Borrowing it
Nothing to install: this file belongs to delorenj/mcp-server-trello. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/delorenj/mcp-server-trello/main/.agents/skills/bmad-party-mode/SKILL.mdgit clone --depth 1 https://github.com/delorenj/mcp-server-trelloWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/delorenj/mcp-server-trello/bmad-party-mode)<a href="https://agentmods.dev/skills/delorenj/mcp-server-trello/bmad-party-mode"><img src="https://agentmods.dev/badge/skills/delorenj/mcp-server-trello/bmad-party-mode/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/delorenj/mcp-server-trello/bmad-party-mode"><img src="https://agentmods.dev/badge/skills/delorenj/mcp-server-trello/bmad-party-mode.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.01892 |
| Opus 5 | $0.00030 | $0.00946 |
| Sonnet 5 | $0.00012 | $0.00378 |
| Haiku 4.5 | $0.00006 | $0.00189 |
Grade A, and why
bmad-party-mode scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- bmad-party-mode — 100% identical, 0 lines differ
- bmad-party-mode — 100% identical, 0 lines differ
- bmad-party-mode — 100% identical, 0 lines differ
- bmad-party-mode — 100% identical, 0 lines differ
- bmad-party-mode — 100% identical, 0 lines differ
- bmad-party-mode — 88% identical, 27 lines differ
- bmad-party-mode — 84% identical, 23 lines differ
- bmad-party-mode — 84% identical
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Party Mode
Facilitate roundtable discussions where BMAD agents participate as real subagents — each spawned independently via the Agent tool so they think for themselves. You are the orchestrator: you pick voices, build context, spawn agents, and present their responses. In the default subagent mode, never generate agent responses yourself — that's the whole point. In --solo mode, you roleplay all agents directly.
Why This Matters
The whole point of party mode is that each agent produces a genuinely independent perspective. When one LLM roleplays multiple characters, the "opinions" tend to converge and feel performative. By spawning each agent as its own subagent process, you get real diversity of thought — agents that actually disagree, catch things the others miss, and bring their authentic expertise to bear.
Arguments
Party mode accepts optional arguments when invoked:
--model <model>— Force all subagents to use a specific model (e.g.--model haiku,--model opus). When omitted, choose the model that fits the round: use a faster model (likehaiku) for brief or reactive responses, and the default model for deep or complex topics. Match model weight to the depth of thinking the round requires.--solo— Run without subagents. Instead of spawning independent agents, roleplay all selected agents yourself in a single response. This is useful when subagents aren't available, when speed matters more than independence, or when the user just prefers it. Announce solo mode on activation so the user knows responses come from one LLM.
On Activation
-
Parse arguments — check for
--modeland--soloflags from the user's invocation. -
Load config from
{project-root}/_bmad/core/config.yamland resolve:
- Use
{user_name}for greeting - Use
{communication_language}for all communications
-
Resolve the agent roster by running:
python3 {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} --key agents
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 129 lines · 59 tokens per session scan A bd13fd6cb0b0
bmad-party-mode is a skill published in the GitHub repository delorenj/mcp-server-trello (437 stars, last pushed 13d ago), licensed MIT. It adds 59 tokens to every session and 1,892 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…