routing
Skill123 starsChanged 31 days agoMITView on GitHub
Route tasks to the cheapest capable model automatically using llm_router MCP tools
13 other projects use a version of this.
Adopted by
Most recently taken up by
- AnyOrgName/php-skillstheir own version, first seen Nov 8, 2026
- github/replicatheir own version, first seen Nov 1, 2026
- darknecrocities/SkillBuildertheir own version, first seen Nov 1, 2026
- EONRaider/agent-routertheir own version, first seen Oct 20, 2026
- ThomasLangbroek/frugaltheir own version, first seen Oct 13, 2026
- zprolab/WhaleKittheir own version, first seen Oct 13, 2026
Used with
Projects with it also use parallelization, prompt-chaining and 1 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
hajekimSkillparallelization
This skill should be used when the user wants to "run tasks in parallel", "concurrent LLM calls", "fan-out and fan-in", "parallel agents", "simultaneous execution", "reduce latency with concurrency", "parallel sub-tasks", "parallel processing", "concurrent agent execution", "run agents simultaneously", "speed up with parallelism", "async agent execution", "batch LLM calls", "parallel workflow", "parallelize agent tasks", or needs to execute multiple independent tasks simultaneously. Also responds to Korean: "병렬 작업 실행", "동시 LLM 호출", "팬아웃 팬인 패턴", "병렬 에이전트", "동시 실행", "동시성으로 지연 시간 감소", "병렬로 실행", "동시에 여러 작업", "병렬 처리", "동시에 돌려줘", "빠르게 병렬로", "여러 작업 동시 실행해줘". Also responds to Japanese: "並列処理", "同時実行", "並列エージェント", "並行LLM呼び出し", "ファンアウト・ファンイン", "複数タスクを同時に実行したい", "処理速度を上げたい", "非同期エージェント", "バッチ処理", "並列で動かしたい" Also responds to Chinese: "并行处理", "并发执行", "并行智能体", "同时调用多个LLM", "扇出扇入模式", "多任务并行运行", "加快处理速度", "异步智能体", "批量LLM调用", "同时运行多个任务".. Apply this skill to design or implement the Parallelization agentic design pattern.
Also in 3 projects that use this
- hajekimSkill
prompt-chaining
This skill should be used when the user wants to "chain prompts", "build a pipeline", "break down complex tasks into steps", "sequential LLM calls", "multi-step reasoning", "pipeline pattern", "sequential agent pipeline", "multi-step prompt pipeline", "LLM chain", "step-by-step agent", "prompt pipeline", "decompose task into prompts", "stage-by-stage processing", "chained LLM calls", or when a task is too complex for a single prompt and needs to be decomposed into sequential sub-tasks. Also responds to Korean: "프롬프트 체이닝", "체인 방식 에이전트", "단계별 LLM 호출", "복잡한 작업 분해", "순차적 프롬프트 파이프라인", "단계적 작업 처리", "단계별 처리", "순차 처리 에이전트", "파이프라인 만들어줘", "단계적으로 처리해줘", "연속 프롬프트", "체이닝 해줘". Also responds to Japanese: "プロンプトチェーニング", "ステップごとに処理", "順次LLM呼び出し", "パイプライン構築", "複雑なタスクを分解", "段階的エージェント", "連続プロンプト", "LLMパイプライン", "順番に処理してほしい", "チェーン方式エージェント" Also responds to Chinese: "提示词链", "顺序调用LLM", "构建流水线", "分解复杂任务", "逐步处理", "链式提示", "多步骤流水线", "按顺序处理任务", "提示词管道", "帮我一步一步处理".. Apply this skill to design, implement, or explain the Prompt Chaining agentic design pattern.
Also in 3 projects that use this
NVIDIASkilltesting
Run Python and Rust tests for physicsnemo-curator using uv + pytest with coverage reporting via pytest-cov, and cargo-nextest for Rust tests. Includes benchmark workflows with pytest-benchmark and criterion.
Also in 3 projects that use this
Other versions13 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 13
- github/replica★ 230same name
- ThomasLangbroek/frugal★ 37same name
- darknecrocities/SkillBuilder★ 13same name
- flpbalada/fb-skills★ 8same name
- AnyOrgName/php-skills★ 4same name
- hajekim/agentic-design-patterns-skills★ 4same name
- jkaninda/okapi-skills★ 3same name
- ffsshhttiikk/opencode-agents-skills★ 2same name
- EmmittJ/guild★ 1same name
- hajekim/agentic-design-patterns-extension★ 1same name
3 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository4 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- releaseSkill
- routeSkill
- savingsSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 12 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
Before answering research, code, writing, or analysis tasks — call the appropriate llm_router tool instead of answering directly.
- Very specific: line 47
Run llm_savings anytime to see your totals.
“Common” is measured: kept by at least 6 in 10 of the 12 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
LLM Router — Smart Routing Skill
Route tasks to the cheapest capable model automatically using llm_router MCP tools.
When to Use This Skill
Before answering research, code, writing, or analysis tasks — call the appropriate llm_router tool instead of answering directly. The router picks the cheapest model that can handle the task (Ollama → Codex → paid APIs in free-first order).
Task → Tool Mapping
| Task type | Tool to call | Why |
| Simple factual question | llm_query | Gemini Flash / Groq — 50× cheaper than o3 |
| Research / current events | llm_research | Perplexity (web-grounded, not stale) |
| Writing / summaries / brainstorm | llm_generate | Gemini Flash / Haiku |
| Deep analysis / debugging | llm_analyze | GPT-4o / Gemini Pro |
| Code generation / refactoring | llm_code | Ollama → Codex built-in → o3 |
| Don't know which type | llm_auto | Auto-classifies + routes, tracks savings |
How to Call
llm_query(prompt="What is the capital of France?")
llm_code(prompt="Refactor this function to use async/await", complexity="moderate")
llm_research(prompt="What changed in Python 3.13?")
llm_auto(prompt="<the full user request>") # safest defaultWhen NOT to Route
- File read/write/edit operations — execute directly
- Inline tool calls where you have the answer already
- Tasks under 5 words (likely follow-ups, not standalone requests)
Cost Impact
Routing simple tasks to Gemini Flash instead of o3 saves ~50–100×.
llm_auto shows cumulative savings every 5 calls automatically.
Run llm_savings anytime to see your totals.
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