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 neverinfamous/memory-journal-mcp --skill llm-app-engineeringgit clone --depth 1 https://github.com/neverinfamous/memory-journal-mcpWrote 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/neverinfamous/memory-journal-mcp/llm-app-engineering)<a href="https://agentmods.dev/skills/neverinfamous/memory-journal-mcp/llm-app-engineering"><img src="https://agentmods.dev/badge/skills/neverinfamous/memory-journal-mcp/llm-app-engineering.svg" alt="Measured on agentmods" 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 | $0.00058 | $0.00275 |
| Opus 5 | $0.00029 | $0.00138 |
| Sonnet 5 | $0.00012 | $0.00055 |
| Haiku 4.5 | $0.00006 | $0.00028 |
Grade A, and why
llm-app-engineering 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 7d 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.
What it actually says
LLM App Engineering
Core principles for building robust LLM applications.
Key Patterns
- Prompt Engineering: Use few-shot, chain-of-thought, and clear system instructions.
- Context Window Management: Do not blindly append history to prompts. Implement sliding windows, summarization hooks, or token-based pruning to keep inputs within context limits and ensure high recall for recent instructions.
- Token Management: Track usage, handle limits gracefully, and summarize context dynamically.
- Structured Outputs: Always force structured outputs (e.g., JSON) using schema-guided generation or strict function calling rather than relying on prompt engineering to "ask nicely for JSON".
- Streaming: Always stream responses for perceived performance improvements in UX.
- Evaluations (Evals): Build automated eval pipelines for your prompts to catch regressions. Do not rely solely on human vibe checks. Implement automated deterministic evals (e.g., regex matching, JSON validation) and LLM-as-a-judge evals for semantic quality.
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.
- 7d ago First seen · 19 lines · 58 tokens per session scan A bf52ac32d310
llm-app-engineering is a skill published in the GitHub repository neverinfamous/memory-journal-mcp (20 stars, last pushed 1mo ago), licensed MIT. It adds 58 tokens to every session and 275 once invoked, about $0.0003 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
refine
Transform a brief or prompt into a structured, production-ready prompt via prompt-optimizer. File or text mode.
opus-4-5-migration
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
prompt-writer
Write maximally terse agent prompts from scratch. Use when creating agent specs, command prompts, or instruction sets with constitutional governance.
injection-defense
Injection defense is a set of rules for stopping untrusted text from taking control of an AI agent. Prompt injection is an attack where content such as a document or webpage contains instructions meant to override the agent’s rules.
bootstrap-llm-synthesis
Construct the LLM synthesis prompt from project surface scan + optional tree-sitter context + optional Q&A answers. Call the LLM. Parse and validate the response into 6-8 structured memory entries with clarity tags and source traceability. Used as Stage 3 of the /gaai:bootstrap pipeline.
promptly-prompt
Clarity gate for prompts: intercepts unclear requests and forces the model to restate the user's intent to align before working, then diagnose the root cause and reuse the domain's mature, established practices before improvising. Clear prompts pass through untouched.