Omh LLM App Dev

Llm App Dev

This is a Hermes-native llm-app-dev workflow skill.

Why This Exists

llm-app-dev exists because the failure modes of an LLM feature are not the failure modes of the code around it. A floating model alias, a prompt buried in a string literal, an output scraped out of prose with a regex, and a retrieval layer nobody measured all pass code review and all fail in production, and without a golden set nobody can tell whether the next prompt edit helped or hurt.

Do Not Use When

• The subject is comparing executors or agent harnesses - Codex against Claude Code against Hermes coding - rather than evaluating the product's own model calls; use agent-evaluation.
• An agent run is already stuck, looping, or drifting and needs diagnosis; use agent-debug.
• The subject is the harness's own context window, prompt caching, or token budget rather than the application being built; use context-budget-review.
• The request is a prompt-injection, secret-handling, or dependency risk gate on work that already exists; use security-safety-review.
• The feature makes no model call - the LLM is only mentioned as the subject being discussed - so this is a direct answer, not a build handoff.
• The ask is training the weights themselves on your own data -- SFT, DPO, RLVR, or a LoRA adapter; use model-finetuning.

Examples

Good example:

• Prompt: $llm-app-dev we are adding an invoice-field extractor that calls a model per upload - set it up so we can change the prompt later without guessing.
• Expected behavior: Name the rails, put the provider call behind one client module with a pinned model ID, declare the extraction schema and the repair path, lay the prompt out as a versioned file, and specify the golden set and validators that let the next prompt edit be compared against this baseline.
• Why: The feature is a real model call whose output another system consumes, which is exactly where an unpinned model, an inline prompt, and a missing golden set become expensive later.

Bad example:

• Prompt: $llm-app-dev the extractor is done - confirm the new prompt is better than the old one.
• Expected behavior: Prepare the paired baseline-vs-candidate comparison and state that no result exists until the run is observed; report nothing about which prompt is better.
• Why: Better is a claim about an observed run. Without one, the comparison is a design, and calling it a result is the false-green this workflow exists to prevent.

Completion Checklist

• Every rail - provider boundary, structured output, prompt artifacts, retrieval grounding, evaluation - is either decided or explicitly deferred with a reason.
• One client boundary owns the model ID, credentials, timeout, retry, and backoff, and no credential appears in source, prompts, tests, or examples.
• The model ID is exact, and it is recorded next to any result meant to be compared.
• Every model response is validated against a declared schema, with a bounded repair path and a loud failure - no prose scraping.
• Prompts are files with a version identifier, and system rules, task instruction, and injected context are separated.
• Untrusted retrieved or user-supplied content is fenced from the instruction channel and cannot change the task.
• The eval deliverables - golden set, task-level validators, baseline-vs-candidate comparison - exist as committed artifacts, and retrieval is evaluated before generation when retrieval is in the path.
• Token, latency, and cost figures come from an observed run or stay null; no design output is reported as an eval result, implementation, review, CI, or merge evidence.
• If the feature communicates through a public board, the destination carries a public-audience label, each action class names its own authority and outbound data, the exact draft and its host-recorded approval reference travel with the request through compaction and executor handoff, and no publication is reported without an observed connector result.

Recovery Notes

• If the exact model ID or provider is not decided yet, name the candidates and prepare the boundary against a config value rather than choosing one silently.
• If no failing case can be stated, the golden set has no seed: collect the real failures first, because a golden set written from imagination measures the imagination.
• If a response cannot be made to satisfy the schema after one bounded repair, treat that as a schema or prompt defect and record it as a golden-set case rather than loosening validation.
• If retrieval quality was never measured, stop before scoring generation and route the retrieval evaluation first; a generation score on unmeasured retrieval is not attributable.
• If the comparison run did not emit tokens or cost, leave those fields null and say the harness did not report them; never reconstruct them from pricing tables.
• If a public-board send returned no confirmed outcome, do not retry: read the board back or resolve the receipt first, because a duplicate public post cannot be withdrawn the way a failed private write can be repeated.

Workflow Lane

• Current lane: Coding handoff (idea-to-deploy, llm-app-dev, cto-loop, deploy-and-monitor, code-review, build-failure-triage, verification-gate, security-safety-review, +28 more) - coding owners, handoffs, review, CI, and merge evidence.
• If intent belongs to another lane, hand back to oh-my-hermes or name the adjacent workflow.
• Shared product, routing, compatibility, and evidence rules: omh-routing/references/skill-common-rail.md.

