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Requires OFOX_API_KEY — create one at https://app.ofox.ai. Turn an article, doc or release note into a short explainer clip — one person to camera, or a voiceover over illustrative footage. The user supplies the source text and the model generates the speech; audio cannot be uploaded, measured. A 30-second clip holds about eighty spoken words in eight sentences — measured, and well under a tenth of a 1,200-word post — so this skill does not summarise an article, it picks the single idea worth saying and helps choose which one. Use when a user asks to turn writing into a short spoken video, e.g. "make a 30-second explainer from this blog post", "explain this feature in a short video", "turn our changelog into a clip", "a quick video explaining what this paper found". Do not use for a scene between people (see seedance-short-drama), a brand or product ad (see seedance-ad-creative), a handheld creator clip (see ugc-ads), or when the user already has both a portrait and the finished words (see talking-head). Budget sentences as well as words — each sentence boundary costs about 0.7 seconds of silence, so a script with more sentences runs longer at the same word count.

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Takes raw leads and enriches them to outreach-ready status. Chains company-researcher, cro-auditor, decision-maker-finder, apollo-lead-finder, email-validator, and personalization-enricher.

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Full daily sales pipeline execution. Run at 9 AM via cron. Scans for signals, enriches company leads, auto-applies to gigs, runs multi-channel outreach, processes follow-ups. All delivery automated via Chrome DevTools MCP.

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Interactive onboarding wizard that generates agency.config.json for any B2B agency. Asks structured questions about the agency, services, ICP, tools, outreach preferences, and CRM setup. Outputs a complete config file.

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Generate structured call scripts by persona and lead context

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Multi-channel personalized message generation with 3 tiers of personalization

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Check all outreach channels for replies using Chrome DevTools MCP. Scans Zoho webmail, LinkedIn messaging, and Instagram DMs.

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Generates personalized proposals for freelance gigs on Upwork, Freelancer, PeoplePerHour, and Guru. References specific project requirements and includes relevant case studies.

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。

Research a specific person for hyperpersonalized outreach using their public activity

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Framework-based cold email composer with 3 personalization tiers. Generates subject line, body, and follow-up variants. Never uses templates -- builds from frameworks (PAS, BAB, AIDA, Signal-Proof-Ask).

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Deep research on a prospect company using WebSearch for outreach personalization

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Principled action selection for autonomous agents using Expected Free Energy (EFE) under the Active Inference framework. Each candidate action is scored as G(n) = w_prag * (-E[p_n] * damping) + w_epist * (-Var[p_n] * novelty(n)), where agent beliefs are maintained as per-node Beta distributions and updated via Bayesian observation. Selection is a softmax over -gamma * G, making exploration an automatic consequence of uncertainty rather than a hardcoded epsilon. Precision weights w_prag and w_epist self-adapt via surprise history, shifting the agent between exploitation and exploration regimes without any manually tuned schedule.

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Data structures and algorithms for AI agent episodic memory. Covers vector stores (HNSW, IVF, PQ), temporal indexing, knowledge graphs with triple stores, hierarchical summarization, forgetting curves, working/long-term/ procedural memory, and memory consolidation. Deep analysis of MemGPT/Letta, Zep/Graphiti, Mem0, and the Stanford generative agents memory architecture. Teaches the CS fundamentals behind how agents remember, retrieve, and forget. Activate on: "agent memory", "episodic memory", "vector search algorithm", "HNSW", "memory retrieval", "forgetting curve", "knowledge graph memory", "MemGPT", "Letta", "Zep", "Mem0", "memory consolidation", "temporal retrieval", "agent long-term memory", "memory layer". NOT for: conversation protocol design (use agent-conversation-protocols), agent infrastructure selection (use agentic-infrastructure-2026), building RAG pipelines (use ai-engineer).

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An innate exploratory drive that guarantees no graph node remains permanently invisible to an agent population. After a warm-up period (step > 3), each agent fires a coverage scan with probability |unseen_nodes| / |all_nodes|. When triggered, the agent teleports directly to the globally unseen node with the highest belief uncertainty, works it, and returns control to the normal action-selection loop. Probability is self-annealing: it starts near 1.0 when almost everything is unseen and decays toward 0.0 as coverage saturates, requiring no manual schedule. The mechanism is "innate" (hard-coded into the agent step loop) as distinct from the "adaptive" EFE path, which requires local neighbors and learned precision weights. Together they form a two-layer exploration architecture: adaptive for locally-guided foraging, innate for topological completeness.

