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Configures TrueFoundry AI Gateway end-to-end. Covers unified OpenAI-compatible LLM access, provider account integrations, content safety guardrails, and observability (traces, costs, errors) via the spans query API.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/truefoundry/skills --skill truefoundry-gateway명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | truefoundry-gateway |
| description | Configures TrueFoundry AI Gateway end-to-end. Covers unified OpenAI-compatible LLM access, provider account integrations, content safety guardrails, and observability (traces, costs, errors) via the spans query API. |
| license | MIT |
| compatibility | Requires Bash, curl, and access to a TrueFoundry instance |
| allowed-tools | Bash(*/tfy-api.sh *) Bash(curl*) Bash(python*) |
Routing note: For ambiguous user intents, use the shared clarification templates in references/intent-clarification.md.
Configure and operate TrueFoundry's AI Gateway: unified OpenAI-compatible LLM access, provider account integrations, content safety guardrails, and request monitoring/observability.
platform skill (Status Check section)mcp-servers skillplatform skill (Secrets section)observability skillobservability skillStart from the official template: truefoundry/custom-guardrails-template. Build on top of it, then deploy.
Your App -> AI Gateway -> OpenAI / Anthropic / Azure / Self-hosted vLLM / etc.
^
Unified API + Auth + Rate Limiting + Routing + Logging
Key benefits: Single endpoint for all models, one API key (PAT/VAT), OpenAI-compatible, rate limiting, budget controls, load balancing with fallback, guardrails, and full observability.
{TFY_BASE_URL}/api/llm
PAT (Personal Access Token): Dashboard -> Access -> Personal Access Tokens. For development.
VAT (Virtual Access Token): Dashboard -> Access -> Virtual Account Tokens. For production (not tied to a user, supports granular model access).
tfy-secret:// references, never raw values.tfy-secret:// URI. Or have them set it via ! export TFY_API_KEY=... so it stays in the shell.Verify tfy login is complete. If missing, stop and use truefoundry-onboard.
Set TFY_API_SH for direct API calls:
TFY_API_SH=~/.claude/skills/truefoundry-gateway/scripts/tfy-api.sh
For read-only questions, go straight to the API. Do not explore CLI subcommands — they don't exist. See references/cli-reference.md.
| User asks | Single call |
|---|---|
| What models/providers are attached? | $TFY_API_SH GET /api/svc/v1/provider-accounts |
| What models can I call? | curl -s "${TFY_BASE_URL}/api/llm/models" -H "Authorization: Bearer ${TFY_API_KEY}" |
| What guardrails are configured? | $TFY_API_SH GET /api/svc/v1/gateway-guardrails-configs |
| Show recent gateway requests | $TFY_API_SH POST /api/svc/v1/spans/query '{"startTime":"...","dataRoutingDestination":"default","limit":20,"sortDirection":"desc"}' |
| Is the gateway reachable? | curl -s "${TFY_BASE_URL}/api/llm/health" |
After login is confirmed, the next step for any read question is the API call above — nothing else.
The gateway is OpenAI-compatible. Minimal example:
curl "${TFY_BASE_URL}/api/llm/chat/completions" \
-H "Authorization: Bearer ${TFY_API_KEY}" \
-H "Content-Type: application/json" \
-d '{"model": "openai/gpt-4o", "messages": [{"role": "user", "content": "Hello!"}], "max_tokens": 200}'
Or set environment variables for any OpenAI SDK:
export OPENAI_BASE_URL="${TFY_BASE_URL}/api/llm"
export OPENAI_API_KEY="<your-PAT-or-VAT>"
For complete SDK examples (Python, Node.js, streaming), supported APIs table, framework integrations (LangChain, LlamaIndex, Cursor), and routing/rate-limiting/budget configuration, see references/calling-models.md.
