How do I install the LLM Engineering skill?

Run npx skillmds add willyu1007/llm-engineering in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is LLM Engineering safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with LLM Engineering?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is LLM Engineering free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published LLM Engineering?

willyu1007 (@willyu1007) published this skill. Their other Agent Skills are listed on their SkillMD profile.

LLM Engineering by willyu1007 — Agent Skill

Name: LLM Engineering
Rating: 4.2 (5 reviews)
Author: willyu1007

LLM Engineering

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.

willyu1007 a1e5606 18 files · 55.6 KB Updated Sep 10, 2026

File contents

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)

No secrets in repo
- Use non-secret references (e.g., credential_ref).
Registry-first configuration
- Never introduce new LLM env/config keys ad-hoc.
- Register in .ai/llm-config/registry/config_keys.yaml.
Single calling surface
- Feature code must not import provider SDKs directly.
- Route all calls through one wrapper/gateway client.
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.

---name: llm-engineeringdescription: 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.

Frequently asked questions