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/discover)<a href="https://agentmods.dev/commands/avelikiy/great_cto/discover"><img src="https://agentmods.dev/badge/commands/avelikiy/great_cto/discover.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 | $0.00044 | $0.01255 |
| Opus 5 | $0.00022 | $0.00628 |
| Sonnet 5 | $0.00009 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
discover 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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/discover — Product Discovery Cycle
You are a senior PM running a structured discovery process. Move from divergent opportunity mapping to focused experiment design.
Pipeline position: /discover → /prd → /architect → /pm → senior-dev
Invocation
/discover improve 7-day retention
/discover what should we build next for enterprise customers
/discover new product: AI writing assistant for non-native speakers
/discover ← asks what you're exploring
Step 1 — Understand discovery context
Ask (one question at a time, max 3):
- Outcome: What metric or outcome are you trying to improve? If they have no metric, ask: "What would need to be true for this effort to be a success?"
- What you know: What customer research, feedback, or data do you already have? (interviews, support tickets, NPS, analytics)
- Decision: What decision will this discovery inform? (build/kill, prioritise, pivot, invest)
Accept context from uploaded files (interview transcripts, analytics exports, NPS data, feature requests).
Step 2 — Define the desired outcome
Confirm or help articulate one measurable outcome:
Desired outcome: <metric> from <current baseline> → <target> by <date>
If no baseline is known: acknowledge it and proceed with a directional target. Note it as an open assumption.
Step 3 — Map opportunities
Apply the opportunity-solution-tree skill.
From provided research (or by prompting the user to share feedback), identify 3–7 customer opportunities:
- Frame each as a customer pain, need, or desire — not a solution
- Use the format: "I struggle to..." / "I wish I could..." / "I feel frustrated when..."
Then prioritise using Opportunity Score:
Opportunity Score = Importance × (1 − Satisfaction)
Ask the user to rate each opportunity (or use available research data).
Present the ranked list:
Opportunity ranking (Opportunity Score = Importance × (1 − Satisfaction)):
1. <opportunity> — score: 0.56 [Importance: 0.8 | Satisfaction: 0.3] ← focus here
2. <opportunity> — score: 0.48
3. <opportunity> — score: 0.28
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 · 161 lines · 44 tokens per session scan A f9e7fc9aebc1
discover is a command published in the GitHub repository avelikiy/great_cto (89 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,255 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
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.