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 skills add joao-aires/agent-skills-public --skill reliability-designgit clone --depth 1 https://github.com/joao-aires/agent-skills-publicWrote 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/skills/joao-aires/agent-skills-public/reliability-design)<a href="https://agentmods.dev/skills/joao-aires/agent-skills-public/reliability-design"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/reliability-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/joao-aires/agent-skills-public/reliability-design"><img src="https://agentmods.dev/badge/skills/joao-aires/agent-skills-public/reliability-design.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00042 | $0.01044 |
| Opus 5.5 | $0.00017 | $0.00418 |
| Sonnet 5.5 | $0.00008 | $0.00209 |
| Haiku 4.5 | $0.00004 | $0.00104 |
Grade A, and why
reliability-design 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 yesterday.
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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reliability design
Read composition guidance for optional expertise, fallback and scope.
Start with what the user depends on and decide what counts as completing it successfully. Define a service-level indicator (SLI) that measures that outcome over a known population, then establish an accepted service-level objective (SLO) and the monitoring needed to assess it. Infrastructure and request metrics help explain failure, but the indicator needs to retain the meaning of the user outcome.
Decide what eligible work enters the denominator before defining success. Include work that starts but never reaches a terminal event, using a deadline or reconciliation observation where necessary. Otherwise the system can appear reliable by counting only work that managed to finish. Read the journey-to-SLO reference below when deriving the measurement or its arithmetic.
Inputs and execution mode
Discover the target, architecture, relevant feature/critical journeys, current telemetry, existing objectives, owners and operational policy. Use code, docs, rendered configuration and live evidence when available. Work with partial sources but label coverage and semantic decisions still needed.
- Interactive: analyze evidence before asking; ask targeted questions about users, acceptable outcomes, latency and budget decisions. Explain alternatives with diagrams and examples.
- Setup/feature implementation: produce a journey contract and implement its instrumentation/resource definitions, tests and documentation in the target stack. Keep changes compatible with existing consumers.
- Recurring review: compare changed flows and incidents with current contracts, identify uncovered outcomes and telemetry drift, update evidence-backed definitions within assigned policy, and prepare bounded patches. Do not automatically relax targets or suppress pages to make compliance improve.
Derivation workflow
- Identify the users (human or machine), intent/start boundary, observable completion, failure/cancellation/rejection branches, deadline, and important segments.
- Draw an evidenced architecture and Mermaid sequence. Include browser/client code, cached paths, external providers, retries, async work, and dependencies required for the outcome. Use current-system mapping expertise if discovery is needed, or perform the relevant mapping directly.
- Classify request-driven, storage and processing stages. Choose a small set of dimensions: availability, latency, quality, freshness, coverage, correctness or durability.
- Write an SLI specification in outcome terms. Separate technical handling from conversion/business acceptance and infrastructure diagnostics.
- Define the implementation contract: valid event population, good-event subset, observation point, timer, units, deduplication, retries, terminal/deadline attribution, tags, queries and missing-data behavior.
- Compare possible observation strategies by user-experience fidelity, coverage, cost, latency and collection reliability. Name blind spots, consent/sampling bias and requests that never reach instrumentation.
- Choose targets/windows from user needs and measured defensibility. Label initial hypotheses and aspirational targets. Document owner agreement and an error-budget decision policy; targets are product decisions, not arbitrary agent defaults.
- Define symptom alerts and useful predictive risk alerts. Determine responder/action/runbook, scope, routing, no-data policy, timing and recovery. Prefer multiwindow burn rates where appropriate; validate low-traffic and extreme-target behavior.
- Implement metric/monitor/SLO/dashboard definitions using the target's tools. Add missing instrumentation with
observability-operationsor directly. Keep SLO populations consistent with their indicators; label intentionally different operational proxies. - Validate with fixtures, historical incidents/quiet periods and live signal samples. Soak new pages silently when local practice permits, with a review date. Confirm notification and runbook reachability before marking operational.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday Changed · +8 lines ed55ed011aff
- 2d ago Changed · +2 lines 63d06c1831c3
- 7d ago First seen · 52 lines · 42 tokens per session scan A 1aa20a9f3806
reliability-design is a skill published in the GitHub repository joao-aires/agent-skills-public (2 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 1,044 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-15.
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