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-learn-reviewgit clone --depth 1 https://github.com/with-geun/alive-analysisWrote 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/commands/with-geun/alive-analysis/analysis-learn-review)<a href="https://agentmods.dev/commands/with-geun/alive-analysis/analysis-learn-review"><img src="https://agentmods.dev/badge/commands/with-geun/alive-analysis/analysis-learn-review.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01406 |
| Opus 5 | $0.00000 | $0.00703 |
| Sonnet 5 | $0.00000 | $0.00281 |
| Haiku 4.5 | $0.00000 | $0.00141 |
Grade A, and why
analysis-learn-review 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 6d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analysis-learn-review
Complete a learning session with a full scored review and self-assessment.
Instructions
Step 1: Identify the learning session
Read .analysis/education/progress.md to find the active learning session.
- If no active session → "No active learning session."
- If the session hasn't completed all stages → "You're still on {STAGE}. Complete all stages with
/analysis-learn-nextfirst, or run this command to get an early review of what you've done so far."
Step 2: Load reference materials
Read the scenario's files:
core/education/scenarios/{scenario-id}/rubric.md— scoring criteriacore/education/scenarios/{scenario-id}/solution/— reference solution (single file for Beginner, 5 files for Intermediate)
Read the learner's files:
- Beginner: the single Quick learning file
- Intermediate: all 5 stage files in the learning folder
Step 3: Generate the review report
Compare the learner's work against the rubric and solution. Generate a comprehensive review:
Header:
# 📚 Learning Review: {scenario title}
> ID: {ID} | Scenario: {scenario-id} | Difficulty: {level} | Date: {YYYY-MM-DD}
Section 1: Stage-by-Stage Scores
| Stage | Score | Max | Key Feedback |
|---|---|---|---|
| ❓ ASK | {n} | 20 | {1-sentence summary} |
| 👀 LOOK | {n} | 20 | {1-sentence summary} |
| 🔍 INVESTIGATE | {n} | 25 | {1-sentence summary} |
| 📢 VOICE | {n} | 20 | {1-sentence summary} |
| 🌱 EVOLVE | {n} | 15 | {1-sentence summary} |
| Total | {n} | 100 |
Section 2: What You Did Well (3-5 specific positives)
Section 3: Areas for Improvement (2-4 specific items with explanations)
Section 3b: Common Mistakes Found
After generating the score table, read the ## Most Common Mistakes at Intermediate Level section from core/education/scenarios/{scenario-id}/rubric.md. For each stage where the learner lost points, cross-reference the deduction against the common mistakes list. If one or more mistakes match:
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.
- 6d ago First seen · 146 lines · 0 tokens per session scan A c01fef2fd96d
analysis-learn-review 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 1,406 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.
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