Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jhlee0409/omni-harness-kit --skill llm-eng-checksgit clone --depth 1 https://github.com/jhlee0409/omni-harness-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jhlee0409/omni-harness-kit/llm-eng-checks)<a href="https://agentmods.dev/skills/jhlee0409/omni-harness-kit/llm-eng-checks"><img src="https://agentmods.dev/badge/skills/jhlee0409/omni-harness-kit/llm-eng-checks/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jhlee0409/omni-harness-kit/llm-eng-checks"><img src="https://agentmods.dev/badge/skills/jhlee0409/omni-harness-kit/llm-eng-checks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00181 | $0.02125 |
| Opus 5 | $0.00090 | $0.01063 |
| Sonnet 5 | $0.00036 | $0.00425 |
| Haiku 4.5 | $0.00018 | $0.00213 |
Grade A, and why
llm-eng-checks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM engineering — design → round-trip → measure → verdict
The dominant failure pattern is claiming an LLM feature works from the code path alone. Code shows the prompt is assembled and the SDK is called; only a real round-trip shows the model actually returns what the contract promises. This skill is provider-agnostic — detect the repo's own client (a hosted provider SDK / a local inference server / an internal gateway) and drive that, never a vendor CLI you assume exists.
PRIME RULE — one real round-trip or it is not done
Before reporting any LLM change done you MUST show ONE real call:
- Real prompt in (quote the actual assembled prompt, not a paraphrase).
- Real output out (quote the model's actual response, not a mock).
- Provider + model named (
<provider>/<model>) + token counts.
Drive it with the repo's own client via bash (its Python/JS entrypoint) — read config from the repo env, never hardcode a key. A stub / fixture / synthetic response = "static OK, dynamic unverified", never "done". "200 OK" is not proof — the content must satisfy the output contract.
1. Prompt design
- Contract, not vibes. Every prompt names: role, task, hard constraints (negative ones too), and an explicit output contract. If the output is consumed by code, the contract is a schema, not prose.
- Few-shot earns its tokens or it is cut. Add exemplars only when a zero-shot round-trip fails the contract; measure the delta (pass-rate before/after). Never carry examples "just in case" — they inflate every request forever.
- Structured output. Prefer provider JSON-schema / tool-calling for machine-consumed output. Then validate every response against the schema (jsonschema / pydantic / zod — the repo's own). An unvalidated
json.loads()is a latent crash: quote a round-trip where the model returns malformed/extra fields and show the validator catching it. - Determinism where it matters. Pin
temperature=0(or the provider's floor) for extraction/classification; reserve higher temp for generative surfaces. State which and why.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 91 lines · 181 tokens per session scan A b15375bfd82c
llm-eng-checks is a skill published in the GitHub repository jhlee0409/omni-harness-kit (2 stars, last pushed 1mo ago), licensed MIT. It adds 181 tokens to every session and 2,125 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
prompt-engineer
Transform rough prompts/ideas into production-ready LLM prompts. Use when crafting, refining, or optimizing prompts for any AI model (Codex, GPT, Llama, etc.) with advanced techniques like CoT, constitutional AI, RAG optimization.
rag-evaluator
Evaluate RAG pipeline quality across faithfulness, relevance, and hallucination metrics. Use when user asks to test, benchmark, or improve a RAG system, or when RAG outputs look wrong.
refine
Crossover layer between the user's intent and LLM execution: takes a raw prompt (terse, frustrated, ambiguous, or shorthand), resolves every vague reference to a concrete artifact, recalls past decisions and lessons, and compiles a verifiable execution contract BEFORE any code is touched. Invoke explicitly as $refine…
cursor-prompting
Internal guidance for composing compact, operator-style prompts for cursor-agent across coding, review, diagnosis, and research tasks.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.