6,709 件 ・ 関連度順
概要と使いどころ
Validate user stories — INVEST scoring, story-to-test coverage mapping, PRP completeness, and development-readiness assessment. Use after writing stories and before starting implementation. Trigger on "validate the stories", "are these stories ready", "INVEST check", "story coverage", "is this ready to build".
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 component architecture, REST API contracts, and data models (Pydantic schemas) before implementation. Use when planning how to build a feature, defining endpoints, or modeling data. Trigger on "design the architecture", "design the API", "data model", "how should I structure this feature".
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。
Claude-native review of code for quality, structure, naming, and maintainability (no external AI). Use when reviewing a file or diff for code quality before committing. Trigger on "review this code", "code review", "is this well-written". For security, use /security-verify; for companion-AI review, use /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。
Performs critical code reviews using REVIEW.md guidelines. Evaluates code quality, architecture, testing, and domain-specific concerns (legal faithfulness, cross-law references, engine safety). Use after completing significant code changes or before merging.
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 a Go design spec before implementation begins - dispatch a subagent that checks a design doc for completeness, consistency, and idiomatic Go (simplicity, small consumer-defined interfaces, explicit wrapped errors, context propagation). Use when a user has a Go spec to review, asks "is this spec ready?", or is about to implement from a written design. Distilled from spf13/go-skills and adapted to this repo's conventions.
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 when independently reviewing a research-paper draft before submission, running a mock top-conference panel, assessing readiness or accept/reject risk, or predicting reviewer concerns for venues such as CVPR, ICCV, ECCV, ICLR, NeurIPS, and AAAI. Does not draft or audit author responses after official reviews arrive.
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 when planning a source-grounded method overview, architecture, pipeline, system/data-flow, mechanism, or agent-workflow figure for a CS or deep-learning paper. Produces an editable figure brief before image generation or manual redrawing; not for plots, tables, or general prose.
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 before writing custom code or a research workflow when a maintained library, tool, skill, or proven pattern may already exist. Start with local reuse and targeted evidence; broaden to external research for new dependencies, new mechanisms, or uncertain external facts.
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 a seven-agent adversarial pipeline against a resume and cover letter before submission. Produces channel-conditional verdicts anchored to the realistic applicant pool, separates materials quality from submission expected value, and ends with a contrarian pass that can overturn the verdict.
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 a targeted resume and cover letter for a role folder that already has a job description and role analysis. Runs iterative adversarial review rounds until the materials stabilize, then hands off for human review before submission.
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。
Search a named company's careers board, or resolve a natural-language query into a company list and search each. Extract job descriptions, score pool-anchored and channel-conditional fit against the user's profile, run a contrarian gate before tiers are finalized, and create role folders with assessments. Updates the job search tracker.
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。
Set up a slushpile workspace. Ingests an existing resume or LinkedIn export, interviews the user to fill the gaps, then writes profile.md, preferences.yaml, and stories.md. Run once per workspace, before any other slushpile skill.
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。
Identify and remove phrasing, structure, and word choices that signal AI authorship. Runs iterative passes through fresh voice-agent instances with the orchestrator acting as gatekeeper on every change. Use on a cover letter before submission, or on any prose that has to read as human-written.
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。
Facilitate SAFe 6.0 PI Planning events. Structure the 2-day cadence, generate PI Objectives with Business Value scores, build the Program Board for dependency visualization, and run the Confidence Vote. Use before every Program Increment when the organization follows SAFe.
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。
Refine Product Backlog Items to meet INVEST criteria. Write User Stories with Acceptance Criteria in Given/When/Then format, estimate with Story Points, and flag dependencies. Use before sprint planning when backlog items need grooming. Do not use to prioritize the backlog — that is the Product Owner's 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。
Assess organizational readiness for financial audits (internal or external). Map assertions to account balances, check evidence completeness, score readiness using a Red/Amber/Green framework, and generate a remediation timeline. Aligned with SOX, IFRS, and GAAP audit standards. Use before scheduled audits or when preparing for first-time 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。
Write one-page executive briefs and decision memos that turn messy inputs into a concise recommendation, options, risks, decision ask, and next steps. Use when leaders need a fast read before approving budget, policy, vendor, headcount, or strategy changes. Do not use for long-form reports, slide design, or generic summarization without a 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。
Use before playing, blending, or retargeting GLB animation clips in PlayCanvas, or when diagnosing T-pose, frozen playback, or binding failures.
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 when a PlayCanvas app has too many draw calls or is CPU-bound, before hand-merging meshes or writing custom renderers.
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 before writing PlayCanvas behaviour from scratch, to reuse the scripts and helpers the installed Engine already ships.
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 before loading, placing, scaling, or animating GLB models in PlayCanvas to measure bounds, grounding, clips, joints, and hierarchy with the bundled offline inspector.
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 before repeatedly placing GLB models in PlayCanvas to record each asset's uniform scale, grounding offset, pivot compensation, and yaw once.
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 when a PlayCanvas StandardMaterial needs custom shading its properties cannot express, such as stylized looks or vertex displacement, before writing a ShaderMaterial.
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。
在宣称工作完成、已修复或测试通过之前使用，在提交或创建 PR 之前——必须运行验证命令并确认输出后才能声称成功；始终用证据支撑断言
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。