「swarm」の検索結果

The consumer panel. Spins up a swarm of buyer sub-agents (default 100, --quick for 20) trained on the user's target customer, each with a different income, age, buying behaviour and objection, shows every one the same pitch, and runs the business through 100 buyer scenarios to see who buys, who doesn't, and why. Tallies the answers into buy rates by segment, the reasons people pass in their own words, what would flip a no, and price answers for founder-pricing. Use when the user says "would people buy this", "test it on customers", "run the consumer panel", "simulate buyers", "who is my customer", or "validate demand".

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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。

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

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。

15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

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。

Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.

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。

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

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。

15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

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。

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. A top-tier commander (Fable or Opus) orchestrates the swarms; the user picks a power level (Max Power / Heavy / Balanced / Economy) that sets the Opus/Sonnet/Haiku model mix per layer. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

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。

Create original short videos using the 重击对轻群体反制｜诱饵让位、群体绕行与武器缠束 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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。

Create original short videos using the 地面召集天空编队｜近距确认、首领降落与乘骑离场 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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。

Operate a running Guaardvark: GPU and VRAM state, plugin start/stop, logs, Celery tasks, the Interconnector sync to other machines, overnight RAG autoresearch, and infographics. Use when the user asks what is using the GPU, why a job is stuck, to start or stop ComfyUI/Audio Foundry/Swarm, to sync another box, to run autoresearch, or to make an infographic.

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。

Connect this agent to a running Guaardvark (self-hosted AI studio) and check what it can do right now. Use first when the user mentions Guaardvark, asks to generate images/video/music/voice locally, run a Film Crew, train a LoRA, launch a swarm, or when a guaardvark MCP tool is missing or a call fails with "not reachable" / "plugin offline".

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。

Think and work like an expert Geomagnetist. Use when a task calls for Geomagnetist judgment. Reasons from spherical-harmonic main-field theory and remanence physics through IGRF-14/WMM2025 vs CHAOS-8 SV, INTERMAGNET baseline adoption (IBFV2.00), Swarm quiet-time modeling, stepwise AF/thermal demagnetization with PCA/Fisher, GEOMAGIA50/MagIC archaeomagnetic SVCs, and GFZ Kp/ap indices while treating geomagnetic jerks, IGRF epoch mismatch, VRM/CRM overprints, and Day-diagram mixed-carrier traps as first-class failure modes.

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。

Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.

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。

Dokploy is an open-source, self-hosted platform (an alternative to Heroku, Vercel and Netlify) that deploys applications, Docker Compose stacks and databases on your own server with Docker Swarm and Traefik, a web dashboard, automatic Let's Encrypt certificates and an HTTP API. Use when a user asks to "install Dokploy on a VPS", "deploy an app with Dokploy", "deploy a docker-compose project to Dokploy", "add a domain with HTTPS", "trigger a Dokploy deployment from CI", "create a Postgres database in Dokploy" or "set up zero-downtime deploys".

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。

Design and implement agent-based models (ABM) for simulating complex systems with emergent behavior from individual agent interactions. Use when "agent-based, multi-agent, emergent behavior, swarm simulation, social simulation, crowd modeling, population dynamics, individual-based, " mentioned.

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。

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

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。

Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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。

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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。

Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).

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。

Multi-repository coordination, synchronization, and architecture management with AI swarm orchestration

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。

15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.

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。

Rigorous engineering orchestrator ported from Lauren Tan's pstack (poteto-mode): reads your task, picks one of 23 playbooks (bug fix, feature, refactoring, perf, investigation, prototype, babysit, shipping, autonomous run, orchestrate, and more), routes to bundled procedures (how, why, architect, arena, swarm, interrogate, unslop, technical-writing, show-me-your-work, tdd, and others), and applies 24 engineering principles. Self-contained: no plugin install, no sibling skills required, works with any agent that reads skills.sh-format SKILL.md files. Use whenever a task needs rigor: nontrivial code changes, architecture decisions, debugging, reviews, PRs, long autonomous runs, or "work like poteto", "poteto-mode", "pstack".

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。

Designs and runs agent hives and multi-agent waves. Use when setting hive roles, code gates, dependency-graph fan-out, or verifier, vote and loop-until-dry passes.

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。