Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Pattyboi101/oats-autonomous-agents --skill meetinggit clone --depth 1 https://github.com/Pattyboi101/oats-autonomous-agentsWrote 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/pattyboi101/oats-autonomous-agents/meeting)<a href="https://agentmods.dev/skills/pattyboi101/oats-autonomous-agents/meeting"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/meeting/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/pattyboi101/oats-autonomous-agents/meeting"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/meeting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00101 | $0.03401 |
| Opus 5 | $0.00051 | $0.01700 |
| Sonnet 5 | $0.00020 | $0.00680 |
| Haiku 4.5 | $0.00010 | $0.00340 |
Grade A, and why
meeting 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 429 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting — Phase-Based Agent Debate
You are the chair. Run a real meeting — a state machine, not a survey. Agents diverge ideas, challenge each other, build on what survives, and stress test before anything becomes an action. The chair navigates phases autonomously, can skip optional ones, repeat phases generating value, or loop back when a later phase reveals something that needs earlier work.
Before Starting
- Are departments online?
list_peers— need at least 3 for a useful meeting - Is there an open meeting already? Check
meetings/— don't duplicate - What specific decision needs to be made? Clarify before creating the file
How This Skill Works
- Mode 1: Start Meeting — topic provided → create file, run Diverge, facilitate through phases until satisfied or closed
- Mode 2: Close Meeting — user says "close" → extract actions, write briefings, broadcast summary
- Mode 3: Mid-Meeting Injection — user adds a point mid-discussion → route to relevant agents as targeted prompt without restarting the phase
Phase System
| Phase | Purpose | Mandatory? |
|---|---|---|
| Diverge | All ideas on the table — no criticism yet | Yes |
| Clarify | Surface assumptions, define terms, resolve ambiguity | Optional |
| Challenge | Push back, expose tensions, stress-test assumptions | Yes |
| Build | Develop the strongest ideas that survived Challenge | Yes |
| Stress Test | Attack the emerging consensus — try to break it | Yes |
| Converge | Find genuine agreement, surface remaining gaps | Optional |
| Decide | Lead agent resolves anything still genuinely unresolved | Only if unresolved |
Minimum required path: Diverge → Challenge → Build → Stress Test
[SATISFIED] and [CLOSE MEETING] flags are ignored until Stress Test completes. Collect them, note them, do not act on them.
Agent Flags
Agents write these in their responses to signal navigation needs:
| Flag | Meaning | Chair Action |
|---|---|---|
[NEEDS CLARIFY: X] |
X needs defining before I can contribute fully | Insert Clarify phase |
[BACK TO CHALLENGE: reason] |
Build surfaced a new tension that needs challenging | Loop to Challenge |
[BACK TO BUILD: reason] |
Stress Test revealed a better approach worth developing | Loop to Build |
[NEEDS RESEARCH: X] |
I need current data on X before proceeding | Chair searches, prepends findings to next phase |
[SATISFIED] |
Nothing more to add — happy with direction | Count; close if all satisfied (after Stress Test) |
[CLOSE MEETING: reason] |
Lead/CEO: meeting has run its course | Close immediately (only after Stress Test) |
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.
- 12d ago First seen · 429 lines · 101 tokens per session scan A 665675411071
meeting is a skill published in the GitHub repository Pattyboi101/oats-autonomous-agents (6 stars, last pushed 3mo ago), licensed MIT. It adds 101 tokens to every session and 3,401 once invoked, about $0.0005 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 2026-08-31.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
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…