Use When

Use when the work is building or hardening an LLM-powered feature - provider calls, structured outputs, prompt files, retrieval grounding, a user-requested public-board communication path, or the eval suite that guards a prompt or model swap - and the request needs engineering discipline before a coding handoff.

Strong routing signals: `llm-app-dev`, `$llm-app-dev`, `llm app development`, `llm application development`, `build an llm app`, `build an llm feature`, `llm feature development`, `build a rag pipeline`, `rag pipeline`, `retrieval augmented generation`, `structured output schema`, `json schema output`, `prompt versioning`, `llm eval suite`, `golden set`, `LLMアプリ開発`, `LLM機能開発`, `RAGパイプライン構築`, `構造化出力スキーマ`, `プロンプトのバージョン管理`, `LLM評価セット`, `llm 앱 개발`, `llm 애플리케이션 개발`, `llm 기능 개발`, `rag 파이프라인`, `rag 파이프라인 구축`, `구조화된 출력 스키마`, `프롬프트 버전 관리`, `llm 평가셋`, `골든셋`, `大模型应用开发`, `检索增强生成`, `结构化输出模式`, `提示词版本管理`, `评测集`

Catalog Metadata

Category: delivery Phase: llm-app-dev Hermes role: operator Quality tier: delivery-gated Reasoning demand: heavy

Quality bar:

• Decide the rails in order - provider boundary, structured output, prompt artifacts, retrieval grounding, evaluation - and say which are deferred rather than leaving them unnamed. Load references/build-rails.md for the per-rail decision and its failure mode.
• Route every provider call through one client boundary module that owns the model ID, credentials, timeout, retry policy, and rate-limit backoff. A second call site that builds its own client is how a model pin, a timeout, and a retry policy quietly diverge.
• Pin the exact model ID as a named constant or config value, never a floating alias, and record it next to any result that will be compared to another result.
• Take structured output from a declared schema - a JSON schema, a typed parser, or the provider's structured-output mode - and validate every response against it. A response that fails validation is repaired by one bounded re-ask that shows the validation error, then fails loudly; it is never regex-scraped out of prose.
• Keep prompts as reviewable files with a version identifier, separated into system rules, task instruction, and injected context, so a prompt change shows up in a diff instead of inside a string literal.
• For retrieval, fix chunking and citation grounding first and evaluate retrieval before evaluating generation: a generation score on top of unmeasured retrieval cannot tell a bad answer from a bad document set.
• Ship the eval suite as a deliverable, not a follow-up: golden set, task-level validators, baseline-vs-candidate comparison, with deterministic validators wherever the task allows one. Load references/eval-harness.md for the golden-set shape, the corpus-hygiene checks that run before a score is quoted, the validator ladder, and the comparison record.
• Run the regression before a prompt or model swap, not after, and compare baseline against candidate on the same golden set with token and cost capture. Report only what the run reported; a metric the harness did not emit stays null.
• Give every agentic loop its budgets as product features, not prompt advice: step, time, token, cost, and tool-call budgets each with a recorded termination reason, and for recursive delegation the budgets bind the whole tree, not each node separately.
• Separate draft from commit for risky side effects: reads and drafts may run autonomously when scoped and labeled, but external writes, deletions, and communications need an approval record outside the prompt - a model's stated intention is never the authorization.
• When the user asks for communication through a public board, treat the destination as a public external disclosure even when the account is authenticated: give read, search, register, profile, reply, publish their own authority and outbound-data expectation, show the exact destination, the public-audience label, and the complete outbound payload before a host-recorded approval, and reconcile an ambiguous send by read-back or receipt before any retry. Load references/public-board.md for the per-action authority table, the untrusted-peer rules, and what survives compaction and handoff.
• When the feature presents or acts on business records; enforces a cumulative business limit; stores facts about a person, load references/stateful-contracts.md. Provenance is not authorization, follow-up references resolve against the final-order receipt, limits are checked on resulting state inside one atomic apply boundary, and stored user facts are host-validated and deletable. A feature with none of those properties records that and skips this conditional contract.
• Keep design and evidence separate: a prepared schema, prompt layout, or eval plan is not implementation, an observed eval run, review, CI, or merge evidence.