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概要と使いどころ
Weekly dependency upgrade loop for {{target_repo}}. Scans for outdated packages, applies upgrades on an isolated branch, runs the full verification suite in the sandbox, handles breaking changes with documented migrations, and opens a PR only when everything is green.
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。
Weekly reusable-session content-refresh loop for {{target_repo}}. Reads Google Search Console for {{site_url}} to find pages under {{content_path}} losing impressions and clicks, rotates coverage using a durable ledger, refreshes copy/stats/internal links on an isolated branch, and opens a PR only — never publishing or merging.
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。
Daily brand-mention monitoring loop for {{brand_terms}}. Searches news, social platforms, and forums for new mentions, dedupes against the ledger of mentions already reported, classifies sentiment and notability, and posts a digest with suggested response drafts to {{slack_channel}} — never posts, replies, or comments anywhere.
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。
Daily competitor-watch loop for {{watch_list}}. Fetches each tracked page, diffs it against the last run's stored snapshot, filters cosmetic edits from meaningful ones, and posts a short summary to {{slack_channel}} — or nothing on a quiet day.
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。
Evolutionary ideation engine — loop-controlled multi-cycle idea generation through phases of dreaming, cross-domain stealing, recombination, fitness testing, selection, and Lamarckian meta-learning, producing ranked novel solution candidates with provenance. USE WHEN ideate, id8, novel ideas, evolve ideas, dream up solutions, innovate, breakthrough ideas, idea evolution, multi-cycle creativity, need genuinely new approaches. NOT FOR quick single-pass brainstorming (use BeCreative).
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。
Structured incident investigation using Five Whys, Fishbone, blameless Postmortem, Fault Tree, Kepner-Tregoe, and FMEA — traces failures to systemic root causes rather than blaming humans. USE WHEN root cause, RCA, 5 whys, fishbone, postmortem, incident analysis, fault tree, why does this keep failing, blameless, recurring bug. NOT FOR systemic loops (use SystemsThinking).
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。
Discover WHICH new skills you should create, from your own work history plus your satisfaction/frustration signals. Read-only and proposal-only: it surfaces recurring pain that no existing skill, loop, or workflow covers, then hands you a ranked shortlist to build with CreateSkill. It never creates or edits a skill itself. Frustration is a first-class signal (a topic can look 'covered' while you keep hitting the same wall inside it), so it reads low ratings and recurrence markers, not just session topics. USE WHEN should I create a skill, what skills do I need, suggest skills, skill gap, based on my recent work, am I missing a skill, what should I build. NOT FOR creating/validating/testing/optimizing an individual skill (use CreateSkill) — this only decides WHAT to build, not how.
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 optimization loop — hill-climb any target. Code with metrics, or skills/prompts/agents with LLM-as-judge. USE WHEN optimize, hill climb, improve metric, reduce latency, optimize skill, optimize prompt, eval mode.
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。
Physics-based reasoning framework (Musk methodology) that deconstructs a problem to irreducible fundamental truths, classifies every element as hard constraint, soft constraint, or assumption, then reconstructs the optimal solution from fundamentals alone. USE WHEN first principles, fundamental truths, challenge assumptions, real constraint, rebuild from scratch, start over, physics first, question everything, reasoning by analogy. NOT FOR structural feedback loops (use SystemsThinking).
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。
FLOW framework integration: evidence-led SEO using the Find → Leverage → Optimize → Win loop. Surfaces stage-specific AI prompts from the FLOW knowledge base (41 prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "seo flow", "evidence-led SEO", "find leverage optimize win", or wants stage-specific SEO prompts.
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。
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` 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。
This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.
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。
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
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。
This skill should be used when writing, enhancing, or evaluating the launch prompt for a long-running autonomous agent or a parallel multi-agent orchestration attacking a hard problem: pseudo-formal task briefs that define terms and an exact success predicate linguistically, enumerate non-counting outcomes, set persistence rules with explicit stop and return conditions and effort floors, manage a diverse portfolio of parallel approaches with an approach registry and blocked-route bookkeeping, and gate the return on adversarial audit. Route agent topology and coordination protocols to multi-agent-patterns, runtime control surfaces and loop governance to harness-engineering, evaluator and quality-gate construction to evaluation, judge design to advanced-evaluation, and compaction or memory mechanics to context-compression and memory-systems.
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 publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Codex-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.
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。
Autonomously improve a generated paper via Gemini review through gemini-review MCP → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.
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。
Autonomously improve a generated paper via Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.
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。
Workflow 3: Full paper writing pipeline. Orchestrates paper-plan → paper-figure → paper-write → paper-compile → auto-paper-improvement-loop to go from a narrative report to a polished, submission-ready PDF. Use when user says \"写论文全流程\", \"write paper pipeline\", \"从报告到PDF\", \"paper writing\", or wants the complete paper generation workflow.
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。
Autonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.
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 publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with Claude-supervised iterative refinement loop. Use when user says "生成图表", "画架构图", "AI绘图", "paper illustration", "generate diagram", or needs visual figures for papers.
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 ARIS usage logs and propose optimizations to SKILL.md files, reviewer prompts, and workflow defaults. Outer-loop harness optimization inspired by Meta-Harness (Lee et al., 2026). Use when user says "优化技能", "meta optimize", "improve skills", "分析使用记录", or wants to optimize ARIS's own harness components based on accumulated experience.
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。
Automated PR review and CI auto-fix for GitHub and GitLab using the Composio CLI. Pulls diffs, fetches failing job logs, posts review comments, and loops fix commits until checks go green.
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。
This skill should be used when the user asks to "test the harness", "run integration tests", "validate features with real API", "test with real model calls", "run agent loop tests", "verify end-to-end", or needs to verify OpenHarness features on a real codebase with actual LLM calls.
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。
Apply the Fable-derived 10-stage OBSERVE, OBSERVE, LISTEN, THINK, CONNECT, CONNECT, FEEL, ACCEPT, CREATE, GROW loop to consequential or ambiguous reasoning and decisions. Use for think this through, deep think, architecture review, postmortem, tradeoff analysis, or challenges to assumptions. This is a reasoning review, not a deterministic vault health check or repair workflow.
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。