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 instructions/robisson/build-like-amazon-agent-skills/agents-mdgit 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/instructions/robisson/build-like-amazon-agent-skills/agents-md)<a href="https://agentmods.dev/instructions/robisson/build-like-amazon-agent-skills/agents-md"><img src="https://agentmods.dev/badge/instructions/robisson/build-like-amazon-agent-skills/agents-md.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 | $0.03772 | $0.03772 |
| Opus 5 | $0.01886 | $0.01886 |
| Sonnet 5 | $0.00754 | $0.00754 |
| Haiku 4.5 | $0.00377 | $0.00377 |
Grade A, and why
build-like-amazon-agent-skills AGENTS.md 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 5d 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 — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions for AI Agents
This document defines how AI agents should discover, load, and execute skills from this repository. Follow these instructions precisely — they are the operating contract between this skill library and any agent that uses it.
Discovering Skills
Skills are located in the skills/ directory. Each skill is a standalone markdown file with YAML frontmatter containing metadata.
How to Find the Right Skill
- Check the command: If the user invokes a slash command (e.g.,
/wb,/design,/deploy), load the corresponding skill file directly. - Use the meta-skill when no command is clear: If there is no slash command and the right workflow is unclear, read
skills/using-amazon-skills/SKILL.mdfirst. Use it to route the request to the correct lifecycle phase and skill chain. - Check triggers: Each skill's frontmatter contains
triggers— natural language phrases that indicate the skill should be activated. - Check the phase: If you know what lifecycle phase the work is in, browse skills in that phase.
- When in doubt, ask: If multiple skills could apply after consulting the meta-skill, ask the user which workflow they want to follow rather than guessing.
Skill Loading Protocol
When a skill is activated:
- Read the full skill file
- Execute the Context Assessment section to determine if the skill applies
- If it applies, follow the Process section step by step
- At each Verification Checkpoint, evaluate the criteria before proceeding
- If a blocking checkpoint fails, stop and surface the issue to the user
- When complete, confirm all verification checkpoints have been met
Operating Behaviors
These behaviors apply whenever you are operating under this skill library. They are non-negotiable.
0. Respect Approval Gates
When a skill specifies a human approval gate, you MUST pause and present your work. Do not proceed until the user explicitly approves. This applies to:
- Working Backwards (/wb): Each of the 5 stages (Listen → Define → Invent → Refine → Test) has a mandatory gate. Complete one stage, present the output, ask the user to review, and wait for explicit approval before advancing.
- Spec creation (/design): Each artifact (requirements.md → design.md → tasks.md) has a mandatory gate. Present each document for review before generating the next.
- The only exception is /build task execution. Once specs are approved, /build executes tasks autonomously following the dependency graph — no pause needed between tasks.
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.
- 5d ago First seen · 305 lines · 3,772 tokens per session scan A 8d1aa22822d1
build-like-amazon-agent-skills AGENTS.md is an instructions file published in the GitHub repository robisson/build-like-amazon-agent-skills (14 stars, last pushed 3mo ago), licensed MIT. It adds 3,772 tokens to every session, about $0.0189 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 2026-08-30.
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aligndev AGENTS.md
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