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 skills add FlyFission/nuclear-grade-context-engineering --skill learning-from-experiencegit clone --depth 1 https://github.com/FlyFission/nuclear-grade-context-engineeringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/flyfission/nuclear-grade-context-engineering/learning-from-experience)<a href="https://agentmods.dev/skills/flyfission/nuclear-grade-context-engineering/learning-from-experience"><img src="https://agentmods.dev/badge/skills/flyfission/nuclear-grade-context-engineering/learning-from-experience/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/flyfission/nuclear-grade-context-engineering/learning-from-experience"><img src="https://agentmods.dev/badge/skills/flyfission/nuclear-grade-context-engineering/learning-from-experience.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.01609 |
| Opus 5.5 | $0.00026 | $0.00644 |
| Sonnet 5.5 | $0.00013 | $0.00322 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
learning-from-experience 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 1mo 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning From Experience
Overview
Lessons from real operation (OPEX) only help if they change future work. So treat agent mistakes, near misses, review surprises, sloppy analysis, and support tickets as feedback. Each one shows you where a control was weak. A control is anything that steers behavior, like a test, a checker, a rule, or a permission limit. Use the lesson to make that control stronger.
No-blame applies to honest error and at-risk slips: the point is to fix the control, not the person or the model. It does not extend to a willful violation — knowingly disabling a control, fabricating a passing result, or routing around a required gate. Surface that and correct it as a finding; never file it as "just a mistake," and never normalize it (Charter Art. 3, 19). A bypassable gate is also its own weak control, so the lesson fixes both the violation and the gate that allowed it.
Decision contract
- Claim checked: the event is stated as actual-versus-standard with a root cause and weak control named, and closes with an owned, verified control fix or a deliberate waiver saying why no fix was needed.
- Artifact observed: the event, near miss, or operating signal and the control it touched -> an OPEX record with the finding, action, owner, and evidence or close-out reason.
- Decision affected: block -- whether a durable control (test, template, prompt, monitor, checker, or baseline) is updated, or the lesson is explicitly waived with a reason.
- Failure class: unlearned-lesson (a fix with no control change, or a record closed with regret instead of a fix).
- Next action: assign an owner and a recheck trigger; a repeated weak control escalates to a second independent reviewer.
When to Use
- A bad handoff, a wrong-file edit, a made-up claim, an agent going past its allowed tools, a bug that escaped to users, or a surprise in review happened.
- Users or operators misread a release, a public claim, a runbook, a template, or an approved version.
- A past change record, skill, command, test, checker, monitor, or template failed to steer behavior the way it should have.
- A change to the rules or sources produced new text but no lasting change to a control.
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.
- 1mo ago First seen · 107 lines · 65 tokens per session scan A 09a1a3dea41a
learning-from-experience is a skill published in the GitHub repository FlyFission/nuclear-grade-context-engineering (33 stars, last pushed 3d ago), licensed MIT. It adds 65 tokens to every session and 1,609 once invoked, about $0.0003 per session on Opus 5.5. 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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