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 skills/sharpdeveye/maestro/amplifynpx skills add sharpdeveye/maestro --skill amplifygit clone --depth 1 https://github.com/sharpdeveye/maestroWhat 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 | $0.00032 | $0.00637 |
| Opus 5 | $0.00016 | $0.00318 |
| Sonnet 5 | $0.00006 | $0.00127 |
| Haiku 4.5 | $0.00003 | $0.00064 |
Grade A, and why
amplify 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 2d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the tool-orchestration reference in the agent-workflow skill for adding tools effectively.
Take a working workflow and make it more capable. Amplification adds new abilities without breaking existing functionality.
Amplification Strategies
Better Prompts
- Add few-shot examples for edge cases the model currently mishandles
- Add chain-of-thought for tasks where reasoning quality matters
- Add negative instructions for common mistakes
- Upgrade output schema with more structured fields
Better Tools
- Add tools for capabilities the model currently lacks
- Improve existing tool descriptions for better selection accuracy
- Add confirmation steps for high-stakes operations
- Add tools for verification/validation of outputs
Better Context
- Add RAG for domain-specific knowledge
- Add real-time data sources for current information
- Add user profile/history for personalization
- Add project documentation as reference context
Better Models
- Upgrade to a more capable model for critical steps
- Use model cascading (cheap model for simple, capable model for complex)
- Add vision capabilities if processing images/documents
- Add code execution capabilities if generating code
Amplification Process
- Identify the gap: What can't the workflow do that it should?
- Choose the strategy: Which amplification approach addresses the gap?
- Implement incrementally: Add one capability at a time
- Verify: Run the evaluation suite to confirm improvement without regression
Impact Assessment
| Strategy | Cost Impact | Latency Impact | Quality Impact |
|---|---|---|---|
| Better prompts | None | None | Medium-High |
| Better tools | Low | Low-Medium | High |
| Better context (RAG) | Medium | Medium | High |
| Better models | High | Medium-High | High |
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.
- 2d ago First seen · 85 lines · 32 tokens per session scan A 5d72d0bbe676
amplify is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 32 tokens to every session and 637 once invoked, about $0.0002 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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