Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/DNYoussef/context-cascadenpx agentmods add skills/dnyoussef/context-cascade/llm-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/skills/dnyoussef/context-cascade/llm-council)<a href="https://agentmods.dev/skills/dnyoussef/context-cascade/llm-council"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/llm-council/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/dnyoussef/context-cascade/llm-council"><img src="https://agentmods.dev/badge/skills/dnyoussef/context-cascade/llm-council.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 | $0.00019 | $0.01076 |
| Opus 5.5 | $0.00008 | $0.00430 |
| Sonnet 5.5 | $0.00004 | $0.00215 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
llm-council 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Council Skill
LIBRARY-FIRST PROTOCOL (MANDATORY)
Before writing ANY code, you MUST check:
Step 1: Library Catalog
- Location:
.claude/library/catalog.json - If match >70%: REUSE or ADAPT
Step 2: Patterns Guide
- Location:
.claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md - If pattern exists: FOLLOW documented approach
Step 3: Existing Projects
- Location:
D:\Projects\* - If found: EXTRACT and adapt
Decision Matrix
| Match | Action |
|---|---|
| Library >90% | REUSE directly |
| Library 70-90% | ADAPT minimally |
| Pattern exists | FOLLOW pattern |
| In project | EXTRACT |
| No match | BUILD (add to library after) |
Purpose
Run 3-stage multi-model consensus for critical decisions where:
- Single-model hallucination risk is unacceptable
- Multiple perspectives improve decision quality
- High-stakes choices need validation
Architecture (Karpathy Pattern)
STAGE 1: COLLECT
+---> Claude ---> Response A
|
Query --+---> Gemini ---> Response B
|
+---> Codex ----> Response C
STAGE 2: RANK
Each model reviews others (anonymized)
Produces rankings with rationale
STAGE 3: SYNTHESIZE
Chairman aggregates rankings
Produces final answer with consensus score
When to Use
Perfect For:
- Architecture decisions
- Technology selection
- Critical bug triage
- Security assessment
- High-risk deployments
- Contentious design choices
Don't Use When:
- Simple, low-risk decisions
- Time-critical responses
- Single correct answer exists
- Cost is a concern (3x API usage)
Usage
Basic Council
/llm-council "Should we use microservices or monolith for this system?"
With Threshold
/llm-council "Which auth approach is best?" --threshold 0.75
With Chairman Override
/llm-council "Architecture decision" --chairman gemini
Command Pattern
bash scripts/multi-model/llm-council.sh "<query>" "<threshold>" "<chairman>"
Configuration
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 · 190 lines · 19 tokens per session scan A d9b95f9cb644
llm-council is a skill published in the GitHub repository DNYoussef/context-cascade (32 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 1,076 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 2026-09-03.
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