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Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.

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Build and validate a static DAG reachability index from source-qualified path/chain decompositions and partial transitive-edge filtering. NOT for treating a partial edge filter as a complete transitive reduction or runtime scheduler.

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Generates shell completion scripts for CLI tools. Parses command structure from help output, commander/yargs configs, or manual specification. Produces completions for bash, zsh, and fish that follow each shell's conventions.

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Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use jury_rig-architect), specific tool implementation.

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Audit a compliance-ladder design (C0-C6) and transcript-fidelity ladder (T0-T5) plus their adapter conformance fixtures — Codex, Claude Code, Cloudflare, Ollama/LM Studio, custom stdio/HTTP agents — for the two failure modes that make a C-badge or T-fidelity label a lie: the ladder disagreeing with itself across doc/schema/ui/probe surfaces, and a level being reachable by self-report because no daemon-witnessed negative probe actually tries to forge it. Use when designing or freezing a compliance ladder, wiring an adapter conformance probe suite, deciding whether a numeric C-badge or T-label is safe to ship, or investigating a self-attestation or observed-to-controlled bypass risk. NOT for auditing a cryptographic protocol or whitepaper for adversarial soundness (`redteam-review`), writing TLA+/formal invariants for a daemon monitor (`runtime-verification-for-agents`), or agent identity, successor linkage, and reputation across restarts (`agent-identity-continuity-reputation`).

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The barber for your coding agents' config. Weighs everything every agent on this machine loads (instruction files, memory, MCP servers, hooks, skills, commands, subagents, plugins, leftovers), prices each item in tokens from your own transcripts, and parks the dead weight with a receipt and an undo. Use this skill whenever the user mentions config bloat, a heavy or slow session start, tokens burned before the first word, too many MCP servers, hooks that are slow or fire on every prompt, skills or commands or plugins nobody invokes, cleaning up or auditing CLAUDE.md or AGENTS.md, Codex config, or asks what does my agent actually load, why is my context full before I type, which of these servers can I turn off, or what is this plugin costing me. Use it even when they only say the session feels bloated or startup got slow, and even when they mention /context, /doctor or /skill-doctor.

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Convert Claude's Markdown output to each channel's native text syntax before delivery. Adds zero-dependency formatting for WhatsApp, Telegram, and Slack (marker substitution). Also ships a Signal rich-text helper (parseSignalStyles) used by the Signal skill.

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Add Agent Swarm (Teams) support to Telegram. Each subagent gets its own bot identity in the group. Requires Telegram channel to be set up first (use /add-telegram). Triggers on "agent swarm", "agent teams telegram", "telegram swarm", "bot pool".

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Turn a Git repository URL or local path into one elegant, self-contained HTML learning page that teaches the project's value, architecture, and operating principles top-down with diagrams. Use when the user wants to understand, learn, onboard, explain, map, or study a repository.

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Tiered web access router. TIER 1 insane-search: headless extraction plus WAF bypass via curl_cffi, yt-dlp, Jina Reader, public APIs, Playwright fallback for blocked/403/Cloudflare pages, articles, YouTube subtitles. TIER 1.5 agent-reach: platform-native readers for Chinese/social platforms: Xiaohongshu, Douyin, Weibo, Bilibili, V2EX, WeChat, plus Twitter/Reddit/LinkedIn/GitHub. TIER 2 aside-browser FIRST (user's real logged-in browser: aside exec agent, aside repl Playwright), then CloakBrowser + agent-browser as the stealth fallback: source-patched stealth Chromium (CloakBrowser) driven over CDP by the agent-browser CLI for clicks, forms, screenshots, video, cookie login, QA/dogfood, Electron apps. Triggers: browse, stealth browser, CloakBrowser, agent-browser, cloak launch, bypass bot detection, blocked site, scrape, fill form, screenshot, import cookies, play youtube, podcast transcript. NOT for simple searches (web-search), fetches (webfetch), or messaging/workspace platforms — Slack/Discord/Notion/Gmail have Tier-1 CLIs in external-communication; come here only as its fallback.

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。