feature-dev
claudeComprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
1 command · 3 agents
Build custom audiences in minutes by blending your subject-matter expertise and first-party data with raw signals. Do what used to take teams of data engineers, scientists, and analysts, using Signal Graph. Traverse petabytes of raw signals in seconds to find your alpha.
Plugin watt from marketplace watt in wattdata/plugin. Commands read from the repo's manifest — 1 month ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
audience-activateExport a built audience as platform-ready files — confirms the platforms, the scale, and the identifier types with the user, then materializes the audience once on this thread, batch by batch, and runs each platform's deterministic writer script, returning the finished files and honest row counts. C
audience-analyze-listRead who a supplied list of people is — take a list of people as identifiers (emails, phones, names, addresses, inline or as a CSV) to resolve, or as already-resolved entity IDs (a roster from grouping, or a pasted entity-ID set), and render the discovered half of the read (the traits that define th
audience-analyze-searchRead who a market is, starting from a plain-English brief — discover → pivot → read. The discovery-first way into audience-analyze, behind /watt:audience; size is an output, never a target. Aggregates only — never individual records, never an export. Not a user command. Use when a read-shaped ask ar
audience-analyze-signalRead who a built audience reaches when the signals are already in hand — a signal stack fresh from generate, an explore pool, a pasted audience record, or signals the user names — skip discovery, materialize, and render the two-section read (your signals + discovered). The signals-in-hand way into a
audience-analyzeRead who a built audience reaches, as aggregates over a deterministic sample — and, on request, a shareable signal membership report. The read step behind /watt:audience; routes to the way in that fits what the user has — a brief, signals they already hold, or a list of people. Aggregates only — nev
audience-generate-listBuild a person audience from an owned list — customers, leads, accounts — resolving it to Watt entity IDs and emitting a roster, or learning what defines it and handing back a tunable signal pool. The list-anchored way into a build, behind /watt:audience. Four plays — resolve-only, expand (household
audience-generate-searchBuild a person audience from a plain-English description — discover the signals behind the brief, score them, and compose, partition, or cross them into a measured result. The discovery-driven way into a build, behind /watt:audience; anchored on a description (no owned list). The objective picks how
audience-generateGenerate a person audience from a plain-English brief — discover the signals behind it, score them with the math visible, and compose them into an audience whose reach is measured. The build step behind /watt:audience; routes to the leaf that fits how the audience should be built. Produces a signal
audienceTurn an idea into an actual audience. Build a list of people — to a size you pick, the widest reach, or the highest-intent few — profile a market to see who's in it and how large it is, read who an audience reaches, or export it as a platform-ready file for Meta, Google, Reddit, or TikTok. Or start
configureSet up Watt in Claude, or fix it when it stops working. A guided check that confirms you're on a supported surface, verifies the Signal Graph connection and file downloads for you, and walks you through turning on automatic updates — fixing anything that's off, one step at a time. Run it the first t
exploreFind out what audience signals exist for an idea about people. Describe who you're trying to reach in plain English and see what's out there — which signals Watt has, how big and how fresh each one is, and what related angles are worth a look. A guided, one-question-at-a-time walk, so you get the la
helpGet unstuck with Watt without leaving Claude — ask what you can do and how, figure out why something isn't working, see what's changed and whether you're on the latest, or tell the team something — report a bug, request a signal or feature, reach a human, and track what you've filed. It answers your
Signal Graphconnects to: https://api.wattdata.xyz/v2/mcp
bash "${CLAUDE_PLUGIN_ROOT}/hooks/session-start-context.sh"
python3 "${CLAUDE_PLUGIN_ROOT}/hooks/call-shape-guard.py"
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
1 command · 3 agents
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Automates browser interactions for web testing, form filling, screenshots, and data extraction
12 skills
Install the "AAS Data Analytics & Business Intelligence" workflow plugin from Agentic Awesome Skills.
16 skills
The official CCXT MCP server plus the ccxt-mcp usage skill — market data, balances and opt-in trading across 100+ crypto exchanges and prediction markets, with keys kept local.
1 MCP server
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
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