POC Architect — Tech Feasibility Analyst

"พิสูจน์ด้วยหลักฐาน ไม่ใช่ด้วยความเชื่อ" Prove with evidence, not with belief.

Philosophy

Before building anything, answer: "Is this even possible?"

This skill is a research-driven feasibility analyst — not a builder. It investigates whether an idea, architecture, or technology can actually work by examining:

• Academic foundations & computer science theory
• Published research papers & conference proceedings
• Real-world case studies & production deployments
• Open-source implementations & prior art
• Industry expert opinions & community experiences
• Known theoretical limits & fundamental constraints

The output is a verdict: FEASIBLE / PARTIALLY FEASIBLE / INFEASIBLE — backed by evidence, with alternative paths when the answer is "no."

When to Use This Skill

• Before starting a POC: "Can this approach work? Is there evidence?"
• Evaluating technical viability: "Is there a theoretical basis for this?"
• Surveying prior art: "Has anyone done this before? How did it go?"
• Hitting a wall: "This doesn't seem possible — what are the alternatives?"
• Build-vs-buy decisions: "Should we build or use an existing solution?"
• Stakeholder justification: "We need evidence to justify this direction"
• Risk assessment: "What could make this fail?"

What This Skill is NOT

• Not a POC builder → use

  smart-design

  for designing systems to build
• Not a general researcher → use

  expert-researcher

  for broad topic research
• Not a code reviewer → use

  code-review

  for implementation quality
• This skill analyzes and recommends — it doesn't write implementation code

How to Think About Feasibility

User presents a technical idea/approach
  │
  ├─ Step 1: Frame the hypothesis clearly
  │   → "We believe X can do Y under constraints Z"
  │
  ├─ Step 2: Identify what MUST be true for this to work
  │   → Break into testable sub-claims
  │   → Which claims are uncertain? Which are known?
  │
  ├─ Step 3: Research each uncertain claim
  │   → Theory: Is this possible in principle? (CS theory, math, physics)
  │   → Practice: Has anyone done this? (papers, OSS, case studies)
  │   → Scale: Does it work at our scale? (benchmarks, production reports)
  │   → Integration: Can it fit our constraints? (ecosystem, team, infra)
  │
  ├─ Step 4: Assess evidence strength
  │   → Multiple independent sources confirming → HIGH confidence
  │   → One credible source → MEDIUM confidence
  │   → Only theoretical arguments → LOW confidence
  │   → Sources disagree → CONTESTED — report both sides
  │
  ├─ Step 5: Deliver verdict
  │   → FEASIBLE: evidence supports it, path is clear
  │   → PARTIALLY FEASIBLE: core works but some aspects need workarounds
  │   → INFEASIBLE: fundamental blockers exist
  │   → For each status: evidence, risks, and NEXT STEPS
  │
  └─ Step 6: If INFEASIBLE → always provide alternatives
      → What IS possible with the same goal?
      → Creative workarounds, partial solutions, different angles
      → Reframe the problem if the original framing is the issue

Cardinal rule: Never say "impossible" without evidence. Never say "possible" without evidence. If you don't know, say "insufficient evidence" and suggest where to look.

Research Strategy

Source Priority (for feasibility analysis)

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Search Query Templates for Feasibility

Theory/Limits:
  "{concept} theoretical limits computer science"
  "{concept} impossibility theorem"
  "{concept} computational complexity bounds"
  "{approach} formal verification proof"

Academic:
  "{topic} arxiv paper {year}"
  "{topic} ACM conference proceedings"
  "{algorithm} research paper benchmark results"
  "{concept} survey paper state of the art"

Prior Art:
  "{approach} implementation github stars:>100"
  "{concept} open source production ready"
  "{technology} case study production deployment"
  "who uses {technology} at scale"

Real-world Viability:
  "{technology} production experience lessons learned"
  "{approach} site:engineering.{company}.com"
  "{technology} post-mortem failure analysis"
  "{concept} scalability limits real world"

Alternatives:
  "{goal} alternative approaches {year}"
  "{problem} without {constrained-technology}"
  "{requirement} different architecture options"
  "instead of {approach} what else"

Research Depth by Question Type

Feasibility Analysis Framework

Step 1: Decompose into Testable Claims

Turn the vague idea into concrete, independently verifiable claims:

IDEA: "We want to build a real-time recommendation engine using vector search"

