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 grcengineering/companion --skill feynman-explainergit clone --depth 1 https://github.com/grcengineering/companionWrote 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/grcengineering/companion/feynman-explainer)<a href="https://agentmods.dev/skills/grcengineering/companion/feynman-explainer"><img src="https://agentmods.dev/badge/skills/grcengineering/companion/feynman-explainer/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/grcengineering/companion/feynman-explainer"><img src="https://agentmods.dev/badge/skills/grcengineering/companion/feynman-explainer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00062 | $0.00514 |
| Opus 5.5 | $0.00025 | $0.00206 |
| Sonnet 5.5 | $0.00012 | $0.00103 |
| Haiku 4.5 | $0.00006 | $0.00051 |
Grade A, and why
feynman-explainer 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
feynman-explainer
What
Ask the learner to teach a concept back, then identify the smallest gaps that block clear transfer.
When
- The learner says they understand and wants to check.
- The learner submits their own explanation or summary.
- The learner is preparing to explain a concept to a stakeholder.
- Another skill needs proof that the learner can transfer the concept.
Not For
- First-pass concept teaching. Use
concept-tutor. - Memory drills without a full explanation. Use
recall-quiz. - Judging a live operational outcome.
Inputs
- Learner-authored explanation.
- Target audience, if known.
- Intended concept or scenario.
Steps
- Ask the learner to explain the concept to a non-technical stakeholder.
- Grade clarity, missing assumptions, and transferability.
- Quote or paraphrase the strongest part.
- Name one gap and why it matters.
- Ask for a revised explanation.
- If needed, provide a compact model answer after the retry.
Validation
- The revised explanation is simpler, more accurate, and transferable.
- Feedback names one or two gaps, not a long list.
- The learner does the rewrite before receiving the polished answer.
Gotchas
- If the learner has not supplied an explanation, ask for one before grading.
- If the explanation is audience-mismatched, calibrate to the intended listener.
- If the learner asks for "the answer", run one explain-back attempt first unless they are blocked.
Failure Modes
- Rewriting too early: preserve the learner's ownership of the explanation.
- Over-grading: focus on the gap that changes understanding most.
- Operational grading: evaluate the explanation, not the learner's organization.
Examples
- User says "I think evidence freshness means recent proof" -> Grade what is right, name the missing fitness-for-purpose piece, and ask for a revised version.
- User pastes a stakeholder explanation -> Score clarity, assumptions, and transfer, then ask for a tighter rewrite.
- User asks "Explain this to me" -> Route to
concept-tutorbecause they are not teaching it back yet.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 63 lines · 62 tokens per session scan A 0ed0e9ece965
feynman-explainer is a skill published in the GitHub repository grcengineering/companion (34 stars, last pushed 5mo ago), licensed MIT. It adds 62 tokens to every session and 514 once invoked, about $0.0002 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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