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End-to-end social content campaign pipeline. Takes a marketing goal and produces scheduled, on-brand social posts with visuals across postiz-supported platforms, while keeping Typefully for X/thread specialist flows and fallback coverage. Chains: CMO strategy, content writing with voice calibration, AI slop audit, selective Paper MCP visual design, and routed scheduling via postiz or Typefully. Each phase has a human gate. Use this skill whenever someone wants to plan and execute a batch of social posts — not just one-off tweets. Triggers include: "social campaign", "schedule posts", "pre-launch content", "content calendar", "social content pipeline", "build up content", "I need social media content for my launch", "help me build social presence", "plan a week of posts", "batch social content", "social media strategy", or any request involving multiple scheduled posts with optional visuals.

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Design AI chatbot conversation flows with fallbacks. TRIGGERS - Use when user needs help with ai-chatbot-flow related tasks.

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Use when operating Inngest API resources from the terminal with `npx inngest-cli@latest api`: Cloud/local run debugging, event-run lookup, function traces, function invocation, app syncs, webhooks, environments, keys, account checks, and Insights queries. Provides prescriptive command routing for agents: which CLI command to run for a run ID, event ID, app ID, function ID, Cloud environment, API key, missing ID, or potentially mutating operation. Use `inngest-cli` for dev server setup/general CLI workflows and `inngest-api` only when raw REST API v2 docs or OpenAPI fallback are needed.

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Audit code for correctness and rigor — logic errors, silent error swallowing, wishful thinking, and unjustified fallbacks. This is not a style review; it's a logic review. Use when you want a focused correctness audit separate from the full code-police pass.

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Pitfalls, examples, and edge cases for workers executing a Surogates subagent task. Loaded automatically when the dispatcher spawns a session bound to a Task (Session.task_id is set).

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Use when support operations, launch fallout, repeated customer issues, or triage ownership need a structured response layer.

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Use when a feature or code change made by Claude is complete and the developer wants to confirm they actually understood it before shipping. Triggers on "handoff", "quiz me", "comprehension check", "did I understand this", "verify I read the code", "test my understanding", or after finishing a feature when the developer wants to take ownership of AI-written code. Also triggers on requests for an interactive or clickable quiz ("interactive mode", "let me answer with the mouse", "mode interactif", "je réponds à la souris"). Quizzes the developer on WHERE the logic lives (file/function) and WHY decisions were made (trade-offs, behavior, debugging), with the number of questions scaled to the size of the change. Multiple-choice questions are clickable by default (asked through `AskUserQuestion`) and fall back to typed answers. Points to file:line on wrong answers, allows one retry, then reveals the answer, and ends with a score plus the weak areas to re-read. Conducts the quiz in the developer's language.

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Perform critical Rust code reviews covering correctness, edition 2024 compliance, error handling, API design, async pitfalls, and dependency hygiene. ALWAYS use this skill when the user wants to review, audit, or critically evaluate Rust code — whether that's a PR diff, a specific crate or module, a cross-cutting concern like error handling, or the whole codebase. This includes requests to "review my changes", "check this code", "audit for best practices", "look for issues", "what's wrong with this", or "flag anything that could bite us". Use it even when the user doesn't say "review" explicitly but is asking you to find problems, inconsistencies, or anti-patterns in existing Rust code. Do NOT use for requests to write, fix, refactor, test, explain, or implement code — only for evaluating existing code.

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PowerShell Windows patterns. Critical pitfalls, operator syntax, error handling.

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Reasoning data lost across agent steps and missing low-confidence fallback Keywords: sqlite, triangulation, escalation, confidence

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Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or explicit GPT-5.4 upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any fallback browsing to official OpenAI domains.

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Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

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Complete operational workflow for implementer agents (Codex, Gemini, etc.) making code changes and writing tests. Drives all work through atomic commits — each loop operates on the smallest complete, reviewable change. Defines the Code Change Loop, Test Writing Loop, Lint Gate, and Issue Filing process with circuit breakers, severity levels, and escalation rules. Requires `committer` for all commits. Includes bundled provider-aware review scripts that keep same-model shell-outs as the last resort, plus a fresh-context Codex fallback for code review and test audit. Use this skill when starting any implementation task.

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A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured commands.

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命理圆桌会——多流派命理师（盲派、子平格局派、调候派、民国新派）对同一八字命盘进行圆桌论命：多视角总评、交叉质疑、可证伪断语、逐轮记分复盘。用户提到八字、四柱、命盘分析、批命、论流年大运、盲派/格局/调候论命、"多个命理师讨论"、验证式论命时使用本 skill；即使用户只是贴出一张八字排盘求分析，也应启用（可用轻量模式）。Multi-school Chinese BaZi (Four Pillars) roundtable reading: several fortune-tellers from rival schools analyze the same natal chart, cross-examine each other, issue falsifiable predictions, and keep score. Use whenever the user asks about BaZi, Four Pillars, Chinese astrology chart reading, or destiny analysis.

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"Apply the SERVQUAL model (Parasuraman, Zeithaml, and Berry, 1988) to measure service quality gaps across five dimensions. Use this skill when the user needs to diagnose service quality shortfalls, benchmark customer expectations against perceptions, design service improvement programs, or when they ask 'where is our service failing', 'what do customers expect vs experience', or 'how do we measure service quality'.".

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Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.

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Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).

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Build and maintain a rolling financial forecast using a 12–18 month horizon. Lock actuals, reforecast remaining periods, extend the planning window, and generate a waterfall bridge showing prior-to-new variance. Use monthly or quarterly when the organization follows a continuous planning cadence instead of annual budgets.

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Review commercial contracts, NDAs, MSAs, SOWs, and vendor agreements for obligation clarity, risk allocation, termination, data handling, and negotiation red flags. Use when a user needs a structured contract summary, red flag list, fallback positions, or clause-by-clause review. Do not use as jurisdiction-specific legal advice, litigation strategy, or final legal sign-off.

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Detection engineering skill for writing, testing, and tuning Sigma, YARA, YARA-L, Suricata, Snort, Zeek, KQL, SPL, EQL, Chronicle, and Elastic detection rules. Covers ATT&CK coverage mapping, telemetry gap analysis, false-positive tuning, detection-as-code pipelines, and validation with Atomic Red Team and Caldera. Use to design durable detections backed by tests.

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Kubernetes and container security skill for cluster hardening, RBAC review, admission control (OPA Gatekeeper, Kyverno), Pod Security Standards, network policies, secrets management, runtime defense (Falco, Tetragon), image supply chain (cosign, SLSA, in-toto), and CIS benchmark compliance. Use to assess and harden clusters you operate.

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Use for Interpreter app settings and account usage. Prefer the direct workstation tools for reading settings, changing settings, and checking remaining Interpreter balance; use the Interpreter CLI only as a fallback.

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Section-by-section media planning and generation. Image generation (GPT Image 2 primary, built-in fallback), logo/icon generation (Ideogram v3 → favicon set), video generation (Veo 3.1), social preview images (OG 1200x630 + AI search optimization), stock photo curation (Pexels, Pixabay), critique/remix loops (max 3 rounds), asset compression pipeline, and media performance budgets.

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。