Shogun Bloom Config
yohey-w/multi-agent-shogun
Interactive wizard: guided questions with multiple-choice options about subscriptions, then outputs a ready-to-paste capabilitytiers YAML + fixed agent model assignments.
Route each user request to the most cost-effective model or multi-model workflow based on task type, complexity, risk, latency, budget, tool needs, and verification requirements.
$ npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills production-model-router --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/model-routing-orchestrator .claude/skills/production-model-router && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "production-model-router" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestrator into .claude/skills/production-model-router/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "production-model-router", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestratorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills production-model-router --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/model-routing-orchestrator .agents/skills/production-model-router && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "production-model-router" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestrator into .agents/skills/production-model-router/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "production-model-router", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills production-model-router --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/model-routing-orchestrator .cursor/skills/production-model-router && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "production-model-router" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestrator into .cursor/skills/production-model-router/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "production-model-router", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/model-routing-orchestrator--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills production-model-router --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/model-routing-orchestrator .gemini/skills/production-model-router && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "production-model-router" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestrator into .gemini/skills/production-model-router/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "production-model-router", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills production-model-routerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/model-routing-orchestrator .github/skills/production-model-router && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "production-model-router" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestrator into .github/skills/production-model-router/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "production-model-router", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills production-model-router --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/model-routing-orchestrator .opencode/skills/production-model-router && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "production-model-router" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/model-routing-orchestrator into .opencode/skills/production-model-router/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "production-model-router", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
production-model-routerRoute each user request to the most cost-effective model or multi-model workflow based on task type, complexity, risk, latency, budget, tool needs, and verification requirements.
Production Model Router is an agent skill from LeoYeAI/openclaw-master-skills. Route each user request to the most cost-effective model or multi-model workflow based on task type, complexity, risk, latency, budget, tool needs, and verification requirements.
Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).
It sits in AI & LLM Engineering, covering Model routing and gateways. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
12 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Production Model Router loads about 4.9k tokens when it runs. Until then it costs about 51 tokens; SKILL.md has 2,504 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 2,504 words, ~4,874 tokens.
.claude/skills/production-model-router/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Use this skill to decide which model tier, workflow shape, and verification strategy should handle a user's request.
The goal is to maximize cost-effectiveness without sacrificing task fit, correctness, or operational reliability.
This skill does not blindly choose the strongest model. It chooses the cheapest safe path that still meets the quality bar for the task.
It may recommend:
For every request, choose the minimum-cost execution path that can still satisfy:
Use this skill when you need to decide:
Do not use this skill to:
Collect or infer the following from the request and system context:
Classify the request into one or more of these categories:
Simple generation
General reasoning
Deep reasoning
Exact calculation or formal logic
Coding and technical execution
Long-context synthesis
Multi-modal tasks
High-risk tasks
Always prefer the cheapest path that can safely succeed.
Apply this order of preference:
Do not escalate unless the task characteristics justify it.
Use abstract capability tiers unless the deployment specifies exact providers.
Use for:
Strengths:
Weaknesses:
Use for:
Strengths:
Weaknesses:
Use for:
Strengths:
Weaknesses:
Use when exactness matters more than fluent wording.
Use this path for:
Rule: When a task requires exact numeric correctness, prefer tools plus model orchestration over pure model reasoning.
Score the request across these dimensions:
Apply these rules before any soft optimization.
If the task involves exact arithmetic, formulas, tables, accounting-like operations, unit-sensitive conversions, or step-sensitive logic:
If the task is high-risk:
If the task is materially ambiguous and the answer quality depends on interpretation:
If the input is large or multi-document:
If the task includes images, diagrams, PDFs with layout dependence, or visual interpretation:
For code tasks:
Choose one of these workflow shapes.
Use when:
Examples:
Use when:
Examples:
Use when:
Examples:
Use when:
Pattern:
Use when:
Pattern:
Examples:
Use when:
Pattern:
Best for:
Use when:
Pattern:
Best for:
Use when:
Pattern:
Best for:
Use these strategies to keep cost high-value.
Escalate to a stronger model or multi-step workflow when any of these appear:
Use a cheaper path when:
When the request includes complex calculations or formal reasoning:
Never use a fluent but non-verified freeform model answer as the final authority for exact numeric work when a deterministic path exists.
When the request includes large context:
Return exactly this structure:
Routing Decision: <single-economy | single-balanced | single-premium | tool-assisted | staged-pipeline | draft-and-review | parallel-comparison | consensus-check>
Primary Reason:
<one-sentence explanation of why this route is the best cost-effective choice>
Task Profile:
<task category>Recommended Execution Plan:
Model Role Assignment:
Why This Is Cost-Effective:
Escalation Rule:
<when to move to a stronger model or more expensive workflow>
Fallback Rule:
<what to do if the chosen path fails or produces uncertainty>
Request: "Rewrite this email to sound more professional."
Output: Routing Decision: single-economy
Primary Reason: This is a low-risk rewriting task where a low-cost model should meet the quality bar.
Task Profile:
Recommended Execution Plan:
Model Role Assignment:
Why This Is Cost-Effective:
Escalation Rule: Escalate only if the user requests highly nuanced persuasion, legal sensitivity, or multiple tone variants.
