580 件 ・ 関連度順
概要と使いどころ
Sketched Isotropic Gaussian Regularization primitive. Scalar loss matching the embedding distribution to a standard-normal target via Cramér-Wold slicing and the Epps-Pulley empirical characteristic function test. Port of rbalestr-lab/lejepa (MIT). Default-off in v1.49.571.
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。
```yaml name: cognitive-surrogate description: Layer 6 Barton Cognitive Surrogate System - build, train, and validate psychological models that predict and generate authentic responses matching a subject's cognitive patterns version: 1.0.0 trit: 0 # Ergodic/coordinator role in GF(3) triadic system ```
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。
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
Enforce mandatory pre-action verification checkpoints to prevent pattern-matching from overriding explicit reasoning. Use this skill when about to execute implementation actions (Bash, Write, Edit) to verify hypothesis-action alignment. Blocks execution when hypothesis unverified or action targets different system than hypothesis identified. Critical for preventing cognitive dissonance where correct diagnosis leads to wrong implementation.
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Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.
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 validating name-directory matching
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 modern, high-performance C# code using records, pattern matching, value objects, async/await, Span<T>/Memory<T>, and best-practice API design patterns. Emphasizes functional-style programming with C# 12+ features.
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
Turns a feature request into a full implementation-plan requirements document matching docs/plan/_TEMPLATE.md. Use when the user asks for requirements, a spec, an implementation plan, or wants to flesh out a feature before building. Also use for /requirements or "write requirements for X".
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 short, Semrush-style blog post intros with journey teasers, practical relevance, and reader intent matching. Use when the user asks for /intro, a blog intro, opening paragraph, article lead, listicle intro, or hook copy for a post.
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 branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.
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-test the LLM's ability to execute ASE's core interpreter machinery (control flow, XML placeholders, regex matching, arithmetic) and report an overall compatibility rating. Use when the user wants to check how well the current model/harness is compatible with ASE, or asks to "test ASE compatibility" or "check ASE compatibility".
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 reviewing, writing, or debugging Go code under exercises/part2/ (and matching embedded code in docs/part2/**/*.mdx). Thinks hard about correctness, idiomatic Go, error handling, resource cleanup, concurrency, and — crucially — what the tests would catch: edge cases, races, leaks, timeouts, and port conflicts. Knows the repo's module-mode quirk (GO111MODULE=off) and which dirs carry go.mod.
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Booking & Calendar CRO Engine — Packages all booking, scheduling, .ics download, and Google Calendar integration into a revenue-boosting system. Auto-detect industry → select matching pattern → generate calendar engine + booking UI + export logic. Integrates with cm-google-form (form → sheet) and cm-ads-tracker (conversion events). ALWAYS trigger for: booking, appointment, calendar, reminder, download ics, google calendar, add to calendar, book appointment, schedule, "create booking", "add scheduling", "appointment reminder system", "calendar CRO", "reduce no-show"
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Use ast-grep (sg) to search, lint, and rewrite code across large codebases using AST pattern matching. A blazing-fast alternative to regex-based code transformations that understands syntax structure.
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。
Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta's Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format conversion and loudness matching post-separation.
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。
Rewrite recurring code patterns with syntax-aware matching so agents can run migration codemods more safely than plain regex search and replace.
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。
Track approved container images and write back the matching GitOps manifest changes instead of hand-editing tags across Argo CD applications.
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 a written communication drafted in their voice — an email, a Slack/Teams message, a team update, an announcement, a reply to a thread, or a post. Produces a ready-to-review draft matching references/voice.md; never auto-sends external. Trigger on "/draft", "draft an email", "write a reply", "draft the update", "draft the announcement", "respond to this thread in my voice".
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 clippy locally and report only issues in the current diff, matching the CI reviewdog flow. Use when asked how to lint changed code or get clippy feedback on a branch before pushing.
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。
Primary router skill for broad .NET work. Classify the repo by app model and cross-cutting concern first, then switch to the narrowest matching .NET skill instead of staying at a generic layer.
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Evaluates deal protection mechanisms including break-up fees, no-shop clauses, matching rights, and force-the-vote provisions. Use when analyzing deal protections, negotiating break fees, or assessing termination provisions.
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。
Configurable regex-based alert system for detecting patterns in tool calls and session activity. Supports error triggers, content regex matching, token threshold triggers, and pattern detection with configurable actions.
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。