feynman

feynman is a command for coding agents from neurofoo/agent-skills. It costs 0 tokens per session (604 once invoked), scanned A, original, MIT.

A framework for understanding the practical, emotional, and social reasons people choose a product or solution. It describes the situation, the person involved, the desired outcome, and the alternatives they use now.

In plain words
What is it for?
It helps with product discovery, competitive analysis, customer research, and describing the job a feature or service is hired to do.
Why use it?
It helps teams look beyond stated features and identify the real problem a product is expected to solve. This can clarify customer needs and comparisons with existing workarounds.

Command

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 commands/neurofoo/agent-skills/feynman
Clone the repo
git clone --depth 1 https://github.com/neurofoo/agent-skills

Wrote 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.

agentmods badge for feynman

README.md
[![agentmods](https://agentmods.dev/badge/commands/neurofoo/agent-skills/feynman.svg)](https://agentmods.dev/commands/neurofoo/agent-skills/feynman)
Your own site
<a href="https://agentmods.dev/commands/neurofoo/agent-skills/feynman"><img src="https://agentmods.dev/badge/commands/neurofoo/agent-skills/feynman.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 604 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00000 $0.00604
Opus 5 $0.00000 $0.00302
Sonnet 5 $0.00000 $0.00121
Haiku 4.5 $0.00000 $0.00060

Measured 4d ago against content hash 733295f1d7bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

feynman 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 4d 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.

feynman/commands/feynman.md · 106 lines

How it starts

The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Feynman Technique

Apply the full Feynman learning technique to deeply understand a concept.

Instructions

Work through all four steps of the Feynman technique. Be honest about gaps—they're the point.

Output Format

Concept: [What are we trying to understand?]


Step 1: Explain It Simply

Explain as if teaching someone with no background in this field

Simple Explanation

[Write a plain-language explanation. Use everyday words. Avoid jargon. Aim for a bright 12-year-old to understand.]

Analogy

[Create an analogy using something familiar to illustrate the concept]


Step 2: Identify Gaps

Where did the explanation get fuzzy, hand-wavy, or require jargon?

Gaps Found

Gap What I Said What I'm Not Sure About
1 [vague part] [the underlying question]
2 [vague part] [the underlying question]
3 [vague part] [the underlying question]

Jargon Used

Term Can I Explain It Simply?
[term] Yes / No / Partially

Step 3: Fill the Gaps

Research or think through each gap

Gap 1: [Topic]

  • The question: [What wasn't clear?]
  • The answer: [What I learned]
  • Now I can explain it as: [Simple version]

Gap 2: [Topic]

  • The question: [What wasn't clear?]
  • The answer: [What I learned]
  • Now I can explain it as: [Simple version]

Gap 3: [Topic]

  • The question: [What wasn't clear?]
  • The answer: [What I learned]
  • Now I can explain it as: [Simple version]

Step 4: Refined Explanation

Rewrite the complete explanation with gaps filled and simpler language

Final Simple Explanation

[The improved, complete explanation in plain language]

Improved Analogy

[A refined or new analogy that better captures the concept]

Key Takeaways

  1. [Core insight 1]
  2. [Core insight 2]
  3. [Core insight 3]

Remaining Questions [What still feels unclear? These are topics for deeper study]

Read the full file on GitHub · 106 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. 4d ago First seen · 106 lines · 0 tokens per session scan A 733295f1d7bd

Subscribe to this mod's changes

feynman is a command published in the GitHub repository neurofoo/agent-skills (111 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 604 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.