Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/flyfission/nuclear-grade-context-engineering/ng-classifygit clone --depth 1 https://github.com/FlyFission/nuclear-grade-context-engineeringWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.00856 |
| Opus 5 | $0.00000 | $0.00428 |
| Sonnet 5 | $0.00000 | $0.00171 |
| Haiku 4.5 | $0.00000 | $0.00086 |
Grade A, and why
ng-classify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ng-classify
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.
Picks Quick, Standard, or a stronger human-reviewed mode based on consequence, how easy it is to undo, and how much is unknown. Use when you start a change to code, docs, dependencies, AI power, releases, or public claims and the right level of care is unclear. Do not use for a tiny easy-to-undo edit with obvious proof, which is Quick by default, or for a purely administrative reversible edit crossing no trust boundary, which needs no packet at all.
Use when
- A change request is new, vague, or has grown.
- You know the decision question, but the evidence bar is unclear.
- A pull request has AI-generated code, tests, docs, prompts, or release files.
- Reviewers disagree on whether Quick evidence is enough.
- The work is routine, by-the-book, new, interrupted, resumed, handed off, or high stakes, and you need the right habit to control it.
Do not use when
- A change record already has a fresh mode choice and the scope has not changed.
- The system is failing right now and needs incident handling first.
- The change is purely administrative, instantly reversible, and crosses no trust boundary -- that is the administrative floor (no packet; the commit message is the record), not a mode to rate.
- The mode is already chosen and you now need to author or update the packet files -- use
creating-change-recordsinstead.
Inputs
- The user request, issue, pull request, or diff.
- The files, dependencies, prompts, data, credentials, APIs, release files, and users the change affects.
docs/02-operating-system/activation-thresholds.md.- The change record's
risk.md, if one exists.
Prompt text
Sort this change into a Nuclear-grade mode.
Inputs:
- Request or diff: <paste/link>
- Affected files/assets: <list>
- Impact on users, security, dependencies, data, AI behavior, or release: <known facts>
- Archetype (posture) this work is in, if known: Prototyper / Builder / Sweeper / Grower / Maintainer
Return:
- the decision question and the proof that must clear before work goes on
- the archetype floor that applies (Sweeper and Builder start at Standard; Maintainer at Nuclear on trust-bearing surfaces; a Prototyper's low floor holds only while shipping is an explicit non-goal)
- the chosen mode: Quick, Standard, or a stronger mode that a human reviews -- it must clear that floor, or state the reason for going under it
- how bad it is if wrong, how easy to undo, how exposed, how easy to catch, how uncertain
- the work mode and which safety habit (HPI) to use: none, context pack, handoff, self-check, an independent check, a record of lessons from real operation (OPEX), or a trust check
- the assumptions or facts that drove the mode choice
- the record files this mode needs
- the least proof required
- the conditions that should make you ask for help
- a limits note: do not claim formal verification and validation, compliance, certification, safety, security, or regulatory adequacy
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 61 lines · 0 tokens per session scan A 49892e08e688
ng-classify is a command published in the GitHub repository FlyFission/nuclear-grade-context-engineering (33 stars, last pushed 23d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 856 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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