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Nasa System Safety

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jgsystemsconsulting
nasa-system-safety

Knowledge base from NASA System Safety Handbook Volume 2 (NASA/SP-2014-612). Use for risk-informed safety cases (RISC), integrated safety analysis (ISA), adequate safety concepts, safety objectives and requirements, acquirer/provider roles, graded safety analysis, claims-evidence-argument structure, SSMP development, and RISC evaluation. Does not cover nuclear or chemical safety domains, occupational health regulations, or NASA-specific programmatic requirements beyond system safety.

Overview

Publisherjgsystemsconsulting
Repositoryjgs-se-knowledge-packs
Skill namenasa-system-safety
Stars
121
Forks
1
Bundled files
11
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.

  • 11 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 Nasa System Safety 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/nasa-system-safety
  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/nasa-system-safety .claude/skills/nasa-system-safety
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nasa System Safety 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 Nasa System Safety 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 Nasa System Safety 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.

NASA System Safety Handbook Volume 2 (NASA/SP-2014-612)

Source: NASA Office of Safety and Mission Assurance (US Government work, public domain) | Chapters: 7

When to use

Use this skill when working on safety cases, system safety analysis, risk-informed decision making, or safety requirements for aerospace or safety-critical engineering programmes. It is the primary reference for structuring a RISC, scoping an ISA, setting safety thresholds and goals, understanding Acquirer/Provider roles, or evaluating an existing safety case. This skill covers the NASA implementation of the safety case paradigm; it is applicable to other safety-critical domains by analogy but is grounded in NASA aerospace practice.

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 RISC development, ISA scoping, ASARP, SCI designation, safety thresholds, assurance deficits, graded approach, SSMP content, or RISC evaluation; I read the relevant chapter.
  • With a chapter — ask for ch01 through ch07.

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

Core Frameworks & Mental Models

1. The Two-Principle Framework for Adequate Safety

A system is adequately safe if and only if it simultaneously:

  1. Meets or exceeds the minimum tolerable level of safety performance — a probabilistic floor set by the Acquirer, expressed as a maximum acceptable total loss probability (threshold for first operations; goal for mature operations).
  2. Is ASARP — further safety improvement would require intolerable sacrifice in cost, schedule, or technical performance.

Both conditions must hold throughout all life-cycle phases. Meeting the minimum alone is insufficient; ASARP without meeting the minimum is insufficient.

2. The Acquirer / Provider Split

RoleResponsibilityPrimary Output
AcquirerSafety assurance: set requirements, evaluate RISC, accept safety riskSafety requirements; RISC Evaluation Report; risk acceptance decision
ProviderSafety ensurance: achieve adequate safety through design/operation; build the RISCSSMP; ISA; RISC Report

When a Provider sub-tasks another organisation, the Provider becomes Acquirer for that sub-relationship.

3. The Risk-Informed Safety Case (RISC) Structure

Top Claim: System is adequately safe
  └── Intermediate Claim 1 (meets minimum tolerable level)
        └── Intermediate Claim 1.1
              ├── Base Claim A  [Direct Evidence + Supporting Evidence → Assurance Deficit Score 1-5]
              └── Base Claim B  [Direct Evidence + Supporting Evidence → Assurance Deficit Score 1-5]
  └── Intermediate Claim 2 (system is ASARP)
        └── Base Claim C  [...]
  • Direct evidence: quantitative (PRA results, failure rates, test data, anomaly rates, best-practice adherence).
  • Supporting evidence: qualitative (personnel qualifications, V&V of tools, documentation quality, safety culture, external review quality).
  • Assurance deficit score: 1 (very low deficit, ~95–100% confidence) → 5 (very high deficit, ~0–35% confidence). Set by Acquirer based on mission criticality.

4. Safety Performance Margin and Requirement Derivation

Safety Threshold (total loss probability including UU risks)
  - Safety Performance Margin (estimated from historical SPF)
  = Analytic Requirement for Known Risks (what the ISA/PRA must demonstrate)

Safety Performance Factor (SPF) = Total actual loss prob ÷ Known-risk modelled loss prob
  (derived from historical programmes; values of ~5 have been observed for early development)

Initial analytic requirement = Safety Threshold ÷ SPF
Mature analytic requirement  = Safety Goal ÷ SPF_mature
  (trajectory follows exponential burndown over ~125 flights)

5. Graded ISA / RISC Approach

Project PriorityTriggerRequired Analysis
Priority 1Human spaceflight; White House approval; planetary protection; strategic; >$1BFull PRA + qualitative system safety analysis
Priority 2$250M–$1B life-cycle costQualitative analysis + PRA where appropriate
Priority 3<$250M life-cycle costQualitative analysis only

Apply finer-grained grading within any mission: highest-criticality scenarios → deepest analysis.

6. The Three-Phase Staged RISC Evaluation

  1. Acceptance Review — completeness and protocol compliance check; gate before substantive review.
  2. Qualitative Scope/Methods Review (Surveying) — methods, coverage, ASARP, evidentiary strength; Evaluator forms independent view.
  3. Quantitative Evaluation (Auditing) — spot-check calculations, sensitivity studies, simplified independent models for critical claims.