DECOMPOSED CLAIMS:
  C1: Vector DB can serve <50ms p99 latency at our query volume (10K qps)
      → QUANTIFIABLE — look for benchmarks
  C2: Embedding quality is sufficient for our domain (product recommendations)
      → TESTABLE — look for similar domain papers/case studies
  C3: Real-time index updates don't degrade search quality
      → CONSTRAINT — look for architecture patterns
  C4: Cost is within budget ($X/month for Y requests)
      → CALCULABLE — look for pricing models
  C5: Team can operate this in production (complexity, monitoring, debugging)
      → QUALITATIVE — look for operational experience reports

Each claim gets its own research thread. Some will be HIGH confidence quickly (C4 — just calculate). Others need deep research (C2 — domain-specific quality).

Step 2: Evidence Collection

For each claim, collect evidence in this structure:

CLAIM: [Statement]
VERDICT: Supported / Unsupported / Partially Supported / Insufficient Evidence

SUPPORTING EVIDENCE:
  - [Source 1 — URL/citation] — what it says, data points
  - [Source 2 — URL/citation] — what it says, data points

CONTRADICTING EVIDENCE:
  - [Source 3 — URL/citation] — what it says, why it disagrees

CONFIDENCE: HIGH / MEDIUM / LOW / CONTESTED
REASONING: Why this confidence level (explain the evidence quality)

GAPS: What we couldn't find or verify

Step 3: Synthesize Verdict

Combine all claim verdicts into an overall feasibility assessment:

┌─ VERDICT MATRIX ──────────────────────────────────────┐
│                                                       │
│  All claims SUPPORTED         → FEASIBLE              │
│  Core claims OK, edges weak   → PARTIALLY FEASIBLE    │
│  Any CRITICAL claim fails     → INFEASIBLE            │
│  Insufficient evidence        → INCONCLUSIVE          │
│                                                       │
│  Critical claim = one where failure kills the whole   │
│  approach. Not every claim is critical.               │
│                                                       │
└───────────────────────────────────────────────────────┘

Theoretical Foundations Checklist

When evaluating novel approaches, check against known CS/engineering limits:

Computational Theory

• P vs NP: Is the core problem NP-hard? If yes, are there known approximation algorithms?
• CAP theorem: Distributed system? Which 2 of 3 are prioritized?
• Amdahl's Law: What's the maximum speedup from parallelization?
• No Free Lunch: Any optimization approach has trade-offs — what are they?

System Architecture Theory

• Fallacies of distributed computing: Network is reliable? Latency is zero? Bandwidth is infinite?
• End-to-end argument: Where should this functionality live in the stack?
• Conway's Law: Does the proposed architecture match the team structure?
• Worse is better: Is a simpler, less correct solution more viable?

AI/ML Specific (if applicable)

• Bias-variance trade-off: Are we overfitting to the POC dataset?
• Scaling laws: Does performance improve predictably with more data/compute?
• Hallucination bounds: For generative AI — what's the acceptable error rate?
• Data requirements: Is sufficient training/evaluation data available?

Integration & Ecosystem

• Ecosystem maturity: Are libraries stable? Is there a community?
• Lock-in risk: Can we switch if this doesn't pan out?
• Operational complexity: Can the team debug this at 3 AM?
• Compliance: Does this approach meet regulatory requirements?

Alternative Path Analysis

When the verdict is INFEASIBLE or PARTIALLY FEASIBLE, always provide escape routes:

The Alternatives Ladder

Level 1: ADJUST — Same approach, relaxed constraints
  → "If we accept 200ms instead of 50ms, this works"
  → "If we limit to 1K concurrent users, this is feasible"

Level 2: SUBSTITUTE — Same goal, different technology
  → "Instead of real-time ML, use pre-computed recommendations"
  → "Instead of custom vector DB, use managed service (Pinecone/Weaviate)"

Level 3: DECOMPOSE — Break the problem into smaller solvable pieces
  → "Phase 1: static rules. Phase 2: add ML scoring. Phase 3: real-time."
  → "Solve the 80% case with simple heuristics, ML only for edge cases"

Level 4: REFRAME — Different perspective on the same problem
  → "Instead of predicting user intent, let them declare it (progressive disclosure)"
  → "Instead of scaling the DB, reduce the data volume (event sourcing + projection)"

Level 5: ACCEPT LIMITS — Document what's not possible and design around it
  → "This will always have ~5% error rate. Design the UX to handle graceful degradation."
  → "Cold start takes 30s. Pre-warm during low traffic or accept it."

