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概要と使いどころ
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
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Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.
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Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
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Comprehensive guide for writing Apache Cassandra in-JVM distributed tests (dtests). Use when creating tests that simulate multi-node Cassandra clusters within a single JVM for faster integration testing. Covers cluster creation (single-node, multi-node, multi-datacenter), configuration (all cassandra.yaml parameters, features, network topology), instance lifecycle (startup/shutdown/restart), query execution, message filtering for failure scenarios, running code on instances, ClusterUtils utilities, and debugging classloader-related issues (serialization failures, same-class-different-classloader problems).
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Multi-pass deep code review for large patches (1000+ LOC) that maximizes real bug detection. Orchestrates targeted-review, shallow-review, and deep-review in parallel across all HIGH and MEDIUM risk files and commits: understands the feature holistically, splits by file and commit, runs deep review on every HIGH/MEDIUM file, targeted review across the same scope, and per-commit shallow review, followed by cross-cut consistency checks. Use when: reviewing a large patch (feature branch, multi-commit, or single large diff, 1000+ LOC), doing a thorough pre-merge review, or when shallower reviews miss bugs due to patch size. Triggers on: "review this branch", "review these commits", "review this feature", "mega review", "thorough review", "full review of X commits", or when the user specifies a commit range or feature for review.
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Findings-driven targeted code review. Understands the patch via patch-explainer and surrounding code via codebase-analysis, then consults a catalog of bug patterns (~11 categories under references/categories/). Picks only categories whose diff-signals match the patch and items whose "look for" hints match actual code shapes, groups them by review focus (function/file/feature), and spawns parallel subagents — each with a tight scope and selective checklist. Use for medium-to-large patches (50-1000 LOC) as a focused alternative to whole-patch ensemble or whole-file deep review. Triggers on: "review this patch/diff/change", "find bugs in this change", "scoped review", "what could go wrong here", "review using findings", or proactive PR 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。
Audit X Ads measurement, X Pixel and Conversions API, campaign objectives, keyword and conversation targeting, creative, budgets, brand safety, app measurement, and reporting. Use for X Ads, Twitter Ads, promoted posts, X Pixel, X Conversions API, conversation targeting, or paid campaigns on 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。
Audit Reddit Ads measurement, campaign structure, community and interest targeting, creative-native fit, catalog advertising, budgets, brand safety, and reporting. Use for Reddit Ads, promoted posts, conversation ads, community targeting, Reddit Pixel, Reddit Conversions API, or Reddit dynamic product ads.
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。
Student semester onboarding — clinic procedures, tool walkthrough, practice exercises before real cases. Reads the handbook the professor uploaded at setup and teaches it interactively. Use when a new clinic student says "onboard me", "I'm new to the clinic", "getting started", or at the start of each semester; pass --card for the one-page reference.
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。
Cold-start interview — builds your watchlist, indexes the policy library, and learns your materiality threshold so the monitor surfaces signal instead of noise. Use on fresh install, when reconfiguring (--redo), or when re-checking what connectors are actually responding (--check-integrations).
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。
Cold-start interview — connects to your launch tracker, reads past reviews, learns your risk calibration. Use on fresh install, when onboarding product counsel, or when the plugin config has placeholders. Run with --redo to re-interview, or --check-integrations to re-probe connectors only.
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。
House cold-start for the litigation plugin — branches by role (in-house, firm associate, solo) and side (plaintiff, defense, both), captures risk calibration, landscape, and house style, and writes the practice profile CLAUDE.md. Use on a fresh install, when the user wants to set up or redo the practice profile, or to re-check available integrations.
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 the cold-start interview — learns your privacy practice and writes CLAUDE.md from your policy, DPA template, and a reference PIA. Use on first run, when CLAUDE.md is missing or has placeholders, or when the user says "set up the privacy plugin", "onboard me", "configure privacy", or wants to re-run the interview or re-check integrations.
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 roadmap for a legal issue — statutes to check, case law areas to investigate, regulatory frameworks, Westlaw search terms. Leads and frameworks, NOT authoritative citations; students verify and develop everything. Use when a student asks where to start researching, wants a research roadmap for an issue, or needs gaps identified in existing research.
