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概要と使いどころ
专业微信公众号文章助手，支持四个独立且可组合模式：article 写正文；image-prompts 从文章生成版本化、provider-neutral 的图片提示词 manifest 与稳定占位符，但不调用生图；layout 把文章和 manifest 确定性转换为微信安全的内联 HTML；full-pipeline 串联正文、提示词/占位符和排版。用户提到公众号、微信文章、推文、配图提示词、图片 manifest、公众号排版、内联 HTML 或完整图文流水线时触发。支持企业官号、个人技术博客、活动回顾和产品评测。不用于：带引用的研究报告（用 deep-research）、PPT/组图（用 deck-studio）、英文文章、仅润色一小段。永不自动发布，永不把提示词或占位符声称为已生成图片。
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。
Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps. Use when generating, reviewing, refactoring, or migrating IaC and when building delivery/testing pipelines.
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。
Manage HubSpot CRM contacts, companies, deals, and CMS content via API. Use when the user says "add contact to HubSpot", "create deal", "search CRM", "HubSpot contacts", "update deal stage", "CRM report", "HubSpot pages", or asks about managing their sales pipeline, CRM data, or HubSpot content.
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。
公众号运营｜微信公众号｜公众号一条龙｜公众号全流程｜自媒体运营｜wechat automation｜content pipeline｜AIGC workflow — 公众号一条龙运营总控入口，选题→写稿→审稿→排版→配图→发布串联 8 个子 skill，单条指令完成整篇图文从 0 到上架。面向公众号小编、自媒体、品牌内容。触发词分层：**一条龙流程**「一条龙」「完整流程」「从头做」「从 0 到发布」；**新做新发**「帮我写篇公众号文章」「做一篇公众号文章」「我想发一篇」「帮我发一篇」「再来一篇」；**选题起点**「今天写什么好」「有什么好写的」「找个话题」「爆款选题」「热点选题」「起个爆款标题」；**策划起点**「内容日历」「系列策划」「专栏规划」「连载」；**流程恢复**「接着上次那篇」「继续昨天的」「继续上次的」「接着之前的进度」；**显式模型新写**「用 GPT 写一篇」「用 DeepSeek 写一篇」「把提纲写成文章」。子 skill（topics/writing/review/formatting/images/publish/sticker/assets）单独触发仅限对**已有产物**的修改场景（如"改标题""润色这段""排版""审稿""加封面""发布"）；新做/策划/多环节串联一律走本入口。
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。
Automate the full QuarkPanTool → mswnlz GitHub content publishing pipeline. Supports Quark / Baidu / Aliyun drives with multi-account rotation. Use when the user provides share URLs and wants: (1) auto-detect drive type and route to correct handler, (2) save resources with automatic account rotation, (3) clean junk files from shared folders, (4) copy promotional files into each folder, (5) generate permanent encrypted share links, (6) auto-classify items into local content dirs (book/movies/AIknowledge/tools/etc.), (7) append/update YYYYMM.md and README index, (8) local git commit, (9) send unified Telegram notifications to multiple groups, (10) rebuild site and deploy to Cloudflare Pages.
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。
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to flag hallucinated citations, build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".
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。
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimental_log.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER when the orchestrator delegates Step 2 or when the user asks to "generate the figures for my paper" or "render the plots from this experiment log".
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。
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimental_log.md). TRIGGER when the user says "aggregate my agent logs for paper writing", "extract experiments from my coding agent history", "prepare PaperOrchestra inputs from my cache", "turn my agent logs into a paper", mentions a folder or directory they want to use as the basis for a paper, or wants to run PaperOrchestra but only has scattered agent experiment histories rather than structured inputs. Run this BEFORE paper-orchestra. Also called automatically by paper-orchestra when workspace/inputs/idea.md or workspace/inputs/experimental_log.md are missing.
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。
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimental_log.md, template.tex, conference_guidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator delegates Step 1 or when the user asks to "outline a paper from raw materials" or "generate the paper structure".
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。
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the 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。
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate concession-threshold guard that blocks acceptance on unresolved critical findings. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this 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。
Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).
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。
Use for software supply-chain security assessment covering SBOM, SCA, CI/CD pipelines, container images, build integrity, dependency provenance, and vulnerability reachability.
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。
Use when the user wants a small, ad-hoc change handled with GSD guarantees (atomic commits, STATE.md) but without the full pipeline. Tasks land in .planning/quick/; --full, --discuss, --research, --validate add the skipped agents back.
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。
Pointer to the upstream skill for building voice agents with speech-to-speech and STT-LLM-TTS pipelines, installed from upstream.
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。
Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.
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。
Weekdays, file work only, no browser at all. Reads every ledger, queue file, and run record written since its own cursors, turns the member's ticks into sent rows and closed cards, folds the card inbox, re-renders the pipeline board, and writes the short morning brief the member opens first. It names the veto window every day. It holds every outbound action unless you released the channel, and it never touches a credential.
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, on a Friday, read only everywhere. Scores the week from the ledgers with a source path beside every number, replays the browser flows the other routines depend on, names one thing to kill and one thing to scale, and files both as cards. It sends only where you released the channel, spends only where you released it, never touches a credential, and never writes a number it did not count out of a named file this run.
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。
Runs once by hand on the first day and once a month after that, on the first weekday, light browser lane. On the first run it researches the business from its own public surfaces before asking anything, writes the strategy folder, seeds the pipeline, reconciles the schedule table, and registers the recurring jobs. On every monthly run it re-reads the evidence the kit produced, applies what changed to the files it owns, and carries every member written setting across verbatim. It holds every outbound action unless you released the channel, and it never enters a credential.
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。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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 nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
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。
TLA+ formal verification for design correctness and PBT pipeline integration
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。
Infrastructure as Code patterns (Terraform, Kubernetes), observability design (SLOs, metrics, alerting, dashboards), and pipeline security stages. Load when designing infrastructure, observability, or security scanning.
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。
Platform design review critique dimensions and severity levels. Load when reviewing CI/CD pipelines, infrastructure, deployment strategies, observability, or security designs.
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。