LLM Gateway
BagelHole/DevOps-Security-Agent-Skills
Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
$ npx skills add sickn33/agentic-awesome-skills --skill llm-gateway -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills llm-gateway --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/llm-gateway .claude/skills/llm-gateway && 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 "llm-gateway" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/llm-gateway into .claude/skills/llm-gateway/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-gateway", 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/sickn33/agentic-awesome-skills/tree/main/skills/llm-gatewayType 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 sickn33/agentic-awesome-skills --skill llm-gateway -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills llm-gateway --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/llm-gateway .agents/skills/llm-gateway && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "llm-gateway" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/llm-gateway into .agents/skills/llm-gateway/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-gateway", 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 sickn33/agentic-awesome-skills --skill llm-gateway -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills llm-gateway --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/llm-gateway .cursor/skills/llm-gateway && 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 "llm-gateway" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/llm-gateway into .cursor/skills/llm-gateway/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-gateway", 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/sickn33/agentic-awesome-skills.git --path skills/llm-gateway--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 sickn33/agentic-awesome-skills --skill llm-gateway -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills llm-gateway --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/llm-gateway .gemini/skills/llm-gateway && 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 "llm-gateway" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/llm-gateway into .gemini/skills/llm-gateway/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-gateway", 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 sickn33/agentic-awesome-skills llm-gatewayInstalls 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 sickn33/agentic-awesome-skills --skill llm-gateway -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/llm-gateway .github/skills/llm-gateway && 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 "llm-gateway" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/llm-gateway into .github/skills/llm-gateway/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-gateway", 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 sickn33/agentic-awesome-skills --skill llm-gateway -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills llm-gateway --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/llm-gateway .opencode/skills/llm-gateway && 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 "llm-gateway" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/llm-gateway into .opencode/skills/llm-gateway/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-gateway", 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.
llm-gatewayDeploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
LLM Gateway is an agent skill from sickn33/agentic-awesome-skills. Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.
It sits in Backend & APIs, covering Model routing and gateways, Rate limiting and Cloud networking. It works with Docker, vLLM and OpenAI. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.
Read from SKILL.md and the folder at commit b84d35a. 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.
Shell commands in SKILL.md call:
curldockerFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use curl and docker, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
OPENAI_API_KEYLITELLM_MASTER_KEYANTHROPIC_API_KEYLANGFUSE_PUBLIC_KEYLANGFUSE_SECRET_KEYPOSTGRES_PASSWORDFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled.
From compatibility in the SKILL.md frontmatter.
LLM Gateway loads about 2.1k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 310 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 sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 310 words, ~2,109 tokens.
.claude/skills/llm-gateway/SKILL.md (or your agent's skills folder).A unified API gateway that routes LLM requests across providers and self-hosted models — with rate limiting, cost tracking, caching, and failover.
Use this skill when:
LiteLLM is the de facto open-source LLM gateway with OpenAI-compatible API.
# Run with Docker
docker run -d \
--name litellm-proxy \
-p 4000:4000 \
-e OPENAI_API_KEY=$OPENAI_API_KEY \
-e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
-v $(pwd)/litellm-config.yaml:/app/config.yaml \
ghcr.io/berriai/litellm:main-latest \
--config /app/config.yaml \
--detailed_debug# litellm-config.yaml
model_list:
