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 liqiongyu/lenny_skills_plus --skill building-with-llmsgit clone --depth 1 https://github.com/liqiongyu/lenny_skills_plusWrote 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/liqiongyu/lenny_skills_plus/building-with-llms)<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/building-with-llms"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/building-with-llms/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/liqiongyu/lenny_skills_plus/building-with-llms"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/building-with-llms.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 | $0.00048 | $0.02268 |
| Opus 5 | $0.00024 | $0.01134 |
| Sonnet 5 | $0.00010 | $0.00454 |
| Haiku 4.5 | $0.00005 | $0.00227 |
Grade A, and why
building-with-llms 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 12d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building with LLMs
Scope
Covers
- Building and shipping LLM-powered features/apps (assistant, copilot, light agent workflows)
- Prompt + tool contract design (instructions, schemas, examples, guardrails)
- Data quality + evaluation (test sets, rubrics, red teaming, iteration loop)
- Production readiness (latency/cost budgets, logging, fallbacks, safety/security checks)
- Using coding agents (Codex/Claude Code) to accelerate engineering safely
When to use
- “Turn this LLM feature idea into a build plan with prompts, evals, and launch checks.”
- “We need a system prompt + tool definitions + output schema for our LLM workflow.”
- “Our LLM is flaky—design an eval plan and iteration loop to stabilize quality.”
- “Design a RAG/tool-using agent approach with safety and monitoring.”
- “We want to use an AI coding agent to implement this—set constraints and review gates.”
When NOT to use
- You need product/portfolio strategy and positioning (use
ai-product-strategy). - You need a full PRD/spec set for cross-functional alignment (use
writing-prds/writing-specs-designs). - You need primary user research (use
conducting-user-interviews/usability-testing). - You are doing model training/research, infra architecture, or bespoke model tuning (delegate to ML/eng; this skill assumes API models).
- You only want “which model/provider should we pick?” (treat as an input; if it dominates, do a separate evaluation doc).
- You want to design an eval/benchmark framework without building a specific feature (use
ai-evals). - You need to evaluate a vendor/tool for adoption rather than build an LLM feature (use
evaluating-new-technology). - You want to quickly prototype or vibe-code an idea without production planning (use
vibe-coding).
Inputs
Minimum required
- Use case + target user + what “good” looks like (success metrics + failure modes)
- The LLM’s job: generate text, transform data, classify, extract, plan, or take actions via tools
- Constraints: privacy/compliance, data sensitivity, latency, cost, reliability, supported regions
- Integration surface: UI/workflow, downstream systems/APIs/tools, and any required output schema
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- eval/eval_config.json 1.7 KB
- eval/SHOWCASE.md 5.3 KB
- eval/with_skill.md 43 KB
- eval/without_skill.md 21 KB
- README.md 1.8 KB
- references/CHECKLISTS.md 2.4 KB
- references/EXAMPLES.md 1.5 KB
- references/INTAKE.md 1.8 KB
- references/RUBRIC.md 5.0 KB
- references/SOURCE_SUMMARY.md 2.5 KB
- references/TEMPLATES.md 3.7 KB
- references/WORKFLOW.md 3.0 KB
- skillpack.json 375 B
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.
- 12d ago First seen · 137 lines · 48 tokens per session scan A dc668bb6e85e
building-with-llms is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 2,268 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
prompt-optimizer
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agent-prompt-builder
Stop writing system prompts by hand — let structured identity generate them automatically from beliefs and responsibilities.
prompt-engineer
Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW).
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework.
plan
Use when a request needs shaping before any code is written — a rough or vague prompt to sharpen, an ambiguous idea to design, or a clear-enough task to decompose. One chain-starter that amplifies the prompt, designs the approach, and decomposes it into a batched task file, skipping whichever phases the request…