By FlyFission
Govern AI-assisted software work with risk-graded context engineering: split scope into product-first work breakdowns, create focused context packs for agent handoffs, enforce change records with evidence tracking, and run structured reviews (pre/post checks, red-teaming, mission drift detection) before deploys or releases.
Portable command prompt generated from `skills/recording-a-known-good-version/SKILL.md`. Edit the skill, then run `python tools/ng.py gen-commands`; do not edit this file by hand.
Portable command prompt generated from `skills/breaking-down-the-work/SKILL.md`. Edit the skill, then run `python tools/ng.py gen-commands`; do not edit this file by hand.
Portable command prompt generated from `skills/rating-change-risk/SKILL.md`. Edit the skill, then run `python tools/ng.py gen-commands`; do not edit this file by hand.
Portable command prompt generated from `skills/closing-stale-packets/SKILL.md`. Edit the skill, then run `python tools/ng.py gen-commands`; do not edit this file by hand.
Portable command prompt generated from `skills/reviewing-code-quality/SKILL.md`. Edit the skill, then run `python tools/ng.py gen-commands`; do not edit this file by hand.
Five subagents map onto the **PROVE** beats, each with tool boundaries that *encode* the authority
PROVE Educate stage. Use after the verdict to lock in the approved baseline and turn operation into a lesson — record the baseline, OPEX/lessons, and any charter update into .nuclear/. Do not use to build product code, decide ship/block, or run the change.
PROVE Verdict stage. Use to make the ship / block / defer / ship-with-named-risk decision on the evidence alone — read-only and independent of the runner. Do not use to build, to gather new evidence, or to write code.
PROVE Observe stage. Use to verify and review the runner's output — run tests, gather evidence, read the diff — WITHOUT writing product code, so it cannot fix code to pass its own evidence. Do not use to build, plan, or decide ship/block.
PROVE Plan stage. Use to turn a request into an approved plan — question, discover, specify, plan — writing only to the change packet, never product code. Dispatch first, before any building. Do not use to edit code, run commands, or decide ship/block.
Splits scope into a product-first work breakdown that follows the 100% rule, keeps pieces from overlapping, uses outline numbers, and gives every piece a dictionary entry. Use when an epic, feature, or new subsystem needs a clean split into deliverables, or one source of truth before folders or work begin. Do not use for a one-file edit or a backlog item already broken down.
Prepares focused context for an AI agent, reviewer, verifier, or releaser, with a clear role, goal anchor, authority, evidence to produce, forbidden actions, and stop conditions. Use when handing off or resuming work that matters. Do not use for a tiny self-contained task that needs no handoff.
Reviews public text for license, warranty, compliance, safety, security, certification, and fitness claims that go too far, then rewrites them to stay inside the real limits. Use when shipping or editing public docs, READMEs, or rollout copy. Do not use for internal code comments, or for deciding actual legal fitness, which needs a qualified lawyer.
Records a ship, block, defer, or ship-with-risk decision that ties baseline, evidence status, residual risk, rollback, monitoring, and handoff together. Use when a packet, PR, release, dependency change, or agent-authority change approaches merge. Do not use early in development before evidence exists.
Checks that the way you cite source families, agencies, standards, or borrowed ideas is honest and does not claim too much. Use when public docs, templates, skills, change records, or rollout copy point to outside sources. Do not use for private notes, or for checking whether code actually works.
Uses power tools
Uses Bash, Write, or Edit tools
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AI agents no longer just suggest code. They edit files, change prompts, call tools, swap dependencies, write the evidence, and help ship releases. That is a lot of power with very little ceremony. Nuclear-grade gives that work a clear path you stay in control of, so you can move fast and still stand behind what ships.
You do not need to read the whole repo to start. Run one command in See it work, then make it yours, copy one folder, and add the rest only when a change earns it.
I have spent over a decade in the nuclear field, and I run FlyFission Consulting Group, an independent design-review and advisory practice for nuclear projects. That work teaches one durable lesson: complex systems rarely fail in one big step. They fail when authority outruns evidence, one reasonable-looking shortcut at a time.
AI agents are gaining exactly that kind of authority over codebases. This repo ports the habits that keep high-consequence engineering honest (a questioning attitude, configuration management, evidence before decisions) into a shape software teams can use at AI speed. It borrows the discipline, not the regulations: see What this is NOT.
Before an agent builds, you ask hard questions and find the facts. You write down what the change must do. The agent works only inside the limits you set. Then you check the claims against real evidence, decide on purpose, lock the version you trust, put it to work, and learn from what happens next.
The discipline is borrowed from how high-consequence engineering is run: question your assumptions, prove your claims, and never let standards slip one small step at a time. The name is the standard of care, not the vocabulary. Keep the discipline and rename the local copy if "nuclear-grade" would mis-calibrate your team (see DISCLAIMER.md).
Go fast while you are exploring. Slow down the moment the work becomes a promise.
An agent can try ideas and throw them away cheaply, so let it. But the rules tighten as soon as the work turns into a claim, a file you have to keep under control, a public statement, an approved version, a release call, or a change to what the agent is allowed to do.
So the first question is the one that matters most: what does this change have to prove, and what fact would change my decision?
That question has a shape, and the shape has a name.
The kind of software engineering people stake decisions on runs on one habit: you PROVE every claim. Five moves, in order:
PROVE: Plan · Run · Observe · Verdict · Educate
(Observe = weigh the evidence; Verdict = decide on purpose; Educate = ship the result, then learn from how it runs.) The same path at a glance is three moves, PRO: Plan · Run · Operate — up close it's PROVE, from across the room it's PRO. The step it never lets you fold away is Verdict, where a draft becomes a decision: ship it and you can defend every line.
Normal AI coding:
prompt -> diff -> persuasion -> merge risk
npx claudepluginhub flyfission/nuclear-grade-context-engineering --plugin nuclear-gradeYES.md — PUA says NO, YES says YES. 6-layer AI governance: format → trigger → hooks → anti-slack → gates → memory. Makes AI do things RIGHT with encouragement, not pressure. Available in English, 中文, 日本語.
Core safety skills for AI-assisted development: Four Laws, Three Strikes, production-first, scope validation, and environment separation
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Route upstream epistemic deficits and evaluate execution-time risks — /attend (προσοχή: attention)
Personal Claude Code + Codex dev stack: security hooks, AI-first code conventions, /security-review, /repo-map, /stack-check, portable statusline. Designed to complement other skills-based plugins, not replace them.