Agent Skills: LLM Integration Code Review Patterns

LLM Integration Code Review Patterns

Stack-specific rules loaded by dh:code-reviewer when prompt files, model selection logic, or evaluation harness code are detected.

Prompt Hygiene

• System prompt must be separated from user content — mixing them in a single string removes the security boundary
• System prompts must not include user-controlled content unless that content is explicitly sanitized and bounded
• Prompt templates must use structured variable substitution, not string concatenation — f-strings with raw user input are a blocking finding
• Long prompts should be stored in dedicated files, not inline strings — inline multi-line strings are acceptable only below 10 lines

Model Selection

• Model tier must match task complexity — using Opus for tasks that Haiku can handle is a blocking finding (cost regression)
• Using Haiku for tasks requiring multi-step reasoning, architecture decisions, or complex judgment is a blocking finding (quality regression)
• Model selection must be documented with the rationale — model = "haiku" # retrieval only, no reasoning required
• Model names must not be hardcoded as full version strings — use the tier alias (sonnet, haiku, opus) so upgrades require one change

# WRONG: hardcoded version string
model = "claude-haiku-4-5"

# RIGHT: tier alias — version resolved by the client
model = "claude-haiku-latest"
# or better: configurable
model = config.model_tier  # "haiku" | "sonnet" | "opus"

Context Management

• Unbounded context accumulation (appending all messages without a limit) is a blocking finding — long sessions will silently hit context limits and start dropping messages
• Sliding window or summarization strategy must be implemented for conversations expected to exceed ~50 turns
• Token count must be tracked and logged — silent context truncation is harder to debug than explicit overflow handling

Token Economics

• Token count must be estimated before sending requests in batch or high-volume operations — surprise cost overruns from unexpectedly large inputs are preventable
• Fail fast on oversize inputs rather than truncating silently — silent truncation corrupts the task without surfacing an error
• Structured output requests (JSON mode) reduce token waste from freeform formatting — use when parsing responses programmatically

Structured Output Validation

• Model responses used programmatically must be validated against a schema before use — json.loads(response) without validation is a blocking finding
• Schema validation must produce a specific error that includes the raw response for debugging — swallowing parse errors is a blocking finding
• Partial response handling: when streaming, the validation schema must tolerate incomplete JSON until the stream closes

Temperature and Sampling

• Temperature setting must be documented with the rationale
• temperature=0 is required for deterministic tasks (classification, extraction, code generation with tests) — any other value is a blocking finding
• temperature>0 is required for creative tasks (variation generation, brainstorming) — using 0 eliminates variation intentionally

# RIGHT: documented temperature
response = client.messages.create(
    model="claude-sonnet-latest",
    temperature=0,  # deterministic — this is a classification task
    messages=[...]
)

Evaluation

• Prompt changes must be accompanied by regression tests that verify the old expected outputs still hold
• Evaluation datasets must be versioned alongside the prompts that were evaluated against them
• Evaluation harness must run in CI — manual evaluation without automation is a blocking finding for production prompts

Safety

• User-controlled content must not be passed as the system prompt — only static, developer-controlled content belongs in the system prompt
• PII (names, email addresses, IP addresses, financial data) must not be passed to external model APIs without documented user consent and data retention agreements
• Prompt injection vectors — places where user content could override or escape the intended prompt structure — must be identified and bounded

Retry Logic

• Retry logic must use exponential backoff with jitter — fixed-interval retries exacerbate rate limit pressure
• Retry on 429 (rate limited) with backoff is correct
• Retry on 400 (bad request, context length exceeded) is a blocking finding — these errors are not transient and retrying wastes budget
• Maximum retry count must be bounded — infinite retry loops are a blocking finding

Streaming

• Streaming clients must handle partial responses — a streaming handler that only processes the final concatenated string is acceptable but wastes the streaming benefit
• Connection drops must be handled explicitly — unhandled streaming errors that silently return empty results are a blocking finding
• Timeout on first token is separate from timeout on stream completion — both must be configured

Anti-Patterns

# WRONG: user input in system prompt
system = f"You are a helpful assistant. The user's name is {user_name}."

# RIGHT: user data in user turn only
system = "You are a helpful assistant."
messages = [{"role": "user", "content": f"My name is {user_name}. ..."}]

# WRONG: retry on context limit
for attempt in range(3):
    try:
        return client.messages.create(...)
    except APIError:  # catches 400 context limit AND 429 rate limit
        time.sleep(2 ** attempt)

# RIGHT: only retry transient errors
for attempt in range(3):
    try:
        return client.messages.create(...)
    except RateLimitError:
        time.sleep(2 ** attempt + random.random())
    except APIError:
        raise  # non-retryable — propagate immediately