Research & Intelligence
Strategic Technology Assessment
Strategic Technology Assessment helps organizations move from a fuzzy sense of technical direction to a pragmatic roadmap. It captures current state, identifies gaps, and turns broad ambitions into recommendations that can be sequenced and discussed. This skill is ideal for consulting teams, technology leaders, and operators planning change across products, platforms, or delivery functions. It supports roadmap conversations where the challenge is not a lack of ideas, but a lack of structure and prioritization. What makes it production-grade is its orientation toward action. The output balances assessment with execution by combining gaps, recommendations, and staged roadmap elements that can feed planning and budget conversations immediately.
One-Time Purchase
$19.99
Strategic Technology Assessment: NovaTech Solutions
Prepared for: CTO and Board Technology Committee Scope: Full-stack product engineering, infrastructure, and data platform Method: Engineering interviews (12), repo and incident review, deploy and ticket telemetry from the last two quarters
Executive Summary
Headline
NovaTech's technology organization is operationally functional but architecturally fragile. The core product is a Django monolith deployed via hand-run SSH scripts, with no CI/CD, no automated tests, and a deploy cadence that has slipped from weekly to monthly inside the last year because every release is a regression risk. The single highest-leverage investment is a basic deployment pipeline (Phase 1, 4–6 weeks) — it unblocks every downstream improvement and pays back inside one quarter. Estimated cost of inaction across the top three items is $380K–$520K per year in lost engineering velocity.
Overall posture: Degraded — recoverable inside two quarters
Health Scorecard
| System | Deploy cadence | Incident rate | MTTR | Dependency currency | Health |
|---|---|---|---|---|---|
| Core product (Django monolith) | Monthly (was weekly) | ~3 / month | 4–8 hours | Django 3.2 (EOL) | Degraded |
| Data pipeline (Airflow) | N/A — DAG-driven | Low (~1 / quarter) | Hours | Current | Adequate |
| Customer portal (React) | Weekly | Low | <1 hour | Current | Strong |
| Internal admin tool | Ad hoc | Low | Days (low urgency) | One major version behind | Adequate |
| Identity & SSO | Quarterly | None this year | N/A | Current | Strong |
Current Stack vs. Proposed Stack
Current state
Manual, monolith, brittle
How releases work today
Proposed state (12 months)
Automated, layered, recoverable
After Phase 1–3 completion
Strategic Priorities
Weighted priority order — next 12 months
Technical Debt Register
| Item | Category | Business impact | 12-month cost of inaction | Severity |
|---|---|---|---|---|
| No CI/CD pipeline | Infrastructure | Every deploy is a manual risk; on-call burden compounds | ~$180K engineer time on manual deploys | High |
| No automated tests | Process | Regression fear slows delivery; bugs caught in production | ~$120K bug-fix rework + opportunity cost | High |
| Django 3.2 (EOL) | Infrastructure | No security patches; package ecosystem moving on | Unquantified security exposure + forced upgrade later | Medium |
| Manual infra changes | Infrastructure | Drift between staging and prod; recovery is bespoke | ~$80K in incident hours per year | Medium |
| Monolith billing module | Architecture | Billing changes block all other deploys | Velocity tax across 4 product squads | Medium |
| No service ownership map | Process | New-hire ramp is slow; on-call routing is tribal | Onboarding drag, ~30 days per hire | Low |
Phased Roadmap
Phase 1 — Stabilize (0–90 days)
| Initiative | Effort | Success criterion |
|---|---|---|
| GitHub Actions CI/CD for core product | Medium (3–4 wks) | Every merge to main auto-deploys to staging |
| Critical-path test coverage | Medium (4–6 wks) | 40% coverage on checkout, auth, billing |
| Django 3.2 → 5.x upgrade | Large (6–8 wks) | All tests pass on 5.x; staging verified for one full week |
Phase 2 — Layer (3–6 months)
| Initiative | Effort | Success criterion |
|---|---|---|
| Terraform baseline for prod + staging | Medium | All infra changes go through PR review |
| Extract billing module behind a service interface | Large | Billing deploys independently of the rest of the monolith |
| Observability baseline (structured logs + traces) | Medium | MTTR drops below 2 hours on the next two incidents |
Phase 3 — Compound (6–12 months)
| Initiative | Effort | Success criterion |
|---|---|---|
| Replace admin tool with internal app on the modern stack | Large | Admin tool no longer blocks production migrations |
| Service catalog with ownership + on-call routing | Small | Every service has a named owner and a runbook |
| Cost & capacity review cadence | Small | Quarterly review with finance; budget variance under 10% |
Recommendations
Do this first
Stand up CI/CD before anything else. Every other initiative — the Django upgrade, the test backfill, the billing split — is gated on having a safe way to ship. Trying to land any of them on top of hand-run SSH deploys will either fail or burn so much engineering attention that nothing else moves.
Do not skip the test backfill
Upgrading Django without test coverage is a coin flip. The framework upgrade and the test backfill should be sequenced together, with the tests landing first so the upgrade has a regression net.
Re-platform the admin tool last
It is the most visible piece of legacy and the most tempting to rewrite first. Resist. Customer-facing reliability comes from the core product; the admin tool can wait until Phase 3.
Cultural payoff
The DX win matters as much as the technical win. A deployable, testable monolith restores engineer confidence, lowers attrition risk, and makes the team a better target for senior hiring. Plan internal communication around each Phase 1 milestone.
This is an operational technology assessment, not a procurement or audit document. Numbers are estimates based on observed cadence and engineering interview data; verify with finance before any board-level commitment. Names and figures are illustrative.
This sample illustrates the skill's output format. Names, metrics, and operational details are illustrative unless the artifact explicitly analyzes public information.
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Includes support for Claude Code, Codex, OpenClaw, and Google Antigravity in the same license.
Also in Business Intelligence Suite
Bundle price: $55. Compare this skill with the full workflow bundle or Pro access.
Best for
New CTOs in their first 90 days, fractional technology leaders writing an opening report for a client, and engineering directors building the case for the next year of platform investment. Most useful when the audience is a board or exec team that needs a phased, sequenced view rather than an unranked wishlist.
Not ideal for
Healthy, well-instrumented engineering organizations where the highest-impact gaps have already been identified — the assessment shape implies more triage value than it can add. Also a poor fit as a substitute for an actual platform team operating model; the output is a roadmap, not an org design.
Included in this purchase
- Claude Code, Codex, OpenClaw, and Google Antigravity skill files.
- Setup guidance for the right adapter in your workspace.
- One-time license for the purchased skill version.
Setup
Plan for a short copy-and-configure setup in your preferred agent workspace. No custom integration is required for the skill file itself.
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Future Updates
This purchase includes the current version of the skill. If you want future adapter updates — meaning compatibility and packaging updates as supported platforms evolve — plus new catalog additions included automatically, upgrade to Pro.