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Mil Hdbk 338

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jgsystemsconsulting
mil-hdbk-338

Knowledge base from MIL-HDBK-338B (Electronic Reliability Design Handbook). Use for electronic reliability engineering: R/M/A theory, reliability specification/allocation/prediction, parts management and derating, reliable circuit and fault-tolerant design, environmental and human performance reliability, FMEA/FMECA/FTA/sneak circuit analysis, design reviews and testability, FRACAS, reliability demonstration and growth testing, and systems-level R&M parameters. Covers selected Part 2 design-guidance topics only; skips large annex/part-stress tables. Guidance handbook — not a contractual requirement text. Does not replace MIL-HDBK-217 prediction libraries, service-specific reliability regs, or safety standards (see mil-std-882).

Overview

Publisherjgsystemsconsulting
Repositoryjgs-se-knowledge-packs
Skill namemil-hdbk-338
Stars
121
Forks
1
Bundled files
13
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jgsystemsconsulting on GitHub. Read the source before you install it.

Installation

Install the Mil Hdbk 338 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs/tree/main/packs/mil-hdbk-338
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs.git /tmp/jgs-se-knowledge-packs
mkdir -p .claude/skills
cp -r /tmp/jgs-se-knowledge-packs/packs/mil-hdbk-338 .claude/skills/mil-hdbk-338
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mil Hdbk 338 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mil Hdbk 338 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mil Hdbk 338 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MIL-HDBK-338B — Electronic Reliability Design

Source: DoD MIL-HDBK-338B (US Government work, public domain; Distribution Statement A) | Chapters: 9

When to use

Reach for this pack when designing or reviewing electronic/hardware reliability programs grounded in MIL-HDBK-338B — especially reliability requirements and allocation, parts derating, prediction caveats, FMEA/FTA, testability, FRACAS, and reliability growth/demonstration strategy. Use it to brief design teams on inherent reliability levers and to structure analytical and test feedback loops.

Prerequisites: none — plain Markdown; no MCP server, API key, or licence tier needed at runtime.

How to Use This Skill

  • Without arguments — load the core frameworks below.
  • With a topic — ask about MTBF/availability, allocation/prediction, derating, fault tolerance, FMEA/FTA, growth testing, FRACAS, or testability.
  • With a chapter — ask for ch01 through ch09.

Supporting files: glossary.md, patterns.md, cheatsheet.md.

Guidance only. 338B states it is not to be cited as a requirement. Synthesize practices; do not paste annex reliability tables or third-party standard text.

Core Frameworks & Mental Models

Inherent reliability is a design output

Once the design (parts, stresses, architecture) is approved, inherent reliability is largely fixed. Parts, derating, circuit design, and fault tolerance set that ceiling before test.

Spec, allocate, model, predict

Quantitative requirements with environment; allocation tree; reliability model matching architecture; prediction with explicit method/assumptions; refresh when design changes.

R/M/A and system effectiveness

Reliability, maintainability, and availability metrics (including MTBF/MTTR-class measures) feed system effectiveness. Know whether you discuss inherent or operational measures.

Analysis pair: FMEA/FMECA + FTA (+ SCA when needed)

Inductive failure mode analysis for breadth; deductive fault trees for critical top events; sneak circuit analysis for latent paths without part failure.

Empirical loop: FRACAS + demonstration/growth

Data collection and closed-loop corrective action; choose growth (design still plastic) vs demonstration (frozen design decision test); accelerated tests need physics-aware reading.

Testability as a reliability property

Detection/isolation provisions and design-review gates make predicted reliability verifiable in integration and field use.

Chapter Index

#SectionKey content
ch01R/M/A Theory FoundationsDistributions, failure modeling, MTBF/MTTR/availability, Bayesian notes
ch02Spec, Allocation, PredictionRequirements, apportionment, models, prediction methods
ch03Parts Management & DeratingParts control, electronic/mechanical derating
ch04Circuit Design & Fault ToleranceRobust circuits, redundancy, fault-tolerant design
ch05Environment & Human ReliabilityEnvironmental design, human performance reliability
ch06FMEA/FTA/SCAFMEA/FMECA, fault trees, sneak circuits
ch07Reviews, Testability, Safety LinkDesign reviews, DFT/BIT, safety program interface
ch08Data, FRACAS, Demo & GrowthData uses, FRACAS, demonstration, growth, accelerated test
ch09Systems Reliability EngineeringEffectiveness, system R&M parameters, COTS/NDI

Topic Index

  • Allocation / apportionment → ch02
  • Availability (inherent vs operational) → ch01, ch09
  • BIT / diagnostics → ch07
  • COTS / NDI reliability → ch09
  • Derating → ch03
  • Environmental stress design → ch05
  • Failure distributions / Weibull / exponential → ch01
  • Fault tolerance / redundancy → ch04
  • Fault tree analysis (FTA) → ch06
  • FMEA / FMECA → ch06
  • FRACAS → ch08
  • Human performance reliability → ch05
  • MTBF / MTTR → ch01, ch02
  • Parts management → ch03
  • Physics-of-failure / stress drivers → ch05, ch03
  • Reliability demonstration → ch08
  • Reliability growth testing → ch08
  • Reliability prediction → ch02
  • Sneak circuit analysis → ch06
  • System effectiveness → ch09
  • Testability / DFT → ch07

Supporting Files

  • glossary.md — R&M terms used across selected 338B topics
  • patterns.md — implementation patterns with When/How/Trade-offs
  • cheatsheet.md — decision rules, maps, tells & smells

Scope & Limits

This pack covers selected design-guidance material from MIL-HDBK-338B (1 Oct 1998; Notice 2 keeps active) — R/M/A theory, specification/allocation/prediction, core Section 7 design guidelines (parts through safety interface), reliability data/FRACAS/demonstration/growth, and systems reliability engineering. Extraction metadata reports 1046 PDF pages for the nde-ed.org mirror copy (DLA catalog often cites ~716 content pages; PDF page objects can differ). It does not reproduce large annex part-stress/reliability tables, full worked numerical appendices, or third-party standards quoted inside 338B. Guidance only — not contractual requirements text. US Government public domain work (Distribution Statement A). No source-material download link is published.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mil Hdbk 338 AI skill do?

Knowledge base from MIL-HDBK-338B (Electronic Reliability Design Handbook). Use for electronic reliability engineering: R/M/A theory, reliability specification/allocation/prediction, parts management and derating, reliable circuit and fault-tolerant design, environmental and human performance reliability, FMEA/FMECA/FTA/sneak circuit analysis, design reviews and testability, FRACAS, reliability demonstration and growth testing, and systems-level R&M parameters. Covers selected Part 2 design-guidance topics only; skips large annex/part-stress tables. Guidance handbook — not a contractual req...

Why use Mil Hdbk 338 on TypingMind?

Because you install it once and use it with any model. Mil Hdbk 338 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mil Hdbk 338 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs/tree/main/packs/mil-hdbk-338. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mil Hdbk 338?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mil Hdbk 338?

As many as you like. As long as a model supports skills, you can use Mil Hdbk 338 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mil Hdbk 338 AI skill free?

It is published on GitHub by jgsystemsconsulting. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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