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.
git 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/commands/avelikiy/great_cto/api-contract-review)<a href="https://agentmods.dev/commands/avelikiy/great_cto/api-contract-review"><img src="https://agentmods.dev/badge/commands/avelikiy/great_cto/api-contract-review/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/commands/avelikiy/great_cto/api-contract-review"><img src="https://agentmods.dev/badge/commands/avelikiy/great_cto/api-contract-review.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 | $0.00048 | $0.00376 |
| Opus 5 | $0.00024 | $0.00188 |
| Sonnet 5 | $0.00010 | $0.00075 |
| Haiku 4.5 | $0.00005 | $0.00038 |
Grade A, and why
api-contract-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
You are the great_cto /api-contract-review command.
Step 1 — ARCH
ARGS="${ARGUMENTS:-}"
SLUG="$ARGS"
[ -z "$SLUG" ] && SLUG=$(ls docs/architecture/ARCH-*.md 2>/dev/null | sort -V | tail -1 | xargs -I{} basename {} .md | sed 's/^ARCH-//')
ARCH="docs/architecture/ARCH-${SLUG}.md"
[ ! -f "$ARCH" ] && echo "BLOCKED: $ARCH not found" && exit 1
API_HITS=$(grep -ciE "openapi|graphql|grpc|webhook|public api|partner api|developer portal|api key|oauth|sdk" "$ARCH" .great_cto/PROJECT.md 2>/dev/null || echo 0)
[ "$API_HITS" -eq 0 ] && echo "No API platform signals — skipping." && exit 0
Step 2 — Invoke api-platform-reviewer
subagent_type: api-platform-reviewer — write docs/sec-threats/TM-api-${SLUG}.md using skills/great_cto/templates/TM-api.md.
Step 3 — Surface
Print: design checklist status, anti-patterns flagged, SLA targets, gates (gate:api-contract).
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.
- 5d ago First seen · 31 lines · 48 tokens per session scan A 45a4fbff30f5
api-contract-review is a command published in the GitHub repository avelikiy/great_cto (89 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 376 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 2026-09-03.
Other commands, from other repositories
fec-doc-sync
Sync README, docs, environment variables, scripts, API/routing/component descriptions and deployment instructions from code and project sources of truth.
dotnet-harden
Scan and harden .NET backend code against high-impact anti-patterns such as sync-over-async, lifetime bugs, fat endpoints, and fragile SignalR state.
dotnet-critique
Deep architecture critique of pure .NET backend code. Evaluates AI slop, OOP/SOLID, layer boundaries, DI lifetimes, endpoints, SignalR, data access, concurrency, and distributed-system choices.
py-critique
Deep architecture critique of Python backend code. Evaluates AI slop, SOLID compliance, layer boundaries, anti-patterns, and design quality.
dotnet-structure
Analyze and recommend .NET backend solution and folder structure improvements. Checks projects, layers, oversized files, and boundary clarity.
fec-review
Conduct a standardized review of the specified file or recently changed front-end code, output a graded review report and save it as a Markdown file.