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概要と使いどころ
Craft verifiable, tight goal prompts for AI coding agents (Claude Code /goal, Codex Automations, Pi), either from a standalone task interview or from an already-approved to-goal handoff. Use when the user says "给我一个 goal 提示词", "帮我写个 goal", "设置一个自动化任务", "我想让 agent 自动做 X", or asks to turn a task into a self-running loop. Ensures every goal has a checkable completion criterion so the agent knows when it's done.
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。
Review and adapt MetaX attention backends, MLA, sparse indexers, cache layouts, and their kernel wrappers against a target vLLM revision and the actually installed MetaX component APIs. Verify dispatch, supported configurations, and GPU numerical behavior. Use for attention adaptations outside vllm_metax/patch/; do not apply monkey-patch headers or patch audit requirements.
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 structured implementation plan from an evidence draft. Validate paths, obtain configured independent Codex and Qoder reviews, synthesize available advice against repository evidence, preserve the draft, and produce testable acceptance criteria and validation steps.
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。
Mine AI coding-agent session transcripts into structured, gate-validated GPU-kernel optimization records for the wiki. Use when asked to turn vibe-coding sessions, Codex rollout logs, or Claude Code project transcripts into wiki records; to summarise what a kernel-optimization session achieved; to build or extend a session-trace store; or to re-run and validate one. Also use when asked how a session-derived record's number, snippet, or provenance was established.
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。
Iterative rubric-based evaluation and self-improvement loop. Builds a scoring rubric interactively, evaluates an artifact with multiple models in parallel (Codex, Gemini, Claude), then autonomously improves the artifact one criterion at a time until a score threshold is met or circuit breaker fires. "/rulph", "rubric evaluate", "rubric score", "multi-model evaluate", "score and improve", "evaluate and iterate", "grade this", "루브릭 루프", "채점 루프", "자율 개선", "개선 루프", "루브릭 평가"
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。
Hoyeon Socratic discussion workflow for Codex. Use when the user invokes "$hoyeon-discuss", "/discuss", asks to think through an idea, challenge assumptions, clarify requirements before planning, run a deep/scored discussion, or says Korean phrases like "같이 생각해보자", "문제 정의", "이거 어떻게 생각해?", "요구사항이 불명확", or "아이디어 구체화".
日本語の概要は準備中です。原文の説明を表示しています。
Hoyeon ambiguity-resolution interview for Codex. Use when the user invokes "$hoyeon-clarify", "/clarify", asks to keep asking until clear, remove ambiguity, clarify requirements/design/domain/plan, or says Korean phrases like "모호한 게 없게", "명확해질 때까지", "질문 계속해", or "Q&A로 정리".
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。
Install and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add, installing a block or component, wiring an installed item into index.html, or working with hyperframes.json. Covers the add command, install locations, block sub-composition wiring, component snippet merging, registry discovery, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).
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。
Build autonomous financial research agents that analyze stocks, SEC filings, earnings calls, and market data to produce investment reports. Use when: automating investment research, building AI-powered stock analysis, creating financial due diligence agents.
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 Codex CLI, Claude Code, or other coding agents as background processes for programmatic control. Use when a user asks to run a coding agent, delegate a task to another AI, spawn a sub-agent, run Claude Code in the background, or orchestrate multiple coding agents on separate tasks.
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。
Measures cyclomatic complexity, cognitive complexity, and function length across codebases to identify maintenance hotspots. Use when someone asks about code complexity, function length analysis, maintainability metrics, or needs to find the most complex parts of their codebase. Trigger words: complexity, cyclomatic, cognitive complexity, long functions, hotspots, maintainability index, code metrics.
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。
Bridge local AI coding agents (Claude Code, Cursor, Gemini CLI, Codex) to messaging platforms (Slack, Telegram, Discord). Use when: controlling AI agents from team chat, sending coding tasks via Slack/Telegram, building team-accessible AI workflows.
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。
Comprehensive expertise in decentralized perpetual futures protocols, including GMX, dYdX, Hyperliquid, and similar platforms. Covers funding rate mechanics, liquidation engines, position management, oracle integration, and protocol risk management. Use when "perpetual futures, perps, funding rate, liquidation, GMX, dYdX, Hyperliquid, leverage trading, margin trading, long position, short position, mark price, index price, open interest, basis trading, " mentioned.
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。
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.
