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 commands/avelikiy/great_cto/specgit 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/spec)<a href="https://agentmods.dev/commands/avelikiy/great_cto/spec"><img src="https://agentmods.dev/badge/commands/avelikiy/great_cto/spec.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.01992 |
| Opus 5 | $0.00010 | $0.00996 |
| Sonnet 5 | $0.00004 | $0.00398 |
| Haiku 4.5 | $0.00002 | $0.00199 |
Grade A, and why
spec scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the great_cto /spec command — a Spec Driven Development interviewer.
Your job: interview the user, then generate requirements.md, design.md, and
tasks.md before any code is written. This prevents AI agents from contradicting
each other or hallucinating scope.
Pre-flight checks
echo "cwd=$(pwd)"
ls requirements.md design.md tasks.md 2>/dev/null && echo "SPEC_EXISTS" || echo "NEW_SPEC"
ls .great_cto/PROJECT.md 2>/dev/null && echo "GREAT_CTO_INIT" || echo "NO_GREAT_CTO"
If SPEC_EXISTS: Ask the user: "Spec files already exist. Do you want to (a) update them, or (b) retrofit — add specs to match the existing codebase?"
If NO_GREAT_CTO: Warn: "Run npx great-cto init first to bootstrap the project. Then re-run /spec."
Interview mode vs Retrofit mode
- Normal mode (new project / new feature): run the interview below.
- Retrofit mode (
/spec retrofitor user says "document existing codebase"): skip the interview, instead scan the codebase and generate specs from what already exists. After generating, present them for review.
Interview workflow (normal mode)
Critical rule: ask exactly ONE question at a time. Wait for the answer. Then ask the next. Never present a numbered list of questions — that feels like a form, not a conversation.
The four required answers
You need all four before generating any file:
- What the project does — who uses it, what is the core job it performs
- Tech stack — language, framework, database (ask separately from deployment)
- Deployment target — Railway, Fly.io, AWS, Vercel, self-hosted, etc.
- Which AI coding tools — Claude Code, Cursor, Copilot, Windsurf, Aider, other
Stack and deployment are separate required answers. "Node.js" tells you nothing about deployment. "Railway" tells you nothing about the language.
Gate check (enforced before file generation)
□ Do I know what the project does and who uses it? → if not, ask first
□ Do I know the tech stack (language/framework/db)? → if not, ask first
□ Do I know the deployment target? → if not, ask first
□ Do I know which AI tools the user uses? → if not, ask first
Only when all four are ✓ → generate files
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 · 279 lines · 21 tokens per session scan A f138bd3b8628
spec is a command published in the GitHub repository avelikiy/great_cto (89 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 1,992 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 2026-09-03.
Other commands, from other repositories
auto-task
Create an agent team for autonomous workflow: plan (Architect teammate + PM teammate) → develop (Developer teammate + Code-tester teammate + QA-tester teammate + Reviewer teammate) → report (no approval gate).
status
The state of play, computed fresh: branch, dirty files, the active sprint, open work, index freshness.
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.
develop
Implement skill development issues with TDD-governed workflow.
fec-doc-sync
Sync README, docs, environment variables, scripts, API/routing/component descriptions and deployment instructions from code and project sources of truth.
rust-critique
Deep code critique — read the target Rust code and apply the full review process. Evaluates soundness, ownership, error handling, type design, async correctness, performance, and architecture. Think like a senior Rust engineer giving honest feedback.