Output: RISC Evaluation Report with summary and detailed findings; overall rating of Acceptable or Unacceptable.

7. Assurance Deficit → CRM Integration

Assurance deficits identified in the RISC convert directly into CRM risk statements (per NPR 8000.4A) with assigned owners, tracked under the same Continuous Risk Management process as all other programme risks. The RISC and CRM process are the same management loop, not parallel activities.

8. Key Decision Rules

  • RISC is not the basis for the risk acceptance decision — it informs the decision but does not bind the decision maker.
  • Safety requirement tailoring is mandatory, not optional — blanket levy of all historical requirements without ASARP review is explicitly identified as sub-optimal.
  • SCI designation must be top-down from ISA, not bottom-up from FMEA only — management practices, interfaces, and organisational factors are SCIs.
  • Confirmation bias is the primary threat to RISC validity — the RISC must include both failure scenario analysis (deductive) and safety argument (inductive).

Chapter Index

#SectionKey content
ch01IntroductionVolume 2 scope; 5 rationales; 8 principal themes; RISC as organising concept; graded approach; UU risk primacy
ch02Key ConceptsAdequate safety two-principle framework; Acquirer/Provider roles; safety assurance vs ensurance; safety threshold/goal; ASARP; RISC definition; UU risk taxonomy
ch03System Safety Framework OverviewFour-element framework; ISA as central engine; SCI designation; allocated/derived requirements; tailoring; Acquirer-Provider interaction tables; life-cycle perspective
ch04Safety Objectives and Requirements (Acquirer)Safety performance margin derivation; SPF; KMO sub-cases; burndown profile; deterministic requirements; ASARP tailoring; verification procedures; SSRA
ch05System Safety Ensurance (Provider)SSMP (42-element); graded ISA (Priority 1/2/3); M&S CAS; assurance deficit management; SCI top-down identification; ASARP trade analysis; precursor analysis
ch06RISC Development (Provider)Claims tree structure; intermediate/base claims; direct/supporting evidence; assurance deficit scoring; GSN; graded RISC approach; RISC Report structure; living document lifecycle
ch07RISC Evaluation (Acquirer)Staged evaluation (acceptance/surveying/auditing); Evaluation Team independence; assurance deficit × importance matrix; confidence aggregation methods; VOI analysis; RISC Evaluation Report

Topic Index

  • Adequate safety / adequately safe → ch02, ch03
  • ASARP → ch02, ch03, ch04, ch05
  • Assurance deficits → ch05, ch06, ch07
  • Claims tree / claims-evidence-argument → ch06, ch07
  • Confirmation bias → ch02, ch06
  • Continuous Risk Management (CRM) → ch05, ch07
  • Graded approach → ch01, ch05, ch06
  • Integrated Safety Analysis (ISA) → ch03, ch05
  • Key Decision Points (KDPs) → ch02, ch03
  • Key Mission Objectives (KMOs) → ch04
  • Minimum tolerable safety level / safety threshold / safety goal → ch02, ch03, ch04
  • RISC development → ch06
  • RISC evaluation → ch07
  • RISC Report → ch06
  • Safety-Critical Items (SCIs) → ch03, ch05
  • Safety performance margin / SPF → ch03, ch04
  • SSMP (System Safety Management Plan) → ch05
  • SSRA (System Safety Requirements Analysis) → ch03, ch04, ch05
  • UU risks (unknown/underappreciated) → ch02, ch03, ch04
  • Value-of-Information (VOI) → ch07
  • Verification procedures → ch04

Supporting Files

  • glossary.md — ~50 defined terms with chapter references, alphabetical order.
  • patterns.md — 13 reusable patterns with When-to-use, How, and Trade-offs sections.
  • cheatsheet.md — Decision rules, quick-reference RISC component table, and tells-and-smells for common failure modes.

Scope & Limits

This knowledge pack covers the full content of NASA/SP-2014-612 (Volume 2), which provides implementation guidance for systems engineering-integrated system safety at NASA: objectives/requirements, integrated safety analysis, RISC development, and RISC evaluation. It does not cover: Volume 1 conceptual foundations in depth; domain-specific standards (nuclear, chemical, occupational health); NASA-specific programmatic requirements not related to system safety; or non-aerospace applications (though the RISC framework is applicable by analogy). All content is US Government work in the public domain; no licence restrictions apply.

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 Nasa System Safety AI skill do?

Knowledge base from NASA System Safety Handbook Volume 2 (NASA/SP-2014-612). Use for risk-informed safety cases (RISC), integrated safety analysis (ISA), adequate safety concepts, safety objectives and requirements, acquirer/provider roles, graded safety analysis, claims-evidence-argument structure, SSMP development, and RISC evaluation. Does not cover nuclear or chemical safety domains, occupational health regulations, or NASA-specific programmatic requirements beyond system safety.

Why use Nasa System Safety on TypingMind?

Because you install it once and use it with any model. Nasa System Safety 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 Nasa System Safety in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jgsystemsconsulting/jgs-se-knowledge-packs/tree/main/packs/nasa-system-safety. 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 Nasa System Safety?

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 Nasa System Safety?

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

Is the Nasa System Safety 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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