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 avelikiy/great_cto --skill well-architectedgit clone --depth 1 https://github.com/avelikiy/great_ctoWrote 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/avelikiy/great_cto/well-architected)<a href="https://agentmods.dev/skills/avelikiy/great_cto/well-architected"><img src="https://agentmods.dev/badge/skills/avelikiy/great_cto/well-architected/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/avelikiy/great_cto/well-architected"><img src="https://agentmods.dev/badge/skills/avelikiy/great_cto/well-architected.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00060 | $0.01608 |
| Opus 5 | $0.00030 | $0.00804 |
| Sonnet 5 | $0.00012 | $0.00322 |
| Haiku 4.5 | $0.00006 | $0.00161 |
Grade A, and why
well-architected 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 7d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Well-Architected — 6 pillars to verify before shipping
Every ARCH document for non-nano work must answer the 6 pillar questions below. Skipping a pillar is allowed only if explicitly justified (e.g. "Sustainability: N/A — backend-only, runs in shared infra.").
This is adapted from AWS Well-Architected (lens: small-team SaaS / LLM applications), trimmed to questions that matter at <10 engineer scale.
Pillar 1 — Operational excellence
Questions
- Observability: What metrics, logs, traces do we emit? How do we tell from a dashboard if this is working in prod?
- Deployability: How do we ship a change? CI gates? Rollback path?
- Runbooks: When this breaks at 3am, what does on-call read?
Pass criteria
- ✅ One metric per business outcome (e.g. webhook-deliveries-acked)
- ✅ One log line per request, with request-id correlatable across services
- ✅ Deploy path is documented and tested (rollback dry-run executed)
- ✅ Runbook covers top-3 failure modes from pre-mortem
Common fail
❌ "We'll add monitoring later." Monitoring is part of the feature.
Pillar 2 — Security
Questions
- Trust boundaries: Where does untrusted data enter? How is it validated/sanitized?
- Authn / authz: Who can call this? Who can read/write the data?
- Secrets: Where are API keys, DB passwords, JWT signing keys stored?
- Data classification: PII? PHI? PCI cardholder data? What's the retention policy?
Pass criteria
- ✅ Every external input has explicit validation at the boundary
- ✅ Authz is enforced at the data layer, not just UI
- ✅ Secrets in env vars or secret manager, never in source
- ✅ Sensitive data classified and retention policy defined
Common fail
❌ "JWT validates the user, that's our authz." JWT is authentication. Authorization is separate (this user can read THIS row).
Pillar 3 — Reliability
Questions
- Failure modes: What happens when a downstream dependency is slow / down / corrupted?
- Idempotency: Can a retried request safely re-execute?
- Backups & recovery: What's the RPO (data-loss tolerance)? RTO (downtime tolerance)? Test plan for both?
- Capacity: What's the max QPS this can handle? What happens at 1.5x that?
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.
- 7d ago First seen · 202 lines · 60 tokens per session scan A 81c72d1869ab
well-architected is a skill published in the GitHub repository avelikiy/great_cto (92 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,608 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
spec-drift
Standalone plan-vs-code audit on any branch: runs /ship Step 8's plan-completion section from disk (hash-pinned) against an explicit plan and base. Report, JSON, exit code. Never edits code.
third-lens-review
After Claude self-pitfall + Codex on a ship-worthy/architecture/RT/security/contract change: run a third external model house (distant training distribution → different blind spots) on the patched artifact, then adversarial synthesis.
quality-review
After a PRD, spec, or plan, before implementation: hunt perceived-quality pitfalls (silent failures, missing loading/empty states, error recovery, state drift) that make a product feel cheap. Complements pitfall-verification.
boundaries
Analyze Phoenix context boundaries and module coupling via mix xref. Use when checking cross-context calls, validating dependencies, before splitting modules, or reviewing architecture.
triage
Triage review findings interactively — approve, skip, or prioritize each issue. Use after /phx:review to filter findings before fixing.
review-code
Review a change along one specific quality dimension — bugs, design, simplicity, maintainability, testability, test quality, type safety, contracts, operational readiness, docs, prose value, change intent, defect-class completeness after a fix, or context-file adherence. Loads exactly one dimension reference and…