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。
World-class database schema design - data modeling, migrations, relationships, and the battle scars from scaling databases that store billions of rowsUse when "database schema, data model, migration, prisma schema, drizzle schema, create table, add column, foreign key, primary key, uuid, auto increment, soft delete, normalization, denormalization, one to many, many to many, junction table, polymorphic, enum type, index strategy, database, schema, migration, data-model, prisma, drizzle, typeorm, postgresql, mysql, sqlite" mentioned.
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。
Technical SEO site audit via the DataForSEO On-Page API. Crawls the site (up to 100 pages by default), surfaces critical errors (broken links, redirect chains, missing meta, slow pages, schema gaps, indexability issues), and returns a Technical Score (0-100) with prioritized fixes.
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 a spec-driven agent loop where coding tasks live as markdown specs that move through inbox → active → archive, get implemented by Claude Code or Codex, and pass a review gate before they count as done. Use when the user mentions "loop factory", a "spec-driven loop", an "agent factory", wants repeatable/reviewable agent work, or when a repo has a factory/specs/inbox or factory/specs/active directory. Also covers installing and scaffolding the loop-factory CLI into a project.
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。
End-to-end substantive-integrity forensic sweep of a research paper (especially autoresearch / AI-Scientist-style output). Orchestrates the whole pipeline: ingest (arxiv-id | pdf | dir → working dir + pdftotext for L0) → /evidence-ledger (artifact manifest + observability level L0/L1/L2 + span-anchored claims.json) → fan out the integrity auditor skills (consistency, citation, baseline, experiment, presentation, proof-derivation, eval-design — each reads the ledger, emits span-anchored findings) + the zero-verdict-weight AIS writing-style track → advisory memos (/adversarial-case-builder + /novelty-duplication-advisory, no verdict weight) → deterministic tools/adjudicate_findings.py (--ledger REQUIRED) → reviewer-ready Integrity Forensics Report. Cross-model (fresh codex per dimension) and reviewer≠adjudicator: the model proposes findings, the deterministic adjudicator decides the verdict. Observability-aware, detect-only, never an opaque AI-text classifier (a separate zero-weight AIS section lists AI writing-style impressions, never moving the verdict). Triggers: "anti-autoresearch", "integrity audit this paper", "forensic review", "audit a submission", "审一篇投稿的诚信".
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。
Synthesize the single strongest EVIDENCE-BOUND reviewer case to reject a paper, built ONLY from the evidence ledger (claims.json) + the other auditors' confirmed findings — never free-floating LLM critique. Two fresh cross-model codex threads: an attack writes the ~200-word rejection paragraph (every accusation tagged to an existing claim_id/finding_id), a defense decomposes it and rules each point against the anchored evidence. MEMO-ONLY: emits adversarial-case-builder.memo.md (fed to the adjudicator via --memo) and carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Honest-null allowed (the paper may survive). Run LAST. Detect-only. Adapted from ARIS kill-argument. Triggers: "adversarial case", "strongest objection", "rejection memo", "kill argument", "最强拒稿点".
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。
MEMO-ONLY prior-work overlap advisory: surfaces the two ADVISORY taxonomy signals neither a tool nor a model can decide from the paper alone — ADV-TRIVIAL-COMBINATION (standard A+B+C / 缝合 stapling) and ADV-DUPLICATE-PUBLICATION (repackaged / duplicate submission). The executor RETRIEVES candidate prior work (DBLP fuzzy-title + boolean · WebSearch · WebFetch) from the paper's own title + contribution spans in the evidence ledger; TWO fresh cross-model codex reviewers (one per axis) LAY OUT the overlap side-by-side against each anchored contribution claim. It NEVER rules 'trivial' or 'duplicate' (that is a human judgment) and absence of a match is NOT evidence of originality. Emits novelty-duplication-advisory.memo.md + an info-only findings mirror; carries NO verdict weight — tools/adjudicate_findings.py lists it in ZERO_WEIGHT_SKILLS and caps it at info. Detect-only. Adapted from ARIS novelty-check, reframed from 'is MY idea novel' to 'here is the overlap a reviewer should weigh'. Triggers: "novelty advisory", "duplication check", "prior-work overlap", "is this stapling", "缝合", "查重", "重复发表", "duplicate submission".
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 Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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 Agent Patterns workflow skill. Use this skill when the user needs Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Algolia Search Integration workflow skill. Use this skill when the user needs Expert patterns for Algolia search implementation, indexing and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
Algolia Search Integration workflow skill. Use this skill when the user needs Expert patterns for Algolia search implementation, indexing and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。