analysis-learn

A guided command for starting a learning exercise based on the ALIVE loop, a structured method for working through an analysis scenario. It checks setup and tracks learning progress.

In plain words
What is it for?
Use it to begin a guided analysis scenario, create or read progress records, check for an active session, and continue or start learning work.
Why use it?
It prevents a learner from starting without the required configuration or losing track of an earlier exercise. It also makes repeated practice easier to follow.

Command for Claude Code

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/with-geun/alive-analysis/analysis-learn
Clone the repo
git clone --depth 1 https://github.com/with-geun/alive-analysis

Made for: Claude Code.

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 1,201 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 $0.01201
Opus 5 $0.00000 $0.00600
Sonnet 5 $0.00000 $0.00240
Haiku 4.5 $0.00000 $0.00120

Measured 3d ago against content hash 3efce28f82b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analysis-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 3d 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.

.claude/commands/analysis-learn.md · 136 lines

How it starts

The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/analysis-learn

Start a new learning session with a guided ALIVE loop scenario.

Instructions

Step 1: Check initialization

Verify .analysis/config.md exists. If not, tell the user to run /analysis-init first. Read config.md to load language setting and team context.

Step 2: Load progress

Check if .analysis/education/progress.md exists.

  • If not, create it from the template below and inform the user: "Welcome to Education Mode! This is your first learning session."
  • If it exists, read it to get completed scenarios, current skill levels, and any in-progress sessions.

Check if there's already an active learning session (In Progress table in progress.md):

  • If yes, ask: "You have an active learning session ({ID} — {scenario}). Resume it with /analysis-learn-next, or start a new one?"

progress.md template:

# Learning Progress
> Last updated: {YYYY-MM-DD}

## Completed Scenarios
| ID | Scenario | Difficulty | Score | Hints | Completed |
|---|---|---|---|---|---|

## Skill Radar
| Skill Area | Avg Score | Stage |
|---|---|---|
| Problem Framing | — | ASK |
| Data Exploration | — | LOOK |
| Hypothesis Testing | — | INVESTIGATE |
| Communication | — | VOICE |
| Reflection | — | EVOLVE |

## Recommended Next
- Start with b1-signup-drop (Beginner)

## In Progress
| ID | Scenario | Current Stage | Started |
|---|---|---|---|

Step 3: Ask setup questions

Use AskUserQuestion to gather:

Q1. Difficulty level?

  • Beginner — Guided single-file analysis with annotations and built-in hints (20-30 min)
  • Intermediate — Full 5-file analysis with minimal guidance (45-60 min)

Q2. Choose a scenario:

Present scenarios filtered by the chosen difficulty. Mark completed scenarios with ✅ and show scores. If the progress data suggests a recommended scenario, mark it with ⭐.

Beginner scenarios:

Scenario Domain Type Status
b1: "Why did signups drop yesterday?" SaaS/Mobile Investigation {✅ 82/100 or Available}
b2: "Which onboarding flow is better?" Product/Growth Comparison {status}
b3: "How much does turnover cost us?" HR/Finance Quantification {status}

Read the full file on GitHub · 136 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. 3d ago First seen · 136 lines · 0 tokens per session scan A 3efce28f82b9

Subscribe to this mod's changes

analysis-learn is a command published in the GitHub repository with-geun/alive-analysis (41 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,201 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.