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Discovery

Skill avelikiy/great_cto/skills/discovery

Don't buy software. Get the work done. GreatCTO ships AI autopilots that run a whole business function — medical coding, legal docs, procurement, accounting, IT, tax — from intake to outcome. A qualified human signs only the judgment calls. Live connectors, built-in compliance.

Install
npx -y skills add avelikiy/great_cto --skill discovery

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What its author says it does

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Structured pre-design questioning to surface hidden constraints before any architecture decision is locked in. Forces the architect/auditor/reviewer to enumerate what they DON'T know before proposing.

SKILL.md

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Discovery — surface hidden constraints first

The biggest cause of bad agent output is missing context. Before locking in a decision, enumerate what you don't know and surface it.

The 7 discovery dimensions

For any non-trivial request, walk through these and record findings in the report's "Context" section:

1. Who depends on this?

  • What other services / teams consume the thing you're changing?
  • Are there public consumers (open API, OSS users)?
  • Is there a deprecation path if you break compatibility?

Grep for: grep -rE "import.*<your-module>|require.*<your-module>" in the repo and any sibling repos you have access to.

2. What's the scale today, what's it in 6 months?

  • Current traffic: requests/sec, queries/sec, MB/day, daily-active-users
  • Storage: rows in main tables, size on disk
  • Cost: monthly LLM spend, infra spend
  • 6-month projection: linear? exponential? unknown?

If unknown, write: "scale unknown — request from user before proceeding."

3. What MUST not change?

  • Existing API contracts (backward compatibility window)
  • Database schema columns referenced by reporting / BI
  • File formats consumed by other tools
  • Regulatory commitments (audit log retention, SLA RPO/RTO)

4. What's the budget?

  • Monthly cost ceiling (LLM + infra)
  • Headcount: 1-person task vs cross-team effort
  • Calendar: "must ship by X" vs "best by Y"

If unstated, default to "small project_size, 1-engineer-week, <$200/mo budget." Surface this default in the report so the user can correct.

5. What's the failure mode that matters?

Ask: "If this feature breaks at 3am, what gets paged?"

  • Data loss → CRITICAL
  • Wrong answer to user → HIGH
  • Slow response → MEDIUM
  • Bad UX (cosmetic) → LOW

The failure mode dictates investment level (e.g., do you need a canary? A circuit breaker? Just a feature flag?).

6. What's already been tried?

  • Search Beads: bd search "<keyword>" — has this been attempted before?
  • Search docs/decisions: any superseded ADR on this topic?
  • Search lessons.md: any past learning about this pattern?

If past work exists, build on it. Don't redo it.

7. Who decides?

  • Is there a CTO sign-off needed (gate:plan, gate:ship)?
  • Is there a compliance reviewer required (PCI for fintech, HIPAA for healthcare)?
  • Does this need an RFC (multi-team decision)?

Output

A discovery section at the top of your report:

## Context

- **Consumers:** <list, or "unknown — TBD with user">
- **Scale:** <today, 6mo projection>
- **Frozen contracts:** <list, or "none identified">
- **Budget:** <cost + time + people>
- **Failure-mode tier:** Critical | High | Medium | Low
- **Prior work:** <links to ADRs/lessons, or "none found">
- **Decision-makers:** <gate or RFC required>

When to skip

  • nano project_size — discovery is overhead. Skip and document that you skipped: "nano — discovery skipped per skill rules."
  • Pure utility extraction with no behaviour change — skip.
  • Verbal bug-fix from user with clear repro — skip.

Common gotchas

  • Don't assume. If you write "I assume the user wants X", that assumption belongs in Context as a question, not as a fact.
  • Don't outsource to user. Discovery is YOUR job. Bring back as many answers as Glob/Grep/git can produce. Only ask the user for what code cannot tell you.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.