learn

A command for learning how a software project works and practising how to explain it. It offers briefings, decision deep-dives, gotcha scenarios, quizzes, and limited code exercises.

In plain words
What is it for?
Use it to catch up on project evolution, study architecture and decisions, test knowledge of tricky details, practise explanations, or rehearse an elevator pitch.
Why use it?
It helps when you have missed project changes or understand a decision only superficially. Questions come before answers in some modes, and feedback points to the source documents.

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/wittyreference/twilio-claude-plugin/learn
Clone the repo
git clone --depth 1 https://github.com/wittyreference/twilio-claude-plugin
Per session 34 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,330 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 $0.00034 $0.02330
Opus 5 $0.00017 $0.01165
Sonnet 5 $0.00007 $0.00466
Haiku 4.5 $0.00003 $0.00233

Measured yesterday against content hash 37d34f112f89, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

learn 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 yesterday.

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.

commands/learn.md · 294 lines

How it starts

The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Learning & Knowledge System

Build and maintain deep comprehension of your project -- its architecture, decisions, gotchas, capabilities, and evolution.

Two Mindsets

Catch-up: "I've been away. What happened?" -> /learn briefing Depth: "I need to deeply understand and articulate X." -> /learn decision, /learn gotcha

Rules

  • Code exercises (no-args mode): Max 2 per session
  • All other modes: No session cap -- these serve information retrieval and articulation practice
  • Exercise-first, answer-second: For decision and gotcha modes -- pose the challenge BEFORE revealing the answer. This is the generation effect.
  • Source-grounded feedback: Every piece of feedback cites the actual source document and path
  • Decline = suppress: Only applies to code exercises, not other modes

Arguments

<user_request> $ARGUMENTS </user_request>

Mode Dispatch

Parse the first word of $ARGUMENTS:

First word Mode
(empty) Code exercises (existing behavior)
briefing or catch-up or catchup Briefing
decision Decision deep-dive
gotcha Gotcha scenario exercise
quiz Rapid-fire knowledge check
status or dashboard Learning coverage dashboard
list List pending exercises
skip Suppress code exercises
review Retrieval practice
generate Generate exercises

Mode: Code Exercises (no arguments)

  1. Look for recent commits from autonomous work (subagents, orchestration, headless)
  2. Identify 2-3 interesting design decisions, patterns, or non-obvious code in the changed files
  3. Generate exercise questions on the spot from the most recent autonomous changes
  4. Present the list and ask which one to work on
  5. When the user picks one, present the exercise question and STOP. Wait for their response.
  6. After their response, provide feedback:
    • Read the actual file referenced in the exercise
    • Compare their prediction/understanding to what the code actually does
    • If they were wrong, say so directly, explain the gap, explore why
    • If they were right, confirm and optionally add deeper context

Read the full file on GitHub · 294 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. yesterday First seen · 294 lines · 34 tokens per session scan A 37d34f112f89

Subscribe to this mod's changes

learn is a command published in the GitHub repository wittyreference/twilio-claude-plugin (2 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 2,330 once invoked, about $0.0002 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 2026-08-31.