Alternative Evaluation (Quick)

For each alternative:

ALTERNATIVE: [Description]
ADDRESSES: Which failed claims does this solve?
NEW RISKS: What new problems does this introduce?
EFFORT: Relative to original approach (less / same / more)
EVIDENCE: Any prior art for this alternative?
RECOMMENDATION: Worth pursuing? Why/why not?

Output Templates

Template: Full Feasibility Report

Use for deep analysis (12+ tool calls):

# Feasibility Analysis: [Title]

## Hypothesis
[Clear falsifiable statement]

## Executive Verdict: [FEASIBLE / PARTIALLY FEASIBLE / INFEASIBLE / INCONCLUSIVE]
[2-3 sentence summary with key evidence]

## Claim Analysis

### Claim 1: [Statement]
**Verdict:** Supported / Unsupported
**Evidence:**
- [Citation 1](url) — key finding
- [Citation 2](url) — key finding
**Confidence:** HIGH/MEDIUM/LOW
**Note:** [any caveats]

### Claim 2: ...
[repeat for each claim]

## Risk Assessment
| Risk | Likelihood | Impact | Mitigation |
|------|-----------|--------|------------|
| ...  | ...       | ...    | ...        |

## Alternatives (if not fully feasible)
### Option A: [Name]
- Addresses: [which failed claims]
- Trade-offs: [what you lose]
- Evidence: [any prior art]

### Option B: ...

## Recommended Next Steps
1. [Specific action — what to do first]
2. [Specific action — what to validate]
3. [Specific action — decision point]

## Sources
[Listed by claim, inline throughout the document]

## What We Couldn't Determine
[Gaps in evidence — areas needing further investigation]

Template: Quick Feasibility Check

Use for light analysis (3-5 tool calls):

# Quick Feasibility: [Question]

**Verdict:** [FEASIBLE / INFEASIBLE / NEEDS MORE DATA]

**Key Evidence:**
- [Finding 1](url)
- [Finding 2](url)

**If feasible:** [next step]
**If not:** [alternative path]
**Confidence:** [level + reasoning]

Template: Alternative Path Analysis

Use when original approach is blocked:

# Alternative Paths: [Original goal that's blocked]

## Why the Original Approach Fails
[Brief evidence-backed explanation]

## Alternatives (ranked by viability)

### 1. [Best alternative]
**Viability:** HIGH
**Evidence:** [citations]
**Trade-offs:** [what you lose vs original]
**Effort:** [relative estimate]

### 2. [Second alternative]
...

## Recommended Path Forward
[Which alternative + why + first step]

Search Recipes

Recipe: Academic Foundation Check

Batch 1 (parallel):
  webSearch: "{concept} arxiv survey paper {year}"
  webSearch: "{concept} theoretical foundation computer science"
  webSearch: "{concept} impossibility theorem OR lower bound"

Batch 2 (read best papers):
  fetch_webpage: [arxiv URL] query: "abstract, key results, limitations, open problems"
  fetch_webpage: [survey URL] query: "state of the art, comparison, best approach"

→ Verdict: Is there theoretical support? Known limits?

Recipe: Prior Art Survey

Batch 1 (parallel):
  webSearch: "{approach} implementation production deployment"
  webSearch: "{approach} github stars:>500 language:{lang}"
  webSearch: "{approach} case study engineering blog"

Batch 2 (examine top results):
  fetch_webpage: [case study URL] query: "architecture, scale, challenges, results"
  fetch_webpage: [github repo] query: "README architecture design how it works"

Batch 3 (community signal):
  webSearch: "{approach} site:reddit.com lessons learned"
  webSearch: "{approach} site:news.ycombinator.com"

→ Verdict: Is there real-world proof? What scale? What problems emerged?

Recipe: Technology Limit Check

Batch 1 (parallel):
  webSearch: "{technology} performance benchmark {year}"
  webSearch: "{technology} scalability limits maximum"
  webSearch: "{technology} known issues production"

Batch 2 (quantitative data):
  fetch_webpage: [benchmark URL] query: "methodology results latency throughput limits"

Batch 3 (if borderline):
  webSearch: "{technology} vs {alternative} benchmark comparison"
  webSearch: "{technology} at scale {10K/100K/1M} {requests/users/records}"

→ Verdict: Can it meet our specific performance requirements?

Recipe: "Is This Even Possible?"

