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/robisson/build-like-amazon-agent-skills/learngit clone --depth 1 https://github.com/robisson/build-like-amazon-agent-skillsWrote 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/robisson/build-like-amazon-agent-skills/learn)<a href="https://agentmods.dev/commands/robisson/build-like-amazon-agent-skills/learn"><img src="https://agentmods.dev/badge/commands/robisson/build-like-amazon-agent-skills/learn.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 | $0.00000 | $0.00641 |
| Opus 5 | $0.00000 | $0.00320 |
| Sonnet 5 | $0.00000 | $0.00128 |
| Haiku 4.5 | $0.00000 | $0.00064 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn — Correction of Errors and Mechanisms
Path resolution: All
skills/,agents/, andpatterns/paths in this command are relative to the plugin root directory. If not found in the working directory, resolve from the plugin installation path.
You are activating the learn skill chain: correction-of-errors → mechanism-creation.
What to do
- Read skills at
skills/correction-of-errors/andskills/mechanism-creation/. - Help the user learn from failures and build systems that prevent recurrence.
Step 1: Correction of Errors (COE)
Guide the user through writing a COE:
- Incident summary: What happened, when, impact (duration, customers affected).
- Timeline: Minute-by-minute from detection to resolution.
- Root cause analysis: Use 5 Whys — dig past symptoms to systemic causes.
- Contributing factors: What conditions allowed this to happen?
- What went well: Acknowledge what worked (detection, response, tooling).
- Action items: Each with owner, due date, and completion criteria.
- Categorize: immediate fix, short-term mitigation, long-term prevention.
Key COE principles:
- Blameless — focus on systems, not individuals.
- Be brutally honest — the goal is learning, not looking good.
- Action items must be mechanisms, not heroics.
Step 2: Mechanism Creation
Convert learnings into durable mechanisms:
- Automation: Replace manual steps with automated checks.
- Guardrails: Pipeline gates, config validation, pre-deploy checks.
- Process changes: Updated runbooks, new review steps, training.
- Architectural improvements: Eliminate the class of failure entirely.
- Metrics and alarms: Detect the preconditions before they become incidents.
A mechanism is complete when:
- It works without human willpower or memory.
- It prevents the entire CLASS of problem, not just this instance.
- It's verified through testing or simulation.
Implementation Memory Capture
After corrective actions are defined, check whether any action item is an implementation-level corrective action (i.e., something that should change how code is written in future builds — not an org/process/people action). If so:
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.
- 4d ago First seen · 57 lines · 0 tokens per session scan A 7d4562d970fb
learn is a command published in the GitHub repository robisson/build-like-amazon-agent-skills (14 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 641 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
upgrade
Upgrade the skillshare CLI binary and/or the built-in skillshare skill.
setup-pm-skills
Onboard a new user — find out what they do, recommend the right bundles & top skills, and set up a project CONTEXT.md so every skill is tailored to them.
verify
Perform a non-destructive post-implementation verification gate validating the implementation against spec.md, plan.md, tasks.md, and constitution.md.
release
Git geçmişinden hedef kitleye uygun sürüm notları oluştur.
spec-forge
Use when generating software specifications — full chain (Idea→Decompose→Tech Design + Feature Specs) or individual documents.
dd-init
Initialize product documentation structure in current directory.