source-command-methodology-advisor

name	source-command-methodology-advisor
description	Analyzes your codebase and asks 3 targeted questions to recommend the right AI-assisted development methodology stack

source-command-methodology-advisor

Use this skill when the user asks to run the migrated source command methodology-advisor.

Command Template

Methodology Advisor

Analyze this project and recommend the best AI-assisted development methodology stack. Read what you can from the codebase first, then ask only what you cannot infer.

Time: 2-4 minutes | Output: One recommended stack + contextual quick start

---

Phase 1 — Silent codebase analysis

Run these reads silently. Do not output results yet — build an internal picture only.

1.1 Project identity

# Config files
cat AGENTS.md 2>/dev/null || cat AGENTS.md 2>/dev/null
cat package.json 2>/dev/null | grep -E '"name"|"description"|"scripts"' | head -10
cat Cargo.toml 2>/dev/null | grep -E '^name|^description' | head -5
cat pyproject.toml 2>/dev/null | grep -E '^name|^description' | head -5
cat go.mod 2>/dev/null | head -3

1.2 Team size

# Unique contributors in last 90 days
git log --since="90 days ago" --format="%ae" 2>/dev/null | sort -u | wc -l
# Total commits
git log --oneline 2>/dev/null | wc -l

1.3 Test maturity

# Test files exist?
find . -name "*.test.*" -o -name "*.spec.*" -o -name "*_test.*" -o -name "test_*.py" \
  2>/dev/null | grep -v node_modules | grep -v ".git" | wc -l
# Test framework hints
grep -rn --include="*.json" --include="*.toml" --include="*.yaml" \
  -l "jest\|vitest\|pytest\|rspec\|mocha\|cypress\|playwright" \
  2>/dev/null | grep -v node_modules | head -5
# CI config
ls .github/workflows/*.yml 2>/dev/null | wc -l
ls .gitlab-ci.yml .circleci/config.yml 2>/dev/null | wc -l

1.4 Spec and documentation signals

# Spec files
find . -name "*.spec.md" -o -name "SPEC*.md" -o -name "spec.md" -o -name "DESIGN*.md" \
  -o -name "ADR*.md" -o -name "RFC*.md" \
  2>/dev/null | grep -v node_modules | grep -v ".git" | head -10
# OpenAPI / contract files
find . -name "openapi*.yaml" -o -name "openapi*.json" -o -name "swagger*.yaml" \
  -o -name "*.proto" \
  2>/dev/null | grep -v node_modules | head -5
# BDD feature files
find . -name "*.feature" 2>/dev/null | grep -v node_modules | wc -l

1.5 Codebase size and structure

# File count (rough)
find . -type f \( -name "*.ts" -o -name "*.tsx" -o -name "*.js" -o -name "*.py" \
  -o -name "*.rs" -o -name "*.go" -o -name "*.java" -o -name "*.rb" \) \
  2>/dev/null | grep -v node_modules | grep -v ".git" | wc -l
# Services / packages (monorepo signal)
ls packages/ apps/ services/ 2>/dev/null | head -10

1.6 AI and LLM signals

# LLM API usage in code
grep -rn --include="*.ts" --include="*.py" --include="*.js" \
  -l "anthropic\|openai\|groq\|mistral\|langchain\|llm\|ChatCompletion\|Codex" \
  2>/dev/null | grep -v node_modules | grep -v ".git" | head -5
# Eval framework hints
find . -name "evals*" -o -name "*eval*" -type d 2>/dev/null | grep -v node_modules | head -5

---

Phase 2 — Score the 8 stacks

Using what you found, score each stack 0-10 based on fit signals:

Stack	Key signals that boost the score
solo-mvp	1 contributor, few files, no CI yet, greenfield
team-greenfield	2-10 contributors, new project, no legacy files
microservices	packages/, services/, OpenAPI files, .proto
brownfield-saas	High commit count, large file count, few test files
enterprise-gov	10+ contributors, CI, ADR files, AGENTS.md
llm-native	LLM imports, eval dirs, AI product signals
power-solo	1 contributor, high commit rate, iterative commits
plan-moderate	Mixed signals, AGENTS.md present, moderate size

---

Phase 3 — Ask only what you cannot infer

After the silent analysis, present your preliminary picture to the user in 2-3 lines, then ask exactly 3 questions. No more.

Format:

From your codebase I can see: [2-3 concrete observations].
Before recommending, 3 quick questions:

1. [Pain point question — pick the most relevant from below]
2. [Deploy frequency — if not inferable from CI/CD signals]
3. [Setup appetite — how much ceremony are you willing to invest?]

Question bank — pick the 3 most relevant given what you found:

• Pain: "What slows you down most right now — regressions, unclear requirements, context rot between sessions, or no traceability?"
• Pain: "When Codex generates a large chunk of code, what is your biggest worry — quality, drift from spec, or losing track of what was built?"
• Deploy: "How often do you ship to production — multiple times a day, weekly, or on longer release cycles?"
• Deploy: "Is this a product with real users today, a prototype, or an internal tool?"
• Governance: "How much initial setup are you willing to invest — none (just start), 30 minutes, or half a day?"
• Governance: "Does anyone outside your dev team (PM, QA, compliance) need to validate what gets built?"
• AI product: "Does your product expose AI-generated outputs directly to end users?"
• Scale: "Do multiple services or teams need to agree on API contracts before implementing?"

---

Phase 4 — Recommendation

Output the recommendation in this structure:

---

Your Stack: [Stack Name] [icon]

Why this fits your project:

• [Finding from Phase 1] → [explains this stack choice]
• [Finding from Phase 1] → [explains this stack choice]
• [Answer to question N] → [explains this stack choice]

Methodologies included: [Method A] + [Method B] (+ [Method C] if applicable)

What this looks like in practice: [2-3 sentences describing the concrete workflow for THIS project, using actual file names or paths found.]

Quick start for your project:

1. [Concrete first step using actual project context]
2. [Second step]
3. [Third step]

Before you start, note:

• [One honest trade-off or limitation of this stack]
• [One thing to watch out for given what you found]

Go deeper: https://cc.bruniaux.com/methodologies/ — interactive quiz and full stack comparison Full methodology guide: https://cc.bruniaux.com/guide/methodologies/

---

Stack reference (internal)

Use this to map your scoring to quick-start language:

solo-mvp (SDD + TDD): Write feature spec in AGENTS.md → "Write failing tests for this spec, then implement until green."

team-greenfield (Spec Kit + TDD + BDD): /speckit.constitution → Given/When/Then scenarios with PM → TDD each scenario.

microservices (CDD + Specmatic + TDD): Write OpenAPI spec first → Specmatic for contract tests → TDD implementation.

brownfield-saas (OpenSpec + BDD + JiTTesting): OpenSpec captures current state → BDD for changed behavior → pre-merge: "Generate tests that catch regressions in this diff."

enterprise-gov (BMAD + Spec Kit + Specmatic): constitution.md → agent role definitions → Spec Kit requirements → Specmatic contract enforcement.

llm-native (Eval-Driven + Multi-Agent): Define eval criteria (accuracy, safety, format) → build eval harness → iterate until evals pass.

power-solo (TDD + Ralph Loop + Iterative): Tight test loop → fresh context per task via git stash + progress files → "Keep iterating until all tests pass and lint is clean."

plan-moderate (Plan-First + SDD + Context Engineering): Every complex task starts in Plan Mode (Shift+Tab) → validate → write spec in AGENTS.md → execute with progressive context loading.