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 skills/beastlabai/multi-llm-plugin/multi-llmnpx skills add beastlabai/multi-llm-plugin --skill multi-llmgit clone --depth 1 https://github.com/beastlabai/multi-llm-pluginWrote 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/beastlabai/multi-llm-plugin/multi-llm)<a href="https://agentmods.dev/skills/beastlabai/multi-llm-plugin/multi-llm"><img src="https://agentmods.dev/badge/skills/beastlabai/multi-llm-plugin/multi-llm.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 | $0.00146 | $0.05181 |
| Opus 5 | $0.00073 | $0.02590 |
| Sonnet 5 | $0.00029 | $0.01036 |
| Haiku 4.5 | $0.00015 | $0.00518 |
Grade A, and why
multi-llm 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 3d 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 — 395 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-LLM Skill
Skill directory & path resolution — read this first.
Every orchestrator script, instruction file, prompt, schema, and template this skill uses is bundled inside the skill's own directory, whose absolute path is:
${CLAUDE_SKILL_DIR}Claude Code expands
${CLAUDE_SKILL_DIR}to that absolute path inside this SKILL.md before you read it, so every command and path shown below is already fully resolved — run them as written.The mode instruction files you open with the Read tool, and any "run this next" commands the orchestrators print to stdout, are not pre-expanded: they still contain the literal placeholder
CLAUDE_SKILL_DIR(written as the shell-style variable${...}). Whenever you encounter that placeholder in a file you read or in script output, substitute the absolute skill-directory path shown above before running the command or reading the file. The shell does not export this variable, so never run a command that still contains an unexpandedCLAUDE_SKILL_DIR.Always double-quote the substituted skill path in shell commands — write
--project "${CLAUDE_SKILL_DIR}"and"${CLAUDE_SKILL_DIR}/script.py", never the unquoted form. The path may contain spaces (e.g. Windows user profiles likeC:\Users\John Smith\...), and an unquoted expansion word-splits and breaks the command.
A unified skill for multi-LLM plan automation. Supports eleven workflow modes plus a status command:
- Review Plan (
--review-plan): Review an implementation plan with multiple LLMs (default) - Apply Suggestions (
--apply-suggestions): Apply validated suggestions from review to the plan - Generate Tasks (
--generate-tasks): Generate detailed implementation tasks from a high-level plan - Review Tasks (
--review-tasks): Review generated tasks with multiple LLMs - Apply Task Suggestions (
--apply-task-suggestions): Apply validated task review suggestions to tasks.md - Implement (
--implement): Execute implementation tasks from a plan - Review Code (
--review-code): Review code changes against the plan - Apply Code Fixes (
--apply-code-fixes): Apply validated fixes from code review - Full Workflow (
--full): Run all modes in sequence - Status (
--status): Show current workflow state and suggested next action - Ask (
--ask): Ask each model a free-text question about a plan; aggregate answers into one markdown file - Init Config (
--init): Set up a per-project provider config override at<git-root>/.multi-llm/providers.yaml(no plan path; routed viainstructions/init-config.md). Fully automatic and zero-prompt: it auto-detects which provider CLIs are installed onPATHand writes a preconfigured override (uncommenting the detected providers' blocks anddefault_provider);--template-onlyskips detection and writes the inert commented stub. Flags:--dir PATH,--force,--gitignore,--template-only.
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 3.1 KB
- apply_code_fixes_orchestrator.py 39 KB runs code
- apply_suggestions_orchestrator.py 14 KB runs code
- apply_task_suggestions_orchestrator.py 24 KB runs code
- ask_orchestrator.py 36 KB runs code
- assets/icons/Anthropic.svg 584 B
- assets/icons/Codex.svg 1.3 KB
- assets/icons/Cursor.svg 793 B
- assets/icons/GoogleGemini.svg 623 B
- assets/icons/Kilocode.svg 531 B
- assets/icons/MiniMax.png 1.5 KB
- assets/icons/MoonshotAI.svg 957 B
- assets/icons/Nvidia.svg 2.4 KB
- assets/icons/OpenAI.svg 1.7 KB
- assets/icons/Opencode.svg 325 B
- assets/icons/Qwen.svg 1.7 KB
- assets/icons/xAI.svg 343 B
- assets/icons/Zhipu.png 442 B
- check_workflow_prerequisites.py 19 KB runs code
- CLAUDE.md 10 B
- code_review_orchestrator.py 65 KB runs code
- display_decisions.py 8.5 KB runs code
- finalize_tracking.py 4.3 KB runs code
- implement_orchestrator.py 26 KB runs code
- init_config.py 45 KB runs code
- instructions/apply-code-fixes.md 34 KB
- instructions/apply-suggestions.md 34 KB
- instructions/apply-task-suggestions.md 35 KB
- instructions/ask.md 12 KB
- instructions/full-workflow.md 22 KB
- instructions/generate-tasks.md 14 KB
- instructions/implement.md 25 KB
- instructions/init-config.md 6.5 KB
- instructions/review-code.md 22 KB
- instructions/review-plan.md 19 KB
- instructions/review-tasks.md 15 KB
- prompts/ask.txt 2.2 KB
- prompts/code_review.txt 2.9 KB
- prompts/consolidate_suggestions.txt 3.1 KB
- prompts/implementation_task.txt 1.3 KB
- prompts/plan_review.txt 4.8 KB
- prompts/task_review.txt 7.4 KB
- providers.yaml 15 KB
- pyproject.toml 896 B
- references/human-decision-batch.md 14 KB
- references/resume-detection.md 1.5 KB
- references/salvage-handling.md 1.7 KB
- references/uv-check.md 5.4 KB
- references/wave-batching.md 3.4 KB
- review_plan_orchestrator.py 26 KB runs code
- review_tasks_orchestrator.py 8.8 KB runs code
- schemas/code_review_issues.schema.json 1.0 KB
- schemas/consolidated.schema.json 6.5 KB
- schemas/consolidation_batch.schema.json 3.7 KB
- schemas/state_file.schema.json 4.9 KB
- schemas/task_decomposition.schema.json 1.2 KB
- templates/config/providers.override.yaml 13 KB
- templates/consolidated_report_template.html 49 KB
- templates/pr_report_template.html 175 KB
- templates/report_template.html 68 KB
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.
- 3d ago First seen · 395 lines · 0 tokens per session scan A fcc75429e892
multi-llm is a skill published in the GitHub repository beastlabai/multi-llm-plugin (8 stars, last pushed 28d ago), licensed MIT. It adds 146 tokens to every session and 5,181 once invoked, about $0.0007 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.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…