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 instructions/flyfission/nuclear-grade-context-engineering/agents-mdgit 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 | $0.01038 | $0.01038 |
| Opus 5 | $0.00519 | $0.00519 |
| Sonnet 5 | $0.00208 | $0.00208 |
| Haiku 4.5 | $0.00104 | $0.00104 |
Grade A, and why
nuclear-grade-context-engineering AGENTS.md 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Guidance
AI agents working in this repo should treat Nuclear-grade as a way to gather evidence, not a brand to show off. The idea is simple. Keep a questioning attitude. Use small habits from Human Performance Improvement (HPI). And keep the approved version under control for AI-assisted software work. In practice that means: question assumptions, write down what the change must do, brief the work, double-check risky actions, and hand off cleanly. Keep the controlled files, the evidence, the decisions, the version everyone agreed is correct, and the release stance all linked together.
Default behavior
- Start with this file and the right change record. Follow links into
README.mdorWORKFLOWS.mdonly when the task needs them; do not preload either wholesale. - Treat instruction conflicts as blockers, not judgment calls: name the conflicting sources, apply the host's documented precedence, and stop before the affected action if precedence does not resolve them.
- Use the lightest mode that is still honest. For a purely administrative, instantly reversible change that crosses no trust boundary, the lightest honest mode is the administrative floor: no packet, the commit message is the record. Any trust boundary, dependency, public claim, or hard-to-reverse step makes it at least a Quick packet.
- Tie every claim to evidence.
- Name the controlled items when you change prompts, models, tools, dependencies, docs, skills, commands, templates, checkers, or releases.
- Hand off the work when you delegate it, pick it back up, or pass it to a reviewer, checker, or releaser with work still open.
- Double-check yourself before risky edits, risky commands, public claims, a trust change to a dependency, model, or API, or a release action.
- Use lessons from real operation (OPEX) when a near miss, a bad handoff, a review surprise, or an operating signal should update a lasting control.
- Declare your intent and reasoning before a critical action, so a reviewer can challenge the thinking, not just the result.
- Match authority to the evidence: decide reversible, well-evidenced work at the edge; escalate anything irreversible, trust-bearing, or thinly evidenced to a person.
- Halt unsafe or unclear work and surface the concern; raise known deficiencies so they are owned, not normalized.
- Hand off so the next owner is more capable, not more dependent.
- Prefer links and short status labels over long, repeated text.
- Run the right tests and checker commands before you say the work is done.
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 · 67 lines · 1,038 tokens per session scan A 9811ff4ed1f0
nuclear-grade-context-engineering AGENTS.md is an instructions file published in the GitHub repository FlyFission/nuclear-grade-context-engineering (33 stars, last pushed 23d ago), licensed MIT. It adds 1,038 tokens to every session, about $0.0052 per session on Opus 5. 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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ephemeral-sandbox CLAUDE.md
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Agent-Memory-Bridge AGENTS.md
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codeTree CLAUDE.md
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symphony AGENTS.md
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