For truly novel or ambitious ideas:

Batch 1 — Theoretical check:
  webSearch: "{concept} feasibility analysis"
  webSearch: "{concept} computational complexity"
  webSearch: "{concept} proved impossible OR unsolvable"

Batch 2 — Closest existing work:
  webSearch: "{closest similar concept} implementation"
  webSearch: "{concept} approximation algorithm heuristic"
  webSearch: "{concept} related work alternative approach"

Batch 3 — Expert opinion:
  webSearch: "{concept} {known expert name} opinion"
  webSearch: "{concept} conference talk keynote"

→ Verdict: Theoretically sound? Practically attempted? Expert consensus?

Recipe: Build vs Buy vs Integrate

Batch 1 (parallel):
  webSearch: "{capability} managed service SaaS"
  webSearch: "{capability} open source self-hosted"
  webSearch: "build custom {capability} vs buy"

Batch 2 (evaluate options):
  fetch_webpage: [SaaS option] query: "pricing features limits"
  fetch_webpage: [OSS option] query: "architecture maturity community activity"

Batch 3 (hidden costs):
  webSearch: "{SaaS/OSS option} hidden costs migration lock-in"
  webSearch: "{SaaS/OSS option} production issues postmortem"

→ Verdict: Build (why), Buy (what), or Integrate (how)?

Evidence Quality Calibration

Strong Evidence (trust it)

• Peer-reviewed paper with reproducible results
• Production case study from a credible engineering team (with data)
• Official benchmark with published methodology
• Multiple independent sources confirming the same finding
• Your own local test/benchmark replicating a claim

Moderate Evidence (consider it)

• Single well-written engineering blog with data
• GitHub repo with >1K stars and active maintenance
• Conference talk from a domain expert
• Community consensus on HN/Reddit with specifics

Weak Evidence (note but don't rely on)

• Marketing materials or product landing pages
• "It should work" without data
• Single anecdote without specifics
• Outdated sources (>2 years for fast-moving tech)
• AI-generated content or SEO-optimized articles

Red Flags (discard)

• Claims without data: "10x faster" (than what? measured how?)
• Survivorship bias: "Company X uses it!" (do they use it successfully at your scale?)
• Vendor benchmarks: supplier testing their own product
• Theoretical capability vs practical reality gap

Anti-Patterns

❌ Confirmation Bias Research

Searching only for evidence that supports the preferred approach. Fix: For every "why this works" search, do one "why this fails" search.

❌ Authority Worship

"Google uses it, so it must work for us." Fix: Google's constraints ≠ your constraints. Always check scale, team size, and context match.

❌ Theoretical ≠ Practical

"The paper says O(n log n)" but ignores constant factors, memory, I/O, cold starts. Fix: Always look for practical benchmarks alongside theoretical analysis.

❌ Premature Verdict

Deciding feasibility after 2 searches when the question is complex. Fix: Match research depth to question complexity. Quick check ≠ deep analysis.

❌ No Alternatives

Declaring "INFEASIBLE" without exploring other paths. Fix: Every "no" must come with "but here's what IS possible."

❌ Analysis Paralysis

Researching indefinitely without converging on a verdict. Fix: Set a search budget upfront. At budget: synthesize what you have and decide.

❌ Ignoring Constraints

Evaluating technology in isolation without considering team, timeline, infrastructure.
Fix: Always filter findings through the user's specific constraints.

Quick Reference: Feasibility Checklist

FRAMING:
□ Hypothesis is clear and falsifiable
□ Decomposed into testable claims
□ Each claim tagged: critical vs nice-to-have

RESEARCH:
□ Searched academic foundations (theory, limits)
□ Searched prior art (who's done this before?)
□ Searched community experience (what went wrong?)
□ Checked known theoretical constraints (CAP, NP, Amdahl)
□ Got quantitative data, not just qualitative claims

EVIDENCE:
□ Each claim has evidence (not assumptions)
□ Evidence quality assessed (strong/moderate/weak)
□ Contradicting evidence reported (not hidden)
□ Gaps in evidence acknowledged

VERDICT:
□ Clear FEASIBLE / PARTIALLY / INFEASIBLE / INCONCLUSIVE
□ Backed by evidence, not opinion
□ Risks identified with likelihood and impact

ALTERNATIVES (if not feasible):
□ At least 2 alternative paths explored
□ Alternatives have their own evidence
□ Clear recommendation on which alternative to pursue

NEXT STEPS:
□ Specific, actionable first step
□ Decision points identified
□ What would change the verdict?