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/fortifynpx skills add sharpdeveye/maestro --skill fortifygit 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 | $0.00029 | $0.00658 |
| Opus 5 | $0.00015 | $0.00329 |
| Sonnet 5 | $0.00006 | $0.00132 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
fortify 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 — 89 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 guardrails-safety reference in the agent-workflow skill for defense-in-depth patterns and error boundary design.
Make the workflow resilient. Every external call will fail eventually — model APIs, tools, databases, third-party services. Fortify ensures the workflow handles failure gracefully.
Fortification Layers
Layer 1: Input Validation
- Validate all inputs before processing
- Return clear error messages for invalid input
- Set size limits on all input fields
Layer 2: Retry with Backoff For transient failures (network errors, rate limits, timeouts):
Retry strategy:
max_retries: 3
initial_delay: 1s
backoff_multiplier: 2
max_delay: 30s
retryable_errors: [429, 500, 502, 503, 504, TIMEOUT, CONNECTION_ERROR]
non_retryable_errors: [400, 401, 403, 404]
Layer 3: Fallback Responses When retries are exhausted:
- Use a cached previous response (if applicable)
- Use a simpler/cheaper model as fallback
- Return a graceful degradation response
- Escalate to human review
Layer 4: Circuit Breakers When a service is consistently failing:
Circuit breaker:
failure_threshold: 5 consecutive failures
state: CLOSED → OPEN (after threshold) → HALF_OPEN (after cooldown)
cooldown: 60 seconds
half_open_max_requests: 1
Layer 5: Timeout Controls Every external call needs a timeout:
- Model API calls: 30-120s depending on task
- Tool executions: 10-60s depending on tool
- Database queries: 5-15s
- Third-party APIs: 10-30s
Fortification Audit
For each component, verify:
- Input validation present
- Retry logic for transient failures
- Fallback for when retries fail
- Timeout set
- Error logged with context
- User gets a meaningful error (not a stack trace)
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 · 89 lines · 29 tokens per session scan A 4e0003e18cc4
fortify is a skill published in the GitHub repository sharpdeveye/maestro (415 stars, last pushed 4mo ago), licensed MIT. It adds 29 tokens to every session and 658 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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