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 agentmods add commands/nmor/the-claude-council/multi-workflowgit clone --depth 1 https://github.com/Nmor/the-claude-councilWrote 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/commands/nmor/the-claude-council/multi-workflow)<a href="https://agentmods.dev/commands/nmor/the-claude-council/multi-workflow"><img src="https://agentmods.dev/badge/commands/nmor/the-claude-council/multi-workflow.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.01685 |
| Opus 5 | $0.00009 | $0.00843 |
| Sonnet 5 | $0.00003 | $0.00337 |
| Haiku 4.5 | $0.00002 | $0.00169 |
Grade A, and why
multi-workflow 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 5d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 5d ago First seen · 196 lines · 17 tokens per session scan A c3a111addf62
multi-workflow is a command published in the GitHub repository Nmor/the-claude-council (9 stars, last pushed 1mo ago), with no licence file. It adds 17 tokens to every session and 1,685 once invoked, about $0.0001 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 commands, from other repositories
deps
Deterministic dependency security bumps: turn the fixable subset of your dependency vulnerabilities into concrete, verified upgrades — planned from the scanners' own fix versions, applied with your repo's own package manager, verified by the correctness floor and the guardrail before anything lands. No LLM anywhere in…
mcp
Serve dxkit's read-only repo tools over the Model Context Protocol, so any MCP-capable coding agent (Claude Code, Cursor, VS Code) can call them natively while working in your repo.
reviewers
Suggest reviewers for a change, grounded on an active-owner model rather than a platform's naive last-touch blame.
prompt-audit
Discover and review LLM prompts in this codebase against the best-practices rubric. Reports findings and proposed diffs in the terminal — never edits without approval.
prompt-init
Initialise the prompt-engineering workspace for this project. Creates docs/prompt-engineering/ with a rules folder, a features folder, and an OVERVIEW of any existing LLM prompts. Run once per project; safe to re-run.
flow-nexus-neural
Train and deploy neural networks in distributed sandboxes.