# OpenAI models
- model_name: gpt-4o
litellm_params:
model: openai/gpt-4o
api_key: os.environ/OPENAI_API_KEY
rpm: 10000
tpm: 2000000
- model_name: gpt-4o-mini
litellm_params:
model: openai/gpt-4o-mini
api_key: os.environ/OPENAI_API_KEY
# Anthropic
- model_name: claude-sonnet-4-6
litellm_params:
model: anthropic/claude-sonnet-4-6
api_key: os.environ/ANTHROPIC_API_KEY
# Self-hosted vLLM instances (load balanced)
- model_name: llama-3.1-8b
litellm_params:
model: openai/meta-llama/Llama-3.1-8B-Instruct
api_base: http://vllm-1:8000/v1
api_key: fake # vLLM key
- model_name: llama-3.1-8b
litellm_params:
model: openai/meta-llama/Llama-3.1-8B-Instruct
api_base: http://vllm-2:8000/v1 # second replica — auto load balanced
api_key: fake
# Fallback: cheap model if primary fails
- model_name: gpt-4o
litellm_params:
model: openai/gpt-4o-mini # fallback to cheaper model
api_key: os.environ/OPENAI_API_KEY
router_settings:
routing_strategy: least-busy # or: latency-based, simple-shuffle
num_retries: 3
retry_after: 5
allowed_fails: 2
cooldown_time: 60
# Fallback configuration
fallbacks:
- gpt-4o: [claude-sonnet-4-6]
- claude-sonnet-4-6: [gpt-4o]
litellm_settings:
# Semantic caching
cache: true
cache_params:
type: redis
host: redis
port: 6379
similarity_threshold: 0.90 # cache if >90% semantic similarity
# Logging
success_callback: ["langfuse"]
failure_callback: ["langfuse"]
langfuse_public_key: os.environ/LANGFUSE_PUBLIC_KEY
langfuse_secret_key: os.environ/LANGFUSE_SECRET_KEY
general_settings:
master_key: os.environ/LITELLM_MASTER_KEY
database_url: postgresql://litellm:password@postgres:5432/litellm
store_model_in_db: trueservices:
litellm:
image: ghcr.io/berriai/litellm:main-latest
command: ["--config", "/app/config.yaml", "--port", "4000"]
volumes:
- ./litellm-config.yaml:/app/config.yaml
ports:
- "4000:4000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
- LITELLM_MASTER_KEY=${LITELLM_MASTER_KEY}
- DATABASE_URL=postgresql://litellm:password@postgres:5432/litellm
depends_on:
postgres:
condition: service_healthy
redis:
condition: service_started
restart: unless-stopped
postgres:
image: postgres:16-alpine
environment:
POSTGRES_DB: litellm
POSTGRES_USER: litellm
POSTGRES_PASSWORD: password
volumes:
- postgres-data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U litellm"]
interval: 5s
retries: 5
restart: unless-stopped
redis:
image: redis:7-alpine
command: redis-server --maxmemory 2gb --maxmemory-policy allkeys-lru
volumes:
- redis-data:/data
restart: unless-stopped
volumes:
postgres-data:
redis-data:# Create a virtual API key for a team (via LiteLLM API)
curl -X POST http://localhost:4000/key/generate \
-H "Authorization: Bearer $LITELLM_MASTER_KEY" \
-H "Content-Type: application/json" \
-d '{
"team_id": "team-backend",
"key_alias": "backend-team-key",
"models": ["gpt-4o-mini", "llama-3.1-8b"],
"max_budget": 100, # USD limit
"budget_duration": "monthly",
"rpm_limit": 100, # requests per minute
"tpm_limit": 500000 # tokens per minute
}'
# View spend
curl http://localhost:4000/spend/keys \
-H "Authorization: Bearer $LITELLM_MASTER_KEY"# nginx.conf — round-robin across vLLM replicas
upstream vllm_backends {
least_conn;
server vllm-1:8000 max_fails=3 fail_timeout=30s;
server vllm-2:8000 max_fails=3 fail_timeout=30s;
server vllm-3:8000 max_fails=3 fail_timeout=30s;
keepalive 32;
}
server {
listen 80;
server_name llm-api.internal;
# Rate limiting
limit_req_zone $http_authorization zone=per_key:10m rate=100r/m;
limit_req zone=per_key burst=20 nodelay;
location /v1/ {
proxy_pass http://vllm_backends;
proxy_http_version 1.1;
proxy_set_header Connection "";
proxy_set_header Host $host;
proxy_read_timeout 300s; # long timeout for streaming
proxy_buffering off; # required for SSE streaming
proxy_cache_bypass 1;
}
}# Check LiteLLM health
curl http://localhost:4000/health
# Model-level health
curl http://localhost:4000/health/liveliness
# Spend by model
curl http://localhost:4000/spend/models \
-H "Authorization: Bearer $LITELLM_MASTER_KEY"
# Active virtual keys
curl http://localhost:4000/key/list \
-H "Authorization: Bearer $LITELLM_MASTER_KEY"| Issue | Cause | Fix |
|---|---|---|
ConnectionRefusedError to backend | Backend not reachable | Check api_base URL; verify backend is healthy |
| Rate limit errors (429) | Budget/RPM exceeded | Increase limits or rotate to fallback model |
| Slow streaming responses | proxy_buffering enabled | Set proxy_buffering off in Nginx |
| Cache miss rate high | Threshold too strict | Lower similarity_threshold to 0.85 |
| Postgres connection errors | DB not ready | Add depends_on with condition: service_healthy |
cache: true with Redis for repeated or similar queries; can cut costs 30–50%.num_retries: 3 with fallbacks to handle provider outages gracefully.least-busy routing strategy for self-hosted models to avoid GPU saturation.vllm-server) - Backend inference serverllm-inference-scaling) - Auto-scaling backendsllm-caching) - Semantic cache patternsllm-cost-optimization) - Cost management© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/llm-gateway of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit b84d35a
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.
