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 raine/consult-llm --skill collab-vsgit clone --depth 1 https://github.com/raine/consult-llmWrote 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/raine/consult-llm/collab-vs)<a href="https://agentmods.dev/skills/raine/consult-llm/collab-vs"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/collab-vs/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/raine/consult-llm/collab-vs"><img src="https://agentmods.dev/badge/skills/raine/consult-llm/collab-vs.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 | $0.00034 | $0.01657 |
| Opus 5.5 | $0.00014 | $0.00663 |
| Sonnet 5.5 | $0.00007 | $0.00331 |
| Haiku 4.5 | $0.00003 | $0.00166 |
Grade A, and why
collab-vs 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.
Brainstorm collaboratively with a partner LLM, building on each other's ideas in alternating turns, then synthesize the best ideas into a plan.
Load the consult-llm skill before proceeding — it defines the invocation contract (stdin heredoc, flags, output format, multi-turn). Do not call the CLI without loading it first.
Available models
Selectors resolvable in this environment (depends on configured API keys):
!`consult-llm models`
Arguments: $ARGUMENTS
Check the arguments for flags:
Partner flag (exactly one required): any --<selector> from the Models block above (e.g. --gemini, --openai, --deepseek). Translates to -m <selector> for the CLI.
Strip all flags from arguments to get the task description.
Set variables from the partner flag:
MODEL: the selector (e.g.gemini,openai)PARTNER: the same selector, used as the display label
If no --<selector> flag is provided, ask the user which partner to use,
listing the selectors from the Models block.
Phase 0: Load consult-llm Skill
Load it now. Follow its invocation contract for all CLI calls in this workflow.
Phase 1: Understand the Task (No Questions)
-
Explore the codebase - use Glob, Grep, Read to understand:
- Relevant files and their structure
- Existing patterns and conventions
- Dependencies and interfaces
Before planning or consulting, do enough research to understand how the requested behavior actually works. Before starting, think about what resources would be useful to obtain first: relevant source files, tests, logs, generated files, config, examples, command output, external docs, or authoritative upstream source. Gather the cheapest useful evidence before forming a plan.
Do not stop at the first plausible file, definition, setting, or example. Follow references, callers, related tests, and runtime usage until you can explain the current behavior and the likely impact of changing it.
-
Ground external semantics before planning - understand the requested behavior in the real system, not just this repo
- If the task depends on an external product, CLI, API, protocol, file format, or ecosystem convention, verify the relevant behavior using the cheapest authoritative evidence available: local binaries/flags, generated files, official docs, public source, package/library code, or web search.
- Capture only decision-relevant facts that affect scope, acceptance criteria, compatibility, or implementation constraints.
- Do not create a separate research artifact unless the evidence materially changes the plan.
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 · 34 tokens per session scan A 03266432432a
collab-vs is a skill published in the GitHub repository raine/consult-llm (142 stars, last pushed 14d ago), licensed MIT. It adds 34 tokens to every session and 1,657 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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