$TFY_API_SH GET /api/svc/v1/provider-accounts
Present as a formatted table with name, provider, type, and model count (from integrations array length).
platform skill, Secrets section)$TFY_API_SH POST /api/svc/v1/provider-accounts "$payload"Supported providers: OpenAI, AWS Bedrock, Google Vertex, Azure OpenAI, Groq, Together AI, Custom (any OpenAI-compatible), Self-Hosted, TrueFoundry.
tfy apply -f manifest.yaml --dry-run --show-diff
tfy apply -f manifest.yaml
Do NOT delegate gateway applies to a deployment skill. Gateway configs are applied inline with tfy apply.
Guardrails add content safety controls. Setup requires two steps:
Quick list: $TFY_API_SH GET /api/svc/v1/gateway-guardrails-configs
For full setup instructions, rule structure, API calls, and common patterns, see references/guardrails-setup.md.
Supported providers reference: references/guardrail-providers.md.
Query gateway request traces via the spans API and aggregate usage via the metrics API. Requires either tracingProjectFqn or dataRoutingDestination for trace queries; suggest "default" as a starting point when the user does not know the destination.
$TFY_API_SH POST /api/svc/v1/spans/query '{
"dataRoutingDestination": "default",
"startTime": "2026-05-15T00:00:00.000Z",
"limit": 20,
"sortDirection": "desc"
}'
Present results as formatted tables (time, model, status, tokens, cost, latency, user).
For all monitoring use cases (cost analysis, errors, model usage, user filtering, MCP tool calls, metadata filtering), filter types, response structure, and pagination, see references/monitoring.md.
Use this path for aggregate questions such as:
$TFY_API_SH POST /api/svc/v1/llm-gateway/metrics/query '{
"startTs": "...", "endTs": "...",
"datasource": "modelMetrics",
"type": "distribution",
"aggregations": [{"type": "sum", "column": "costInUSD"}],
"groupBy": ["modelName"]
}'
When answering a time-range question, calculate exact startTs and endTs, state the range used, and present totals in a compact table. If the user asks for monthly breakdowns, run one query per month unless the API exposes a time-bucket field.
For any gateway entity or policy:
tfy-secret:// for credentialstfy apply -f manifest.yaml --dry-run --show-difftfy apply -f manifest.yaml| Entity | Reference |
|---|---|
| Provider accounts | references/provider-templates.md |
| Virtual models / routing | references/calling-models.md (Virtual Models section) |
| Rate limiting | references/schemas/rate-limiting.md |
| Budget controls | references/schemas/budget-limiting.md |
| Guardrails | references/guardrails-setup.md |
| Observability tables | references/observability.md |
<success_criteria>
tfy-secret:// for all credentialsdataRoutingDestination or tracingProjectFqn asked before querying</success_criteria>
platform skill (Secrets section) -> then tfy-secret:// URImcp-servers skillobservability skill (Tracing section)API key (PAT/VAT) is invalid or expired. Check Authorization: Bearer <token> header.
Token lacks access to this model. PATs inherit user permissions; VATs only access explicitly selected models.
Model name not in gateway. Check exact name via dashboard -> AI Gateway -> Models or GET /api/llm/models.
Wait and retry (check Retry-After header). Request higher limits or use load balancing.
Upstream provider issue. Gateway auto-retries/fallbacks if routing is configured. Check provider status page.
User needs provider-account-manager role. Check collaborators on the provider account.
tfy-secret:// path cannot resolve. Verify format: tfy-secret://TENANT:SECRET_GROUP:SECRET_KEY. Use platform skill to check secret group exists.
The type query parameter on GET /api/svc/v1/provider-accounts does NOT filter. Fetch all and filter client-side.
Use a different name or update the existing account.
Verify: provider account created successfully, integration has correct model_types, collaborators include team:everyone or relevant users.
Check: time range is correct, dataRoutingDestination exists, filters aren't too restrictive, gateway has received requests.
Missing required parameter. Ensure you provide tracingProjectFqn or dataRoutingDestination, and a valid startTime in ISO 8601.