LLM Gateway 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 |
|---|---|---|---|---|---|---|
| LLM Gateway this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| LLM GatewayBagelHole/DevOps-Security-Agent-Skills | 1.2k | — | ~2k | Automated safety check: Pass | MIT | |
| Rtvi Vlm Customize ModelNVIDIA/skills | 3.6k | — | ~5k | Automated safety check: Notes | Apache-2.0 | |
| Deepstream SopNVIDIA/skills | 3.6k | — | ~4.7k | Automated safety check: Notes | Apache-2.0 | |
| System Designninehills/skills | 280 | — | ~4.7k | Automated safety check: Pass | MIT | |
| System Designwondelai/skills | 2.4k | — | ~4k | Automated safety check: Pass | MIT |
BagelHole/DevOps-Security-Agent-Skills
Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
NVIDIA/skills
How to swap the VLM in the VSS Alerts Blueprint — covers RTVI-VLM microservice deployment methods, all three VLM consumers (rtvi-vlm, vlm-as-verifier, vss-agent), and health checks.
NVIDIA/skills
A skill your agent uses when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether…
ninehills/skills
Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues.
wondelai/skills
Design scalable distributed systems using structured approaches for load balancing, caching, database scaling, and message queues.
jeremylongshore/tons-of-skills-marketplace
Understand and handle OpenRouter rate limits. An agent skill from jeremylongshore/tons-of-skills-marketplace.
sickn33/agentic-awesome-skills
Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.
sickn33/agentic-awesome-skills
Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.
sickn33/agentic-awesome-skills
Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.
sickn33/agentic-awesome-skills
Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.
sickn33/agentic-awesome-skills
Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.
sickn33/agentic-awesome-skills
Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.
Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking. LLM Gateway is an agent skill from sickn33/agentic-awesome-skills. Deploy an API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.
LLM Gateway fits situations like: tasks that involve Model routing and gateways; tasks that involve Rate limiting; tasks that involve Cloud networking.
Run `npx skills add sickn33/agentic-awesome-skills --skill llm-gateway -a claude-code`. Or copy the skill folder (skills/llm-gateway in sickn33/agentic-awesome-skills) into .claude/skills/llm-gateway in your project. Claude Code loads it when a task matches its description.
Run `npx skills add sickn33/agentic-awesome-skills --skill llm-gateway -a codex`. Or copy the skill folder (skills/llm-gateway in sickn33/agentic-awesome-skills) into .agents/skills/llm-gateway 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 sickn33/agentic-awesome-skills --skill llm-gateway -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llm-gateway, .gemini/skills/llm-gateway, .github/skills/llm-gateway and .opencode/skills/llm-gateway in your project.
Going by SKILL.md and its folder, LLM Gateway needs the command-line tools its instructions call (curl and docker) and credentials named OPENAI_API_KEY, LITELLM_MASTER_KEY, ANTHROPIC_API_KEY and LANGFUSE_PUBLIC_KEY. Our summary lists: Docker; A credential in OPENAI_API_KEY; A credential in ANTHROPIC_API_KEY. Compatibility (from SKILL.md): Requires the relevant OS/platform tooling and privileged access where noted. Docs-only; helper scripts and templates not bundled..
SKILL.md contains no URLs. Its commands use curl and docker, which can reach the network depending on how they are called. 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.
LLM Gateway is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.1k tokens (SKILL.md is roughly 8.4k 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 LLM Gateway: LLM Gateway (BagelHole/DevOps-Security-Agent-Skills, 1.2k stars), Rtvi Vlm Customize Model (NVIDIA/skills, 3.6k stars), Deepstream Sop (NVIDIA/skills, 3.6k stars) and System Design (ninehills/skills, 280 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.
Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.