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/extract-patternnpx skills add sharpdeveye/maestro --skill extract-patterngit 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 | $0.00028 | $0.00533 |
| Opus 5 | $0.00014 | $0.00267 |
| Sonnet 5 | $0.00006 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
extract-pattern 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 — 69 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.
Turn working solutions into reusable patterns. Every successful workflow contains patterns that are applicable beyond their original context.
Step 1: Identify What Worked
Review the workflow and identify components that:
- Solved a common problem in a particularly effective way
- Would be useful in other workflows or projects
- Required significant iteration to get right
- Represent a non-obvious solution
Step 2: Generalize the Pattern
Transform the specific solution into a reusable template:
From specific → To general pattern:
## Pattern: [Name]
**Problem**: What recurring problem does this solve?
**When to use**: When is this pattern appropriate?
**When NOT to use**: When is this pattern inappropriate?
**Template**: Copy-pastable starting point with customization markers.
**Variants**: Common variations for different contexts.
**Pitfalls**: What went wrong during development and how it was fixed.
**Examples**: 1-2 concrete examples.
Step 3: Test Reusability
- Apply the template to a different but analogous problem
- Confirm the customization points are sufficient
- Verify the documentation is clear enough for someone unfamiliar with the original
| Workflow Element | Extract As |
|---|---|
| Effective prompt structure | Prompt template with customization points |
| Tool chain that works well | Pipeline pattern with data flow diagram |
| Error handling strategy | Resilience pattern with implementation guide |
| Evaluation approach | Quality assurance pattern with scoring rubric |
| Context management technique | Context pattern with budget guidance |
| Agent coordination protocol | Orchestration pattern with handoff templates |
Recommended Next Step
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 · 69 lines · 28 tokens per session scan A c8504df5bd7b
extract-pattern is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 533 once invoked, about $0.0001 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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