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By FlyFission
Govern AI-assisted software work with risk-graded context engineering: scope changes, assign authority, record evidence, and enforce review gates before merge or release.
npx claudepluginhub flyfission/nuclear-grade-context-engineering --plugin nuclear-gradeRecord the version everyone agreed is correct — the baseline — after a review, merge, release, or change to public docs. This is a portable command prompt.
Build a work breakdown for a deliverable — a tree that splits the thing you are building into its parts. The tree must add up to the whole and nothing more (the 100% rule), have no overlap between parts, use outline numbering, and define every part in a short dictionary. Do this so the scope can be checked before any folders or work begin. This is a portable command prompt.
Sort a change into the lightest mode that is still honest. First name the decision the change must settle. Then pick the mode. Then name the proof needed before work goes on. This is a portable command prompt.
Bring an abandoned or half-filled change packet to an honest terminal state: complete it, close it with a recorded rationale, or delete it. This is a portable command prompt that pairs with `ng status` health tags.
Review a diff or a module for slipping standards — oversized files, needless layers of abstraction, feature logic leaking where it should not, and clever indirection — and give one honest verdict. This is a portable command prompt.
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, safe path — so you can move fast and still trust what ships.
You do not need to read the whole repo to start. Run the two commands in See it work in 30 seconds, then copy one folder.
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, save the approved version, 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.
The very first question is the most important one: what does this evidence have to prove, and what fact would change my decision?
Normal AI coding:
prompt -> diff -> persuasion -> merge risk
Nuclear-grade:
question -> specify -> execute -> verify -> decide -> save approved version -> operate
This first release (v0) is a working toolkit you can use today: skills an agent can follow, command prompts you can paste, templates for small and large changes, a small command-line tool, a checker, a public list of sources, one fully worked example, and one hands-on comparison study.
Watch an AI agent prove it stayed inside its workspace, then read the change record that backs the result:
python -m pytest docs/03-worked-examples/ai-agent-tool-permissions/tests/test_workspace_guard.py -v
# 4 passed — every write attempt outside the agent's workspace was denied and logged.
python tools/ng.py validate docs/03-worked-examples/ai-agent-tool-permissions/.nuclear/changes/add-agent-tool-permissions
# OK — the change record exposes the evidence behind that result.
That packet is your template, not a curiosity. Copy docs/03-worked-examples/ai-agent-tool-permissions/ to start your own, and see CORE.md for the seven habits behind it. The longer guided tour lives in QUICKSTART.md. If your shell only has python3, use python3.