cost-estimate

name	cost-estimate
description	Estimate codebase cost-to-build, AI-assisted ROI, and fair-market valuation

Scan this entire codebase and produce a professional cost estimate and valuation report. Analyze:

1. Codebase inventory: Count files, lines of code by language, modules, API integrations, external services, database schemas, UI components, and any complex subsystems.

2. Complexity assessment: Identify the hardest parts — real-time features, protocol implementations, security layers, multi-platform support, API integrations (especially government/enterprise APIs that require domain expertise), custom parsers, streaming, WebSocket/SSE, OAuth flows, etc.

3. Human team estimate: Calculate what a real development team would need to build this from scratch. Use current US market rates (2025-2026):

   • Senior full-stack developer: $125-175/hr
   • Backend specialist: $150-200/hr
   • DevOps/infra: $140-180/hr
   • UI/UX: $100-150/hr
   • Project management overhead: 15-20%
   • QA/testing: 15-20% of dev time
   • Estimate across 4 team sizes: Solo dev, Lean Startup (2-3), Growth Co (4-6), Enterprise (8+)

4. AI comparison: Estimate AI-assisted hours actually spent (based on git history, commit frequency, time span from first to latest commit). Calculate speed multiplier and value per hour.

5. Integration complexity: For each external integration (APIs, channels, protocols, third-party services), assess:

   • API stability and breaking change risk (how often does the upstream API change?)
   • Authentication complexity (OAuth, tokens, QR pairing, binary handshakes)
   • Rate limiting and quota constraints
   • Failure modes and required retry/fallback logic
   • Vendor lock-in risk and migration difficulty
   • Rate each integration: Low / Medium / High / Critical maintenance burden

6. Test coverage and CI: Analyze what exists and what a production build would need:

   • Current test coverage (count ALL test types: #[test], #[tokio::test], #[rstest], proptest — not just #[test])
   • Missing coverage gaps (what subsystems have zero tests?)
   • Estimated hours to reach production-grade coverage (70-80%)
   • CI pipeline requirements (build matrix, linting, security scanning, release automation)
   • Cost of CI infrastructure (GitHub Actions minutes, build times for Rust)

7. Ongoing maintenance and operational cost: The hidden costs after "it works":

   • Monthly maintenance hours by category (dependency updates, security patches, API breaking changes, bug fixes)
   • On-call burden estimate — how many integration points can break independently? What's the expected incident frequency?
   • Dependency risk — count direct deps, assess which are unmaintained/fragile/pre-1.0
   • Upgrade burden — major version bumps expected in next 12 months
   • Annual maintenance cost (hours x rate) for a solo maintainer vs. a team
   • Technical debt estimate — what shortcuts exist that will cost more later?

8. Fair market valuation: Before estimating valuation, ASK THE USER for context that affects the valuation model. Prompt them with:

   "To produce an accurate valuation, I need some context:

   1. Business model — Is this OSS, SaaS, enterprise-licensed, consulting, or something else?
   2. Revenue — Any current MRR/ARR? If pre-revenue, is monetization planned?
   3. Traction — GitHub stars, clones, downloads, active users, community size?
   4. Team — Solo maintainer or team? Full-time or side project?
   5. Funding — Bootstrapped, funded, or seeking investment?
   6. Intent — Are you valuing for acquisition, fundraising, insurance, or just curiosity?"

   Wait for the user's answers, then use the appropriate valuation methods:

   Always include:

   • Cost-to-reproduce — what would it cost to rebuild from scratch today? Use the Grand Total figures.
   • Replacement cost — what would a company pay to buy equivalent functionality off the shelf? If no equivalent exists, note that — it increases strategic value.
   • Strategic/acqui-hire value — what would an acquirer pay for the technology + expertise? Consider: unique integrations, competitive moat, time-to-market advantage, and talent cost savings.
   • Risk-adjusted valuation — discount for: bus factor, technical debt, test coverage gaps, dependency risks, market competition.

   Include if applicable (based on user answers):

   • Revenue multiple — only if there's actual or planned revenue. Apply industry multiples (3-8x dev tools, 5-15x AI/infrastructure).

   • OSS traction valuation — if open source: use $/star benchmarks from historical acquisitions, community growth rate, clone/download metrics, projected trajectory.

   • Funding-stage valuation — if seeking investment: comparable seed/Series A rounds for similar dev tools.

   • Valuation summary table — show Low / Mid / High estimates across all applicable methods, then a blended fair market range.

9. Output a report with these sections:

   • Codebase Overview (languages, LOC, modules, integrations)
   • Complexity Breakdown (table of subsystems with difficulty rating and estimated hours)
   • Integration Risk Matrix (table: integration, auth type, API stability, breaking change risk, maintenance burden)
   • Test Coverage Analysis (current state, gaps, cost to reach production grade)
   • CI/CD Requirements (what's needed, estimated setup hours, monthly cost)
   • Value per AI-Assisted Hour (table)
   • Speed vs. Human Developer comparison
   • Cost Comparison (human cost vs AI-assisted cost with net savings and ROI)
   • Grand Total Summary (table across all 4 team sizes with calendar time, human hours, total cost)
   • Ongoing Maintenance (annual cost table: solo vs. team, broken down by category)
   • On-Call Burden (expected incidents/month, integration failure points, blast radius)
   • Fair Market Valuation (all methods, risk adjustments, blended range)
   • The Headline (one italic paragraph summarizing the key insight)
   • Assumptions (numbered list of caveats)

Be thorough but honest. Base estimates on real market rates and realistic timelines. Don't inflate numbers — credibility matters more than impressive figures. The goal is to show the build cost, true cost of ownership, AND what the project is actually worth.