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 agentmods add commands/with-geun/alive-analysis/analysis-learngit clone --depth 1 https://github.com/with-geun/alive-analysisWhat 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 | $0.00000 | $0.01201 |
| Opus 5 | $0.00000 | $0.00600 |
| Sonnet 5 | $0.00000 | $0.00240 |
| Haiku 4.5 | $0.00000 | $0.00120 |
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.
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} |
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.
- 3d ago First seen · 136 lines · 0 tokens per session scan A 3efce28f82b9
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.
Other commands, from other repositories
mentor
Educational mode that explains concepts, teaches patterns, and guides learning. Ideal when learning a new codebase, technology, or programming concept.
learn
Learn Claude Code best practices and capture lessons into persistent memory.
learn
Learn about Task Master capabilities through interactive exploration.
project_init
이 프로젝트를 ocul-pm 추적 대상으로 초기화 (.oculpm/ + 기록 규칙 생성) — 사용자가 직접 실행하는 명시적 시작.
inception
새 프로젝트/기능 영역의 설계 시작 — 리서치→사양 확정→3-depth 계획→EVALS→rules 시드.
next
활성 플랜의 다음 미완 리프를 잡아 구현 — 구현→검증→일지→플랜 갱신 한 사이클.