tech-stack-research

name	tech-stack-research
version	1.0.0
description	[Architecture] Use when you need research, analyze, and compare tech stack options as a solution architect.

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.

Quick Summary

Goal: Research, analyze, and compare tech stack options for each layer of the system. Act as a solution architect — derive technical requirements from business analysis, research current market, produce detailed comparison report, and present options to user for decision.

Workflow:

1. Load Business Context — Read business evaluation, domain model, refined PBI artifacts
2. Derive Technical Requirements — Map business needs to technical constraints
3. Research Per Layer — WebSearch top 3 options for each stack responsibility
4. Deep Compare — Pros/cons matrix, benchmarks, community health, team fit
5. Score & Rank — Weighted scoring across 8 criteria
6. Generate Report — Structured comparison report with recommendation
7. User Validation — Present findings, ask 5-8 questions, confirm choices

Key Rules:

• MANDATORY IMPORTANT MUST ATTENTION research minimum 3 options per stack layer
• MANDATORY IMPORTANT MUST ATTENTION include confidence % with evidence for every recommendation
• MANDATORY IMPORTANT MUST ATTENTION run user validation interview at end (never skip)
• All claims must cite sources (URL, benchmark, case study)
• Never recommend based on familiarity alone — evidence required

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

Step 1: Load Business Context

Read artifacts from prior workflow steps (search in plans/ and team-artifacts/):

• Business evaluation report (viability, scale, constraints)
• Domain model / ERD (complexity, entity count, relationships)
• Refined PBI (acceptance criteria, scope)
• Discovery interview notes (team skills, budget, timeline)

Extract and summarize:

Signal	Value	Source
Expected users	...	discovery interview
Domain complexity	Low/Med/High	domain model
Team skills	...	discovery interview
Budget constraint	...	business evaluation
Timeline	...	business evaluation
Compliance needs	...	business evaluation
Real-time needs	Yes/No	refined PBI
Integration complexity	Low/Med/High	domain model

Step 2: Derive Technical Requirements

Map business signals to technical requirements:

Business Signal	Technical Requirement	Priority
High user scale	Horizontal scaling, connection pooling	Must
Complex domain	Strong type system, ORM with migrations	Must
Real-time features	WebSocket/SSE support, event-driven arch	Must
Small team	Low learning curve, good DX, batteries-included	Should
Tight budget	Open-source, low hosting cost	Should
Compliance	Audit trail, encryption, auth framework	Must

MANDATORY IMPORTANT MUST ATTENTION validate derived requirements with user via AskUserQuestion before proceeding to research.

Step 3: Research Per Stack Layer

For EACH layer, research top 3 options using WebSearch (minimum 5 queries total):

Stack Layers to Evaluate

Layer	Example Options	Research Focus
Backend Framework	.NET, Node.js, Python, Go, Java	Performance, type safety, ecosystem
Frontend Framework	Angular, React, Vue, Svelte	DX, ecosystem, hiring, enterprise fit
Database	PostgreSQL, MongoDB, SQL Server	Scale, query complexity, cost
Messaging/Events	RabbitMQ, Kafka, Redis Streams	Throughput, reliability, complexity
Infrastructure	Docker+K8s, Serverless, PaaS	Cost, ops overhead, scaling
Auth	Keycloak, Auth0, custom	Cost, compliance, flexibility

WebSearch Queries (minimum 5 per layer)

"{option_A} vs {option_B} {current_year} comparison"
"{option} enterprise production case studies"
"{option} community size github stars"
"{option} performance benchmarks {use_case}"
"{option} security track record vulnerabilities"

Step 4: Deep Comparison Matrix

For EACH stack layer, produce a comparison table:

Criteria	Option A	Option B	Option C	Weight
Team Fit	score + rationale	...	...	High
Scalability	score + rationale	...	...	High
Time-to-Market	score + rationale	...	...	High
Ecosystem/Libs	score + rationale	...	...	Medium
Hiring Market	score + rationale	...	...	Medium
Cost (hosting)	score + rationale	...	...	Medium
Learning Curve	score + rationale	...	...	Medium
Community Health	score + rationale	...	...	Low

Scoring: 1-5 scale. Weight: High=3x, Medium=2x, Low=1x.