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。
Guided customization of your Legal Builder Hub profile — change one thing without re-running the whole cold-start interview. Adjust practice profile, installed starter pack, watched registries, update preferences, or QA strictness. Use when the user says "change my [thing]", "add a registry", "update my profile", "edit my config", or "customize".
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 the cold-start interview to learn your IP practice and write your practice profile. Use on first install when the practice profile is missing or still contains placeholders, when re-onboarding with --redo, or when re-probing integrations with --check-integrations after connecting or disconnecting an MCP. This is the ONLY skill that should run on a fresh install.
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。
About-you interview and materials intake — classes, bar jurisdiction, learning style (drill-me vs explain-to-me), past outlines, graded essays, old exams, MBE sets, syllabi, papers. Use on a fresh install, when the user says "set up" or "get started", or with --check-integrations to re-probe connectors.
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。
Cold-start setup — learns your jurisdictional footprint and escalation rules from your handbook and termination memos. Asks which states and countries have employees, reads seed documents, and builds a jurisdiction-aware escalation table. Use on fresh install, when CLAUDE.md still has [PLACEHOLDER] markers, or when re-running with --redo or --check-integrations.
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 the cold-start interview — learns your AI governance practice and writes `~/.claude/plugins/config/claude-for-legal/ai-governance-legal/CLAUDE.md` from your AI policy, a reference impact assessment, and key vendor AI agreements. Use when the practice profile is missing or contains `[PLACEHOLDER]` markers, or when user says "set up ai governance plugin", "onboard me", "configure ai governance".
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。
Guidance for Worktrunk (the `wt` CLI) — git worktree management, hooks, and config. Load when working out which worktree a `wt` command will act on, or reaching for the global `-C <path>` to target one; editing .config/wt.toml or ~/.config/worktrunk/config.toml; adding, modifying, or debugging hooks (post-merge, post-start, pre-commit, pre-merge, post-switch, etc.); configuring commit message generation or command aliases; or troubleshooting wt behavior. Also answers general worktrunk/wt questions.
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。
Munder Difflin hive sync — runs the start-of-task hive protocol steps: reads memory.md, checks inbox/ for new messages, and reminds you to record durable facts in memory.md and write coordination files before ending. Use when asked to "sync with the hive", "check my inbox", "hive status", or "hive sync". Proactively suggest at the start of a new task if you haven't checked your hive inbox in this conversation. (munder-difflin)
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。
CCPM - spec-driven project management: PRD → Epic → GitHub Issues → parallel agents → shipped code. Use this skill for anything in the software delivery lifecycle: writing a PRD ('write a PRD for X', 'let's plan X', 'scope this out'), parsing a PRD into an epic, decomposing an epic into tasks, syncing to GitHub ('sync the X epic', 'push tasks to github'), starting work on an issue ('start working on issue N', 'let's work on issue N'), analyzing parallel work streams, running standups ('standup', 'run the standup'), checking status ('what's next', 'what's blocked', 'what are we working on'), closing issues, or merging an epic. Use ccpm any time the user is talking about shipping a feature, managing work, or tracking progress — even if they don't say 'ccpm' or 'PRD'. Do NOT use for: debugging code, writing tests, reviewing PRs, or raw GitHub issue/PR operations with no delivery context.
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。
Adapt this C/C++ ASAN vulnerability pipeline to a different vulnerability class, target shape, language, or detection mechanism. Use when the user wants to port, migrate, retarget, customize, or fork the pipeline for something other than C/C++ memory-safety bugs — web apps, smart contracts, deserialization, ML systems, or any other domain.
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。
Sets up and runs cargo-fuzz, the standard fuzzing tool for Cargo-based Rust projects. Covers cargo fuzz init, the nightly toolchain requirement, fuzz_target! harnesses, Arbitrary-derived structured inputs, sanitizer options, cargo fuzz coverage, and reproducing a crash artifact. Use when fuzzing a Rust crate, writing a fuzz_target!, exercising unsafe blocks or FFI in Rust, or triaging a cargo fuzz crash.
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。