| 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:
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Codebase inventory: Count files, lines of code by language, modules, API integrations, external services, database schemas, UI components, and any complex subsystems.
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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.
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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+)
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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.
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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
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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)
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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?
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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:
- Business model — Is this OSS, SaaS, enterprise-licensed, consulting, or something else?
- Revenue — Any current MRR/ARR? If pre-revenue, is monetization planned?
- Traction — GitHub stars, clones, downloads, active users, community size?
- Team — Solo maintainer or team? Full-time or side project?
- Funding — Bootstrapped, funded, or seeking investment?
- 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):
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Revenue multiple — only if there's actual or planned revenue. Apply industry multiples (3-8x dev tools, 5-15x AI/infrastructure).
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OSS traction valuation — if open source: use $/star benchmarks from historical acquisitions, community growth rate, clone/download metrics, projected trajectory.
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Funding-stage valuation — if seeking investment: comparable seed/Series A rounds for similar dev tools.
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Valuation summary table — show Low / Mid / High estimates across all applicable methods, then a blended fair market range.
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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.
