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 tokenbender/agent-guides --skill multi-mindgit clone --depth 1 https://github.com/tokenbender/agent-guidesWrote 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/tokenbender/agent-guides/multi-mind)<a href="https://agentmods.dev/skills/tokenbender/agent-guides/multi-mind"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/multi-mind/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/tokenbender/agent-guides/multi-mind"><img src="https://agentmods.dev/badge/skills/tokenbender/agent-guides/multi-mind.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 | $0.00028 | $0.01105 |
| Opus 5.5 | $0.00011 | $0.00442 |
| Sonnet 5.5 | $0.00006 | $0.00221 |
| Haiku 4.5 | $0.00003 | $0.00111 |
Grade A, and why
multi-mind 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 1mo 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Mind - Subagent-Based Collaborative Analysis
Execute a multi-specialist collaborative analysis on: $ARGUMENTS
Parse an optional rounds=N parameter (default 3).
How this maps to Claude Code's agent system
- Launch each specialist as a parallel subagent via the Task tool — they run concurrently, each in its own context window, and return only their analysis.
- For one-off runs, inline subagent prompts (below) are enough. For topics you revisit, make the specialists persistent by writing definitions to
.claude/agents/<role>.md(see the Subagents Guide) — Claude will then route to them by description automatically. - Subagents run in the background by default; launch all specialists for a round in a single message so they execute in parallel.
- If the question needs codebase-wide changes (not just analysis), the bundled
/batchskill is a better fit. For long-running collaborative sessions, see agent teams in the Claude Code docs.
When to use this vs. /deep-research: the bundled /deep-research workflow fans out web searches and synthesizes a cited report — use it for factual questions. Use multi-mind for judgment questions (architecture tradeoffs, strategy, design decisions) where decorrelated expert perspectives and adversarial cross-pollination matter more than citation coverage.
Phase 1: Specialist Assignment & Research
Analyze the topic and determine 4-6 specialist roles with maximally decorrelated perspectives. Then launch them all in parallel, e.g.:
Task tool, subagent_type: general-purpose — run all in one block:
1. "You are a technical specialist. Research [topic] focusing on implementation
details, architecture, performance, and technical risk. Use web search for
current documentation and case studies. Return ≤500 words of findings."
2. "You are a business strategy specialist. Analyze [topic] from market dynamics,
competitive landscape, ROI, and strategic positioning. Use web search for
current market reports. Return ≤500 words."
3. "You are a user experience / adoption specialist. Investigate [topic] for
user needs, usability concerns, and adoption barriers. Return ≤500 words."
[... additional specialists as the topic demands — security, regulatory,
ethics, operations, contrarian/red-team ...]
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.
- 1mo ago First seen · 115 lines · 28 tokens per session scan A 67069d57189f
multi-mind is a skill published in the GitHub repository tokenbender/agent-guides (367 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 28 tokens to every session and 1,105 once invoked, about $0.0001 per session on Opus 5.5. 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.
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