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概要と使いどころ
Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.
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Two-layer memory for executive decisions, separating raw deliberation from approved decisions to prevent hallucinated consensus. Use when logging decisions, reviewing past decisions, or checking overdue action items.
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Save one piece of brand knowledge to persistent memory with dedup and tags. "store this competitor intel"
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Search the brand's stored knowledge and memory, ranked with provenance. "what are our brand voice guidelines"
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Use when writing agent-relay workflows that must fully validate features end-to-end before merging. Covers the 80-to-100 pattern - going beyond "code compiles" to "feature works, tested E2E locally." Includes repair-before-failure validation gates, review-depth fresh-eyes review/fix loops with test hardening, PGlite for in-memory Postgres testing, mock sandbox patterns, test-fix-rerun loops, verify gates after every edit, and the full lifecycle from implementation through passing tests to commit.
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Use when an application or system — including one built quickly with AI coding agents — needs a security review with regulatory grounding: a STRIDE threat model, a LINDDUN privacy threat model, a dependency exposure screen against live CVE / CISA-KEV / EPSS data, or a selected, non-exhaustive screen of which EU security obligations (GDPR, NIS2, Cyber Resilience Act, AI Act) may apply and which need determination. Orchestrates the server-enforced threat-modeling workflows of the Ansvar Gateway MCP connector and grounds every regulatory statement in officially published text fetched at answer time — scope, role, and application-date limits stated, never a compliance verdict. Never simulates a workflow and never answers legal questions from model memory.
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Sanctions and export control analysis tool for Claude Desktop. Real-time individual screening across 30+ official lists (UN, EU, OFAC, OFSI, French DGT...), sectoral analysis, dual-use goods (EU Reg. 2021/821), US/China extraterritorial regimes (EAR, ITAR, FDPR, ECL), USD/SWIFT risk and jurisdictional mapping across 30+ countries. All results sourced and verified in real time — no memory-based answers. Responds in French, English, German, Spanish, Russian and Chinese. Updated 19 May 2026 (EU 20th package, UK SEUC, Cuba EO). Indicative guidance only — not legal advice.
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Use when a security incident, data breach, or actively exploited vulnerability raises the question "who must we notify, where, and by when?" Screens one incident across the EU reporting regimes — NIS2, GDPR, DORA, and the Cyber Resilience Act — determines which duties fire for each involved entity's roles, resolves the receiving authority per regime and member state from served national law, and produces a deadline table in which every duty, authority, and deadline is cited from official publisher text fetched live through the Ansvar Gateway MCP connector. Never answers from model memory.
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。
Mandatory external verification workflow for ALL non-trivial factual claims before presenting them as true. This skill MUST be used whenever Claude is asked to research any topic, answer factual questions, provide current information, draft documents containing factual claims, give legal advice or cite legal authority, discuss current events or public figures, provide technical or scientific information, state statistics or data points, or answer ANY question where the answer could have changed over time. Also trigger when the user asks Claude to 'check', 'verify', 'confirm', 'research', 'look up', or 'find out' anything, or when the user needs information that requires freshness, precision, or source accuracy. This skill overrides any default tendency to answer from memory. If a task involves stating facts, citing sources, or providing current information — use this skill.
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Produces detailed law-school case briefs of United States judicial opinions in a fixed nine-section structure: memory jogger, facts, procedural history, judicial votes, holding, opinion-by-opinion analysis, five future-application hypotheticals, critique, and verified key quotations. Also builds vote tables, cross-case consistency tables, perspective-based critiques, and LaTeX in article, Beamer and casebook-chapter formats. Use whenever someone asks to brief a case, analyse or explain an opinion, summarise a decision for students, generate hypotheticals from a holding, critique a judgment, tabulate how justices voted, or convert case analysis to LaTeX. Triggers on "brief this case", "case brief", "explain the holding", "what did the court hold", "hypotheticals based on", "critique this opinion", "vote table", "Beamer presentation", "chapter brief", and on any reference to a judicial opinion or Supreme Court decision in a teaching context. Requires web search to verify quotations, votes and holdings.
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。
When you want to create, adapt, or update an Agent Skill (Claude Code, Codex, Cursor, and other hosts) in one of your repos (listed in ~/.config/makerskills/skillify/repos.yaml; defaults to makerskills). Modes — CREATE (from chat, video, dump, or scratch) turns a workflow into a new skill. ADAPT ports an external skill with keep/adapt/add classification, license check, and attribution. UPDATE improves existing skills from learnings with cross-skill propagation, memory-vs-skill triage, and semver. Checks new skills for host portability. Triggers on "/skillify," "create a skill," "make this a skill," "skill from this chat," "adapt this skill," "port this skill," "fork this skill," "update X skill," "apply this to the relevant skills," "propagate this learning," "improve [skill]," "fix [skill]." Part of the -ify trifecta (skillify / toolify / loopify).
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When you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any "I need to actually understand X." Combines web search, URL fetch, agent-browser, last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on "/deep-research," "research X," "investigate X," "do a deep dive on X," "look into X," "what's actually happening with X," "due diligence on X," "validate this market." Differs from a one-shot web search: this is multi-pass with verification.
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Probe IoT/embedded targets for exposed SWD/JTAG debug interfaces using a SEGGER J-Link. Detects whether debug is OPEN, LOCKED (readout-protected), or DEAD (fused off). Use when assessing whether a target's on-chip debug port can be reached, identifying the silicon vendor from DPIDR/IDCODE, and confirming halt+memory access for full debugger control.
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Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.
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Analyse a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying.
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Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.
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Redesign a blocked topic sequence into an interleaved plan with mixed practice across related topics. Use when planning units, homework schedules, or revision programmes.
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 a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.
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Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.
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Universal Runtime best practices for PyTorch inference, Transformers models, and FastAPI serving. Covers device management, model loading, memory optimization, and performance tuning.
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Use when optimizing Godot games — profiler, draw calls, physics tuning, memory management, and common bottlenecks
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Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed. The full decision matrix for OOM (--novram / --cache-none / --disable-smart-memory), shared-VRAM creep on Windows (--reserve-vram N), model-switching with big text encoders (--cache-none), high-VRAM throughput (--gpu-only / --highvram), and attention-backend selection (--use-sage-attention for speed, --use-pytorch-cross-attention as the highest-quality / Z-Image-safe fallback). Also the acceleration-stack + Blackwell/RTX 5000 (sm_120) notes. Use when a graph OOMs (especially long video like LTX 2 / WAN), when the GPU spills into shared VRAM and slows to a crawl, when switching between models eats all RAM, when Z-Image produces black/garbled output under Sage, or when deciding which attention backend to launch with. Flag names verified against upstream comfy/cli_args.py; see Sources.
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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
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Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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。