Fallback Rule: If the first rewrite is too generic, retry once with stricter tone instructions using a balanced model.
Request: "Design an AI agent architecture for long-form writing with memory, retrieval, conflict resolution, and chapter planning."
Output: Routing Decision: single-premium
Primary Reason: This task requires deep reasoning, abstraction, and multi-part architecture tradeoff analysis.
Task Profile:
Recommended Execution Plan:
Model Role Assignment:
Why This Is Cost-Effective:
Escalation Rule: Escalate to parallel-comparison only if multiple competing architecture strategies are explicitly required.
Fallback Rule: If the first answer is too generic, rerun with a staged outline-first then full-design workflow.
Request: "Calculate the projected monthly burn and runway under three hiring scenarios and explain the assumptions."
Output: Routing Decision: tool-assisted
Primary Reason: This request depends on exact calculations, so deterministic computation is safer and more cost-effective than pure model reasoning.
Task Profile:
Recommended Execution Plan:
Model Role Assignment:
Why This Is Cost-Effective:
Escalation Rule: Escalate to premium review only if the scenario assumptions are ambiguous or strategically sensitive.
Fallback Rule: If tool-based computation is unavailable, return assumptions explicitly and mark the result as unverified instead of presenting certainty.
Request: "Compare these five candidate proposals and recommend the best one based on cost, timeline, technical fit, and risk."
Output: Routing Decision: staged-pipeline
Primary Reason: The request is large and decomposable, so staged extraction and synthesis is more cost-effective than sending everything directly to a premium model.
Task Profile:
Recommended Execution Plan:
Model Role Assignment:
Why This Is Cost-Effective:
Escalation Rule: Escalate to consensus-check if the recommendation will drive a major decision or if proposal differences are subtle.
Fallback Rule: If extraction quality is poor, rerun the extraction stage with a stronger model before recomputing the final recommendation.
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in skills/model-routing-orchestrator of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Production Model Router next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Production Model Router this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.9k | Automated safety check: Pass | MIT | |
| Shogun Bloom Configyohey-w/multi-agent-shogun | 1.4k | — | ~3.1k | Automated safety check: Pass | MIT | |
| Codemie Analyticscodemie-ai/codemie-code | 294 | — | ~7.5k | Automated safety check: Pass | Apache-2.0 | |
| Model Routernidhi-singh02/agent-router | 112 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Codex Model Routing Teamzjp1997720/codex-model-routing-team | 158 | — | ~736 | Automated safety check: Pass | MIT | |
| Add Modelget-convex/convex-evals | 130 | — | ~1.5k | Automated safety check: Notes | Apache-2.0 |
yohey-w/multi-agent-shogun
Interactive wizard: guided questions with multiple-choice options about subscriptions, then outputs a ready-to-paste capabilitytiers YAML + fixed agent model assignments.
codemie-ai/codemie-code
CodeMie Analytics expert — use this skill whenever the user asks about CodeMie usage data, AI adoption metrics, user leaderboards, CLI insights, spending, LiteLLM costs, token usage, or wants to…
nidhi-singh02/agent-router
A skill your agent uses when the user asks to pick a model, subscription, or reasoning effort, or to run router status, usage refresh, or resume a router session.
zjp1997720/codex-model-routing-team
在 Codex App 中为复杂、可并行的知识工作或编程任务自动创建多个可指定模型与推理强度的后台任务,由主 Agent 负责规划、分工、集成和验收。用于多来源调研、多章节内容、复杂 Skill/PPT、跨模块开发、独立验证或 2 个以上互不依赖工作流;也用于用户明确要求模型路由、后台 Worker、Agents Team…
get-convex/convex-evals
Add a new model to the convex-evals coding leaderboard, and optionally the decision benchmark, through a PR, then dispatch its baseline runs.
diegosouzapw/OmniRoute
Creates and runs LLM evaluation suites from the omniroute CLI, follows live runs, shows scorecards, compares models and ties eval runs into CI.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Categories
Route each user request to the most cost-effective model or multi-model workflow based on task type, complexity, risk, latency, budget, tool needs, and verification requirements. Production Model Router is an agent skill from LeoYeAI/openclaw-master-skills. Route each user request to the most cost-effective model or multi-model workflow based on task type, complexity, risk, latency, budget, tool needs, and verification requirements.
Production Model Router fits situations like: tasks that involve Model routing and gateways.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a claude-code`. Or copy the skill folder (skills/model-routing-orchestrator in LeoYeAI/openclaw-master-skills) into .claude/skills/production-model-router in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a codex`. Or copy the skill folder (skills/model-routing-orchestrator in LeoYeAI/openclaw-master-skills) into .agents/skills/production-model-router in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill production-model-router -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/production-model-router, .gemini/skills/production-model-router, .github/skills/production-model-router and .opencode/skills/production-model-router in your project.
SKILL.md names no scripts, command-line tools or credentials: Production Model Router is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Production Model Router is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.9k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Production Model Router: Shogun Bloom Config (yohey-w/multi-agent-shogun, 1.4k stars), Codemie Analytics (codemie-ai/codemie-code, 294 stars), Model Router (nidhi-singh02/agent-router, 112 stars) and Codex Model Routing Team (zjp1997720/codex-model-routing-team, 158 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.