llm-engineering

name	llm-engineering
description	Entry workflow for LLM engineering tasks (provider integration, calling wrapper, routing profiles, prompt templates, cost/telemetry, credentials/config keys). Routes to one procedure and enforces required verification.

LLM Engineering (workflow)

Operating mode (token-efficient)

• Treat the llm-engineering skill as a router + governor.
• Do not load multiple procedures. Select exactly one procedure below and follow it end-to-end.
• Use .ai/llm-config/registry/* as the SSOT for identifiers and allowed config keys.

Routing (pick one procedure)

Task	Open the procedure
Add / integrate a provider	reference/procedures/add-provider.md
Standardize or introduce a single calling wrapper	reference/procedures/standardize-calling-wrapper.md
Add / change model routing profiles	reference/procedures/add-model-profile.md
Add / change prompt templates (versioned)	reference/procedures/update-prompt-template.md
Release / major change review	reference/procedures/release-check.md

Examples (end-to-end)

Open one example only if you need a concrete template.

Example	Open
Add a provider (OpenAI-style)	reference/examples/e2e-01-add-provider-openai.md
Update a prompt template (versioned)	reference/examples/e2e-02-update-prompt-template.md
Add a model routing profile	reference/examples/e2e-03-add-model-profile.md

Shared non-negotiables (apply to all procedures)

1. No secrets in repo

   • Use non-secret references (e.g., credential_ref).

2. Registry-first configuration

   • Never introduce new LLM env/config keys ad-hoc.
   • Register in .ai/llm-config/registry/config_keys.yaml.

3. Single calling surface

   • Feature code must not import provider SDKs directly.
   • Route all calls through one wrapper/gateway client.

4. Versioned prompts

   • Prompt references are prompt_template_id + immutable version.

Minimal inputs you should capture before changing code

• Intended user-facing capability (what feature needs LLM?)
• Required modalities/APIs (chat, embeddings, streaming)
• Routing intent (quality vs cost vs latency)
• Credential strategy (credential_ref; no secrets)
• Budget constraints (tokens/$) and telemetry fields

Verification

• If you changed skills:

  • node .ai/scripts/lint-skills.mjs --strict
  • node .ai/scripts/sync-skills.mjs --scope current --providers both --mode reset --yes

• Registry sanity (recommended; run before release):

  • node .ai/skills/workflows/llm/llm-engineering/scripts/validate-llm-registry.mjs
  • In real (non-template) repos, run with --strict in CI.

• If you changed LLM config/env keys (or introduced new ones):

  • node .ai/skills/workflows/llm/llm-engineering/scripts/check-llm-config-keys.mjs

• If the host repo has tests/lint:

  • run the smallest relevant test suite for the modified area (wrapper/adapter/registry).

Boundaries

• Do not edit .codex/skills/ or .claude/skills/ directly (generated).
• Do not add MCP-related content (out of scope for this workflow).
• Do not create new top-level directories for LLM work; keep assets under existing conventions (.ai/, .system/).
• Do not add provider SDK calls directly into product/feature code.