AI Engineering

AI Engineering

Prescriptive guidance for building and reviewing LLM/AI applications. Thirteen orthogonal concerns — load only the rules the current task needs.

This SKILL.md is a thin index. Detailed rules live in rules/*.md and load on demand. Curated source URLs live in references/primary-sources.md. Date-flagged changes since 2024 live in references/recent-changes.md. Literal scaffolding lives in templates/.

Mode Detection

Parse $ARGUMENTS (first token) and detect the mode:

State the detected mode and the area(s) in scope before continuing:

Mode: review
Areas: prompt-writing, system-prompt-design
Targets: src/agents/triage.ts (system prompt at L24-78)

Area Routing

Map the user's request to one or more rule files. Load only the rules listed for the matched area(s).

evals.md covers product-quality measurement (golden sets, LLM-as-judge). testing.md covers engineering-correctness tests (mocks, VCR, snapshots). Load both when the user asks "how do I test this?" without specifying.

Observability composition. When the area is Observability & versioning and the task involves OTEL wiring or attribute naming, invoke the dash0 OTEL skills via Skill() before answering — they hold the source of truth for spans and gen_ai.* semconv:

• Skill("otel-instrumentation", ...) — SDK setup, exporters, span shape.
• Skill("otel-semantic-conventions", ...) — attribute naming and spec validation.

If neither skill is in the available-skills list, fall back to the inline guidance in rules/observability-and-versioning.md and the OTEL spec.

If the user does not name an area, ask one batched clarifying question listing the thirteen options before loading rules.

Workflow

guide (default)

1. Identify the area(s) from the user's request (use the table above).
2. Load the matched rule file(s).
3. Answer using the rules. Cite the source URLs from references/primary-sources.md when a claim is non-obvious or model-version-specific.
4. If the rule references a date-flagged change, also load references/recent-changes.md and call out the date in the answer.

review

1. Read the target file(s) supplied in $ARGUMENTS.
2. Detect which areas the file touches (prompt strings, system prompts, tool schemas, retrieval pipelines, eval scripts, …).
3. Load the matching rule file(s).
4. Produce findings as a numbered list, each with:
   • What — the issue, with path:line.
   • Rule — the rule file + section that's violated.
   • Fix — concrete change, with a code snippet when ≤ 10 lines.
5. End with a "Top 3 fixes" prioritised by impact.

Do not edit the file in review mode unless the user asks for fixes.

design

1. Confirm the artefact: prompt, system prompt, or eval rubric?
2. Ask the user — in one message — for:
   • The task the prompt drives.
   • The model family (Claude / GPT / Gemini / open).
   • Any hard constraints (latency budget, output schema, refusal scope).
   • Whether tool use is in scope.
3. Load the relevant rule(s) and the matching template:
   • System prompt → templates/system-prompt-skeleton.md.
   • Tool definition → templates/tool-description.md.
   • Eval rubric → templates/eval-rubric.md.
   • Golden-set seed → templates/golden-set.md.
4. Fill in the template. Annotate why each section exists (one inline comment per section, not a full essay).

Required Reading by Area

Load on demand — do not preload.

Core Principles

1. Stable-prefix everything. The single highest-leverage optimisation across cost, latency, and determinism is keeping the prompt prefix byte-identical across calls so it caches. Order: tools → system → stable context → volatile context → user input.
2. Match the technique to the model class. Reasoning models (o3, o4, Claude with adaptive thinking) regress under added chain-of-thought; non-reasoning models often need it. See rules/prompt-writing.md.
3. Workflows beat agents until they don't. A predefined control flow is cheaper, faster, and more debuggable than a model-driven loop. Build an agent only when the trajectory genuinely cannot be enumerated.
4. Evals before optimisation. You cannot tell whether a prompt change is an improvement without a golden set + regression run. Without traces in production, you have no eval inputs.
5. Defence-in-depth for prompt injection. OWASP LLM01:2025 ranks prompt injection #1. Layer input classifier + least-privilege tools + human approval on destructive actions + output validator. No single guardrail is sufficient — guardrail LLMs are themselves injectable.
6. Right-size the model. Route 60% Haiku-class / 30% Sonnet-class / 10% Opus-class with confidence-gated escalation. Uniform-Opus is rarely the right answer.
7. Prompts are code. Versioned in-repo, diffed in PR, gated by CI evals, A/B released. Anonymous prompt edits in production dashboards are a regression waiting to happen.
8. Pin model snapshots in production. Aliases auto-upgrade silently — the model that passed evals yesterday is not the model serving traffic today. See rules/model-migration.md.
9. Plan for the failure modes, not the happy path. Rate limits, provider outages, and tail latency hit every prod system. Honour Retry-After, jitter retries, fall back across models, key destructive tool calls for idempotency. See rules/resilience.md.

Anti-patterns (one-liners — full coverage in each rule)

• "Be helpful and detailed" instructions — non-actionable.
• Volatile content (timestamps, user IDs) before stable content — kills caching.
• Adding "let's think step by step" to a reasoning model — measurable regression.
• Flipping tools, tool_choice, or thinking mid-conversation — silent cache flush.
• Vector-only retrieval (no BM25) — misses exact-string matches (IDs, error codes).
• Passing the top-20 chunks to the LLM without reranking — wastes tokens, dilutes attention.
• Single-judge LLM evals using the same model as the actor — silent self-preference inflation.
• Tool descriptions written for humans — agents pick the wrong tool.
• Building a multi-agent system before single-agent + workflow exhausts the design space.
• Toggling extended thinking mid-turn — invalidates the entire cached prefix.
• Sending full-resolution screenshots — resize to ≤ 1568 px first.
• Retrying on 429 without honouring Retry-After — wastes quota and triggers thundering herds.
• Pure exponential backoff with no jitter — every client retries at the same instant.
• Side-effectful tool calls without idempotency keys — retries double-charge.
• Model aliases (claude-sonnet-4-7) in production — auto-upgrade silently.
• Migrating models without a full golden-set run — regressions ship undetected.
• Truncating long conversations instead of summarising — drops context the model needs.
• Cross-user memory leakage — let the model fill in user_id and you have a privacy bug.

Definition of Done

• Mode and areas stated up front.
• Only the matched rule file(s) loaded.
• Each finding cites path:line and the rule it violates.
• Source URLs cited when claims are model-version-specific or recent.
• In design mode, the produced artefact uses the matching template.
• In review mode, findings are prioritised; no edits without consent.