Per-Option Detail Block

For each option, document:

### {Layer}: {Option Name}

**Pros:**

- {Pro 1} — {evidence/source}
- {Pro 2} — {evidence/source}
- {Pro 3} — {evidence/source}

**Cons:**

- {Con 1} — {evidence/source}
- {Con 2} — {evidence/source}

**Best suited when:** {conditions}
**Not suitable when:** {conditions}
**Production examples:** {2-3 real companies using this}

Step 5: Weighted Score & Ranking

Calculate weighted total per option per layer. Present ranking:

### {Layer} Ranking

1. **{Option A}** — Score: {X}/100 — Confidence: {Y}%
2. **{Option B}** — Score: {X}/100 — Confidence: {Y}%
3. **{Option C}** — Score: {X}/100 — Confidence: {Y}%

**Recommendation:** {Option A}
**Why:** {2-3 sentence rationale linking to team skills, scale, and constraints}

Step 6: Generate Report

Write report to {plan-dir}/research/tech-stack-comparison.md with:

1. Executive summary (recommended full stack in 5 lines)
2. Technical requirements table (from Step 2)
3. Per-layer comparison matrices (from Step 4)
4. Per-layer rankings with recommendations (from Step 5)
5. Combined recommended stack diagram
6. Risk assessment for recommended stack
7. Alternative stack (second-best combo) for comparison
8. Unresolved questions

Report must be <=200 lines. Use tables over prose.

Step 7: User Validation Interview

MANDATORY IMPORTANT MUST ATTENTION present findings and ask 5-8 questions via AskUserQuestion:

Required Questions

1. Per-layer recommendation confirmation — "For {layer}, I recommend {option}. Agree?"
   • Options: Agree (Recommended) | Prefer {option B} | Need more research
2. Risk tolerance — "The recommended stack has {risk}. Acceptable?"
3. Team readiness — "Team needs to learn {X}. Training plan needed?"
4. Budget alignment — "Estimated infra cost: ${X}/month. Within budget?"
5. Timeline fit — "This stack enables MVP in {X} months. Acceptable?"

Optional Deep-Dive Questions (pick 2-3 based on context)

• "Should we consider {emerging tech} for {layer}?"
• "Any compliance requirements I haven't captured?"
• "Preference for managed services vs self-hosted?"
• "Monorepo or polyrepo for this team size?"

After user confirms, update report with final decisions and mark as status: confirmed.

Output

{plan-dir}/research/tech-stack-comparison.md    # Full comparison report
{plan-dir}/phase-02-tech-stack.md               # Final confirmed tech stack decisions

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MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION validate EVERY recommendation with user via AskUserQuestion — never auto-decide. MANDATORY IMPORTANT MUST ATTENTION include confidence % and evidence citations for all claims. MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality.

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Next Steps

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS after completing this skill, you MUST ATTENTION use AskUserQuestion to present these options. Do NOT skip because the task seems "simple" or "obvious" — the user decides:

• "/architecture-design (Recommended)" — Design solution architecture with chosen tech stack
• "/plan" — If architecture already decided
• "Skip, continue manually" — user decides

Council escalation (always-offer, second prompt)

After the existing ## Next Steps prompt above resolves, present a second, independent AskUserQuestion call:

• "Skip council — proceed with chosen stack (Recommended)" — Continue with the selected tech stack as-is.
• "Escalate to /llm-council" — Run 11 sub-agent council. Best applied when 2+ stacks score within 15% on the comparison matrix or you have unfamiliar/strategic dependencies. Cheaper alternatives: /why-review, /plan-validate.

Prompt-Enhance Closing Anchors

• IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval
• IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution
• IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence
• IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses

Closing Reminders

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION validate decisions with user via AskUserQuestion — never auto-decide. MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality.

• MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.
• MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using TaskCreate.

[IMPORTANT] Analyze how big the task is and break it into many small todo tasks systematically before starting — this is very important.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

AI Mistake Prevention — Failure modes to avoid on every task:

• Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain.
• Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path.
• When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code.
• Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks.
• Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis.
• Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly.
• Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

MANDATORY IMPORTANT MUST ATTENTION use TaskCreate to break ALL work into small tasks BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION use AskUserQuestion at EVERY decision point — never assume user preferences. MANDATORY IMPORTANT MUST ATTENTION research top 3 options per stack layer, compare with evidence, present report with recommendation + confidence %.

External Memory: For complex or lengthy work (research, analysis, scan, review), write intermediate findings and final results to a report file in plans/reports/ — prevents context loss and serves as deliverable.

Evidence Gate: MANDATORY IMPORTANT MUST ATTENTION — every claim, finding, and recommendation requires file:line proof or traced evidence with confidence percentage (>80% to act, <80% must verify first).