nuclear-grade-context-engineering AGENTS.md

A set of working instructions for AI agents in a software repository, focused on recording evidence, decisions, approved files, and release choices.

In plain words
What is it for?
It guides agents through choosing the right workflow, checking risky changes, resolving instruction conflicts, and handing work to others.
Why use it?
It helps prevent unchecked assumptions and unclear authority when agents make or review software changes.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/flyfission/nuclear-grade-context-engineering/agents-md
Clone the repo
git clone --depth 1 https://github.com/FlyFission/nuclear-grade-context-engineering

Made for: Codex, OpenCode.

Per session 1,038 This file is loaded in full into every session.
When invoked 1,038 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 9811ff4ed1f0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

AGENTS.md · 67 lines

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.md or WORKFLOWS.md only 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.

Read the full file on GitHub · 67 lines

Changes

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.

  1. 2d ago First seen · 67 lines · 1,038 tokens per session scan A 9811ff4ed1f0

Subscribe to this mod's changes

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.