clearance-filtered knowledge base.
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This skill should be used when a session's job is POST-MERGE VERIFICATION — taking merged PRs and establishing whether the change actually does what it claimed, then recording the result against its `pr_verifications` obligation. "validate the merged PRs", "work the verification backlog", "did that PR actually work", "run the post-merge E2E". It owns the obligation ledger the repo-pulse worker fills. Do NOT load it for building a change and opening its PR (that is `genesis-development`), or for driving open PRs to merge (that is `closing-session`).
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 and executes compatibility-preserving migrations across APIs, schemas, frameworks, dependencies, protocols, storage, and infrastructure with baselines, coexistence, staged cutover, rollback or forward-fix paths, and explicit removal criteria. Use for deprecations, upgrades, data moves, contract changes, and replacing live implementations. Not for a local refactor with no external state or compatibility boundary.
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。
Executes an existing implementation plan task by task, preserving its interfaces and constraints, running focused checks after each slice, and stopping when the repository differs from the plan. Use when a written plan or approved checklist already exists. Not for inventing the plan, debugging an unexplained failure, or broad unscoped cleanup.
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。
Processes code review feedback by classifying each finding, reproducing or validating it against the real contract, applying the smallest in-scope fix, and re-running focused proof. Use after comments arrive on a diff, pull request, design, or implementation. Not for requesting a first review or debugging an unrelated failure.
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 manages isolated Git worktrees for parallel agents, risky experiments, or long-running feature work while preserving the user's current checkout. Use before concurrent branch edits or when isolation makes rollback safer. Not for a trivial one-file change that is already isolated.
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。
Grounds version-sensitive engineering decisions in authoritative current sources by detecting the installed stack, retrieving official documentation or source, reconciling conflicts, and recording citations and confidence. Use for framework APIs, dependency behavior, standards, compatibility, migrations, or technical recommendations that may have changed. Not for stable project-local logic or broad product discovery.
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。
Prepares and controls software releases with risk classification, CI quality gates, artifact provenance, feature flags, staged rollout, measurable promotion and rollback thresholds, post-deploy verification, and cleanup. Use for deployment pipelines, launch readiness, canaries, production rollout, release automation, or rollback planning. Not for merely merging a completed development branch or proving one local change.
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。