By FlyFission
Enforce risk-graded governance on AI-assisted software work with structured context packs, change records, evidence traces, and review pipelines that keep agent actions questioned, controlled, and auditable.
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 presented evidence — read-only and context-separated from the runner, but still coupled through the orchestrator and possibly model/resources. Do not use to build, gather new evidence, or write code.
PROVE Observe stage. Use to verify and review the runner's output — run tests, gather evidence, read the diff — with no Edit/Write authority over product code, so it cannot directly patch 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 at the start of a task, with a clear role, goal anchor, authority, evidence to produce, forbidden actions, and stop conditions. Use when an agent or reviewer is about to begin work and needs bounded context before it starts. Do not use for a tiny self-contained task that needs no briefing, or for transferring already-open work to a new owner, which is handing-off-work.
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
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
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. I build software and agentic engineering workflows and tools for the nuclear and other mission-critical industries, where mistakes carry real consequences and "fail forward" is far less optional than it is in ordinary software. That same conviction drives AscertAIn, an AI-powered engineering design-review platform that checks diagrams, calculations, specs, and drawings against thousands of expert-authored criteria across engineering disciplines. All of it 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.
Minimum sufficient context. Nuclear-grade is not about adding more process. It is about giving agents the smallest set of instructions, facts, limits, and evidence needed to do serious work without drifting into vibes. If a rule, template field, or artifact does not improve execution, verification, review, or decision quality, remove it.
npx claudepluginhub flyfission/nuclear-grade-context-engineering --plugin nuclear-gradeAGT governance hooks and MCP tools for Claude Code sessions
YES.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
Elite AI development framework: reference-first design, agent orchestration, automated quality gates, and battle-tested engineering workflows
Run Agents Shipgate Tool-Use Readiness workflows from Claude Code. Skill-only: the plugin supplies the agents-shipgate skill and the /agents-shipgate:shipgate command; the scanner runs through the agents-shipgate CLI installed in the local environment (pipx install agents-shipgate, runtime contract 15). For the deterministic hooks (trigger check after edits, full verify at Stop), run: agents-shipgate install-hooks --target claude-code --write.
Intelligent orchestration platform for AI coding tools — routes tasks to the best model, learns from outcomes, and enforces quality through multi-model consensus. 47 MCP tools for agent management, research, memory, consensus voting, codebase intelligence, and a full dev pipeline.