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概要と使いどころ
Standardizes the creation and evaluation of high-density Agent Skills (Claude, Cursor, Windsurf). Ensures skills achieve high Activation (specificity/completeness) and Implementation (conciseness/actionability) scores. Use when: writing or auditing SKILL.md, improving trigger accuracy, or refactoring skills to reduce redundancy and maximize token ROI.
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。
Tidewave MCP runtime tools — debugging, smoke testing, live state; Use when evaluating code in a running Phoenix app.
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。
Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries; Use when learning about libraries, patterns…
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。
Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.
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。
Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.
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。
Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.
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。
Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
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。
Self-improving loop for plugin skills. Reads program.md, proposes one mutation per iteration, evaluates against deterministic scorer, keeps improvements via git, reverts failures. Targets weakest skill+dimension. Use with /loop for overnight runs.
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。
Audit Hex deps for supply-chain security risk — bidi chars, compile-time exec, maintainer changes, typosquats, CVEs. Use after mix deps.update, when checking if a package upgrade is safe, or reviewing mix.lock PR diffs.
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。
The Balanced Coupling model for software design. Use when: designing modular architectures, evaluating coupling between components, reviewing code modularity, deciding whether to split or merge modules/services, assessing integration patterns, classifying coupling as balanced or unbalanced, applying DDD strategic and tactical patterns, reasoning about cohesion vs coupling trade-offs, identifying distributed monolith risks, or explaining why a system is hard to change. Provides the three-dimensional framework (integration strength, distance, volatility) and the balance rule for making coupling 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。
Produces human-sounding text that avoids detectable AI writing patterns. Activates on any writing task — tweets, emails, articles, bios, captions, reports, copy, messages, LinkedIn posts, cover letters, README files, or any content where the output must not read as AI-generated. Enforces banned vocabulary, structural variety, punctuation discipline, accuracy rules, and voice calibration. Use when the user says "write," "draft," "rewrite," "make this sound human," "anti-slop," "not AI," or any variation of wanting authentic-sounding output.
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。
文本标点规范化工具。只处理标点本身：合并重复标点、统一中英文标点与引号样式、规范省略号和标点周围空格。 当用户提到标点规范化、标点清理、合并重复标点、全半角转换等需求时使用此skill。 适用于科研文本中标点混乱、重复符号过多的场景，不负责 HTML、表情、数字或拼写清理。
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。
Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training 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。
Identifies dependencies at heightened risk of exploitation or takeover. Use when assessing supply chain attack surface, evaluating dependency health, or scoping security engagements.
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。
Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.
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 evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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。
Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.
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 the user needs to plan, de-risk, or ground a software engineering / computer science undergraduate thesis from a real codebase before final writing. Trigger for requests such as "根据项目写毕业论文", "先做论文大纲", "用真实项目材料规划论文", "检查论文是否脱离代码", "根据检测报告降查重", "根据AIGC报告改写定稿", "继续在手改初稿上改", or when an existing draft thesis must be reworked against a school format sample. Produces an evidence-backed outline, chapter rewrite plan, figure plan, and handoff package for academic-paper-composer.
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 the user needs to turn a real software engineering / computer science project and an existing thesis draft into a submission-ready undergraduate thesis manuscript. Trigger for requests such as "根据项目把论文改成定稿", "按学校模板排版成最终版", "复制初稿后生成定稿 Word", "为定稿降查重", "根据PaperPass报告降AIGC", "继续在手改初稿上改", "恢复原来的图表和数据库说明", or when academic-paper-strategist has already produced an evidence-backed rewrite plan. Outputs a cleaned manuscript, final DOCX workflow, and a separate rework report.
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。
Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.
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。
Typosquatting detection, install script analysis, dependency confusion prevention, and phantom dependency detection for npm/pip.
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。
ThoughtWorks Radar model, technology evaluation framework, ADR format, and stack compatibility analysis
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。
Adaptive content summarization - auto-detect content type and produce relevant summary
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 experiment loop: hypothesize > modify > test > evaluate > keep/discard > repeat. Run N experiments automatically with measurable metrics. Works for performance optimization, A/B testing, prompt engineering, and any measurable improvement task.
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。