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概要と使いどころ
Iteratively study and apply elegant coding patterns. Each iteration - understand the code, research what simple and elegant code looks like, apply learnings, verify with CI. Use as a standalone refactoring pass or when the user asks to make code more elegant, simple, or idiomatic.
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。
Generate a PR_MESSAGE.md file from session context using GitHub CLI. Detects related issues via branch-keyword search, writes a structured PR description with commit subject, summary, test plan, and changelog. Validates with commitlint and markdownlint. Use when preparing a pull request on 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。
LCSC part search, stock and pricing checks, and datasheet sync. Load the canonical Circuit Weaver skill from `skills/lcsc/SKILL.md`.
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。
Mouser part search, price comparison, and datasheet sync. Load the canonical Circuit Weaver skill from `skills/mouser/SKILL.md`.
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 DigiKey by keyword or part number, check stock and price breaks, retrieve component specifications, and download or synchronize datasheets for an electronics project. Use when the user explicitly wants DigiKey data, a DigiKey prototype-order comparison, or direct datasheet synchronization. For distributor-neutral sourcing, multi-vendor price comparison, or order-file generation, use the bom skill instead.
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。
DigiKey part search, stock and pricing checks, and datasheet sync. Load the canonical Circuit Weaver skill from `skills/digikey/SKILL.md`.
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。
Assemble a finished piece from human-written fragments via an orchestrated two-loop pipeline - an assembly loop (assembler + cold reviewer) that sequences the fragments into a draft, then a polish loop (polisher + cold reviewer), then frequency analysis and a catalog-deslop pass with a regression gate. Two modes with opposite fidelity contracts - stitch mode preserves the author's exact wording, narrative mode preserves every idea but may rewrite freely. Use when the user says "mosaic", "assemble my fragments", "stitch these notes into a post", or provides a fragments file to turn into a finished piece. For writing from a topic with research, use article-factory; for prose from scratch, use the prose 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。
Orchestrate work as a cyclic directed graph of subagent-executed nodes with transition criteria on edges, budgeted cycles, and per-node gates (runtime verification, metric, or artifact). Use when the user says "workgraph", "run this as a graph", "graph this work", or when a goal has parallel branches, feedback loops, or mixed gate types that a linear loop cannot express. Every node is sized to a 64k context budget, so small goals run as a two-node do/verify graph and large goals scale out without caps. For clock-driven or metric-driven single loops, timeboxed-iterating and autoresearch remain lighter.
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。
Improve an existing agent SKILL.md through one gated cycle: select the target (default this skill), snapshot the committed baseline, diagnose against skill-design-principles plus four rubric dimensions, apply exactly one focused change, and ship only if a different agent than the author PASSes with quoted evidence. Use when the user says "improve this skill", "improve-skill", "iterate on SKILL.md", or "apply skill-design-principles to a skill". Do not use for code slop over a duration (anneal), generic timeboxed work (timeboxed-iterating), graph orchestration (workgraph), metric hill-climb on a codebase (autoresearch), or stripping slop from existing prose (catalog-deslop).
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。
Timeboxed ideation on a topic where lens-driven proposers generate structurally different ideas, blind critics cull them, and pairwise judges rank the survivors on a tournament ladder instead of 1-10 scores, evolving the leaders until the clock runs out. Use when the user wants to brainstorm, explore ideas, discover features, generate options, or think through possibilities for a specified duration. Triggers on requests like "brainstorm X for 30 minutes", "ideate on X", "spend an hour thinking about X", "what features should we build", "explore options for X". Not for executing a task over time (timeboxed-iterating) or optimizing a measurable metric (autoresearch).
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 and run local LLMs with Harbor. Use when the user wants to run models locally, install Harbor, start Open WebUI, llama.cpp, Ollama, vLLM, Docker Model Runner, MLX, or oMLX, pull GGUF or HuggingFace models, add SearXNG web search, Speaches TTS/STT, or Open Terminal code execution, launch Codex/Claude/Grok/OpenCode against a Harbor backend, or troubleshoot GPU, VRAM, and service startup.
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。
Analyzes ELF binaries, executables, and firmware images to understand behavior without source code; coordinates sub-skills binary-re:triage, binary-re:static-analysis, binary-re:dynamic-analysis, binary-re:synthesis, and binary-re:tool-setup. Use when working with a binary, executable, or ELF file; asked to reverse engineer, disassemble, decompile, or figure out what something does; targeting ARM64, ARMv7, x86_64, or MIPS; or analyzing Python bytecode (.pyc, marshal). For Python bytecode analysis, see docs/python-bytecode-re.md.
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。
Fingerprints unknown binaries using rabin2 — detects architecture (ARM, x86, MIPS), ABI, libc, dependencies, and strings of interest. Use when first encountering an unknown binary, ELF file, or firmware blob, or when you need architecture or ABI information before committing to deeper 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。
Compiles binary analysis findings into structured reports — correlates facts from triage, static, and dynamic phases, validates hypotheses against evidence, and produces traceable documentation. Use when ready to summarize findings, generate a report, document what a binary does, or prepare results for handoff or archival.
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。
Observes runtime behavior of binaries via QEMU emulation, GDB debugging, and Frida hooking — syscall tracing, breakpoints, memory inspection, and function interception. Use when running, tracing, or debugging a binary, verifying hypotheses from static analysis, or watching actual syscalls, network calls, or file access; requires human approval before any execution.
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 static analysis of binary code using radare2 and Ghidra — function enumeration, cross-reference tracing, decompilation, and control flow graphs without executing the binary. Use when disassembling or decompiling code, mapping functions, tracing data flow, or building hypotheses before dynamic verification.
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。
Installs and configures reverse engineering tools including radare2, Ghidra, GDB, QEMU, Frida, and cross-compilation toolchains for ARM64, ARMv7, x86_64, and MIPS. Use when a tool command fails, a required tool is missing, or you need to set up a fresh analysis environment.
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。
Evaluate survey data pipelines and research methodology. Analyzes response bias detection (speeders, straight-liners, bots), statistical significance testing (t-test, chi-square, ANOVA with Bonferroni correction), sentiment and theme extraction from open-ended responses.
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 pricing research and sensitivity analysis systems for Van Westendorp price sensitivity meter (OPP/IDP/PMC/PME intersections), Gabor-Granger demand curves, Newton-Miller-Smith revenue extension, price elasticity econometric modeling, willingness-to-pay estimation.
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 SEO + AI-search optimization agent for 2026. Audits and fixes technical SEO, on-page metadata, structured data, Core Web Vitals (LCP/INP/CLS), llms.txt, Generative Engine Optimization (GEO) for ChatGPT/Perplexity/AI Overviews, E-E-A-T author signals.
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。
Analyze grant management and sponsored research operations including proposal lifecycle tracking, pre-award routing and budget development, post-award expenditure monitoring and burn rate analysis, 2 CFR 200 Uniform Guidance cost allowability enforcement.
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。
Analyze university and research institution funding allocation systems including RCM revenue attribution, performance-based budgeting, faculty startup package management, F&A indirect cost recovery distribution, equipment sharing and core facility recharge rates.
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。
Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) — make a site citable by ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews. Triggers: "GEO", "generative engine optimization", "AEO", "answer engine optimization", "AI search optimization".
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。
Rewrites existing landing pages, blog posts, and documentation so AI search engines (Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini) cite them. Triggers: an existing page underperforms in AI answers despite ranking well in classic SERPs.
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。