last30days-v3-spec
Internal architecture spec for the v3 last30days runtime pipeline. Not user-invocable.
Internal architecture spec for the v3 last30days runtime pipeline. Not user-invocable.
Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.
Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.
Use for GPU-accelerated machine learning on tabular data using NVIDIA cuML. Triggers when tasks involve classification, regression, clustering, dimensionality reduction, or model training on datasets.
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' 'who is my target audience,' 'describe my product,' 'ICP,' 'ideal customer profile,' or wants to avoid repeating foundational information across marketing tasks. Use this at the start of any new project before using other marketing skills — it creates `.agents/product-marketing-context.md` that all other skills reference for product, audience, and positioning context.
Add one or more AI models to the language model constants file, researching specs from official docs.
ElevenLabs audio generation — text-to-speech, voice cloning, and sound effects. Use this skill any time the agent needs to: convert text to spoken audio, narrate documents or content, generate voiceovers, clone voices from audio samples, create sound effects, or produce any audio output from text. Supports multiple voices, languages, models, voice cloning, batch processing, and sound effect generation. Requires ELEVENLABS_API_KEY.
Create professional logos through an intelligent, iterative design process. Use this skill when the user wants to create a logo, icon, favicon, brand mark, wordmark, or any visual brand identity mark. Triggers on: 'create a logo', 'design a logo', 'make me a logo', 'logo for my brand', 'I need a logo', 'brand mark', 'wordmark', 'logomark', 'icon design', 'favicon'. This is NOT a one-shot image generator — it researches, strategizes, generates symbols with AI, visually inspects every output, then programmatically composes them with real Google Fonts typography into complete logo systems (logomark, wordmark, combination marks in multiple layouts).
Discover, compare, and run AI models using Replicate's API. Use this skill whenever the task involves AI-generated media — images (text-to-image, style transfer, editing, upscaling, background removal), video, audio, or any other ML model output. Requires a REPLICATE_API_TOKEN — ask the user for it if not already set.
Installs NemoClaw, launches a sandbox, and runs the first agent prompt. Use when onboarding, installing, or launching a NemoClaw sandbox for the first time.
Analyze Walmart sales data to explore trends between store sales and unemployment rates. Generate insightful visualizations and a beautiful HTML report with deep analysis. Suitable for quick insights into the relationship between sales data and macroeconomic factors.
Gemini CLI for one-shot Q&A, summaries, and generation. Use when the user asks to query Gemini, generate text with Google AI, summarize content using Gemini, run a one-shot prompt, get a Gemini response, or produce JSON output via the Gemini CLI. Supports model selection, output formatting, and extension management.
Batch-generate images via the OpenAI Images API using DALL-E 2, DALL-E 3, or GPT image models. Produces random-but-structured prompts, renders them, and outputs a browsable `index.html` gallery. Use when the user asks to generate AI images, create pictures with DALL-E, batch-produce image assets, render AI art, or build an image gallery from text prompts.
Transcribe audio via OpenAI Audio Transcriptions API (Whisper). Use when the user wants to transcribe, convert speech to text, extract words from audio or voice recordings, generate a transcript from an audio file, or perform speech recognition on m4a, ogg, or wav files using the Whisper model.
Local speech-to-text with the Whisper CLI (no API key). Use when the user needs to transcribe audio, convert speech to text, generate subtitles, translate spoken language, or produce SRT/VTT captions from mp3, m4a, or wav files using the local Whisper model.
The agent writes and improves fuzzing harnesses — the entrypoint functions that receive random data from fuzzers and route it to the system under test (SUT). It implements LLVMFuzzerTestOneInput for C/C++ with libFuzzer and AFL++ persistent mode, fuzz_target! macros for Rust with cargo-fuzz and the arbitrary crate, and go-fuzz Fuzz functions for Go. The agent structures inputs using FuzzedDataProvider, applies interleaved fuzzing patterns for multi-operation targets, handles input size validation, resets global state for determinism, and mocks blocking I/O. It applies this technique when creating new fuzz targets, improving code coverage of existing harnesses, fixing non-reproducible crashes, or building structure-aware harnesses with Protocol Buffers.
The agent uses LibAFL, a modular Rust fuzzing library, to build custom fuzzers with fine-grained control over observers, feedback mechanisms, mutators, schedulers, and executors. It supports drop-in libFuzzer replacement mode via libFuzzer.a, fully custom fuzzer construction with InProcessExecutor and coverage-guided feedback, multi-core fuzzing with Launcher, crash deduplication via BacktraceObserver, and dictionary-based token mutations. The agent applies LibAFL when standard fuzzers like libFuzzer or AFL++ lack needed customization — such as custom mutation strategies, novel feedback mechanisms, non-standard target architectures, or fuzzing research requiring component-level control over the fuzzing loop, corpus management, and sanitizer integration.
Initiates, manages, and inspects voice calls through the Otto voice-call plugin using Twilio, Telnyx, Plivo, or mock providers. Supports starting outbound calls, continuing conversations, speaking messages, ending calls, and checking call status. Use when the user wants to make a phone call, dial a number, place a voice call, check call status, send a voice message, or speak to someone over the phone.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.