多角色AI工程指导工具

claude mcp add sensei -- uvx sensei-mcp

User: "Write an API endpoint"
Claude: [reasons about design]
Claude: [writes code]
Output Style: [formats the response]  ← Too late!

User: "Write an API endpoint"
Sensei: [injects API contracts, security, multi-tenancy standards]
Claude: [reasons WITH standards in context]
Claude: [writes code that follows standards]

# Get full Security Sentinel content
content = get_persona_content(
  persona_name="security-sentinel",
  include_metadata=True  # Optional: includes description and expertise
)
# Returns: Complete SKILL.md with principles, personality, expertise, guidelines

# Claude then uses this content as context to analyze your query
# Example workflow:
# 1. Claude suggests personas → ["security-sentinel", "api-platform-engineer"]
# 2. Claude gets content for each → Full SKILL.md files
# 3. Claude analyzes from each perspective using the content
# 4. Claude synthesizes all perspectives into recommendation

# Get persona suggestions for a query
suggestions = suggest_personas_for_query(
  query="How should we handle authentication for our API?",
  max_suggestions=5,  # Maximum number of suggestions (default: 5)
  context_hint="SECURITY"  # Optional: force specific context
)
# Returns JSON:
# {
#   "query": "How should we handle authentication...",
#   "detected_context": "SECURITY",
#   "suggestions": [
#     {
#       "name": "security-sentinel",
#       "display_name": "Security Sentinel",
#       "relevance": 0.95,
#       "rationale": "Expert in authentication, security"
#     },
#     {
#       "name": "api-platform-engineer",
#       "display_name": "API Platform Engineer",
#       "relevance": 0.82,
#       "rationale": "Expert in API design, contracts"
#     }
#   ]
# }

# Auto-detect context from query
suggest_personas_for_query(
  query="Our AWS bill is too high",
  max_suggestions=3
)
# Returns: finops-optimizer, pragmatic-architect, site-reliability-engineer

# Get full session context
context = get_session_context(
  session_id="saas-backend",
  project_root="/path/to/repo"  # Optional: for project-local sessions
)
# Returns JSON:
# {
#   "session_id": "saas-backend",
#   "active_constraints": ["AWS only", "Python 3.11+", "PostgreSQL"],
#   "patterns_agreed": ["Use FastAPI", "JWT auth"],
#   "recent_decisions": [
#     {
#       "id": "dec-001",
#       "category": "architecture",
#       "description": "Use PostgreSQL for primary data store",
#       "rationale": "Team expertise, ACID guarantees",
#       "timestamp": "2025-01-23T10:30:00"
#     }
#   ]
# }

# Claude includes this context when analyzing to ensure consistency

# After Claude analyzes using persona content
record_consultation(
  query="Should we migrate to microservices?",
  personas_used=["pragmatic-architect", "site-reliability-engineer", "finops-optimizer"],
  session_id="saas-backend",
  project_root="/path/to/repo",  # Optional
  synthesis="[Claude's complete analysis and recommendation]"  # Optional
)
# Returns: "✅ Consultation recorded: consult_42"

# This consultation is now in session history for:
# - Analytics (get_session_insights)
# - Export (export_session_summary)
# - Future context

# Run the interactive demo
sensei-mcp --demo

# Shows 5 real-world scenarios:
# - Architecture Decision (microservices migration)
# - Production Crisis (database outage)
# - Security Review (authentication audit)
# - Cost Optimization (cloud spending)
# - Code Quality (technical debt)

# Merge two developer sessions
merge_sessions(
  session_ids=["alice-frontend", "bob-backend"],
  target_session_id="sprint-23",
  conflict_strategy="latest",  # "latest", "oldest", "all", "manual"
  session_id="sprint-23",
  project_root="/path/to/repo"
)
# Returns: MergeResult with decisions merged, conflicts detected, attribution tracking

# Manual conflict resolution
merge_sessions(
  session_ids=["alice-session", "bob-session"],
  target_session_id="team-session",
  conflict_strategy="manual"  # Returns conflicts for human resolution
)

compare_sessions(
  session_a_id="alice-session",
  session_b_id="bob-session",
  session_id="default",
  project_root="/path/to/repo"
)
# Returns: Markdown comparison with decisions, constraints, patterns diff

# Get insights for last 7 days
get_session_insights(
  session_id="saas-backend",
  time_range="last_7_days",  # "last_7_days", "last_30_days", "all_time"
  format="markdown",  # "markdown", "json", "text"
  min_consultations=2  # Only include personas with 2+ consultations
)
# Returns: Most/least used personas, context distribution, decision metrics

# Get JSON for CI/CD integration
get_session_insights(
  session_id="saas-backend",
  format="json"
)

# Export as markdown with full metadata
export_consultation(
  consultation_id="c-2025-01-22-001",
  session_id="saas-backend",
  format="markdown",  # "markdown", "json", "text"
  include_metadata=True
)
# Returns: Professional report with query, personas consulted, synthesis, linked decision

# Export as JSON for API integration
export_consultation(
  consultation_id="c-2025-01-22-001",
  format="json"
)

# Full session export with all sections
export_session_summary(
  session_id="saas-backend",
  format="markdown",  # "markdown", "json", "text"
  include=["decisions", "consultations", "constraints", "patterns"],
  max_consultations=10
)
# Returns: ADRs, consultation history, constraints, patterns in markdown

# Export only decisions as JSON
export_session_summary(
  session_id="saas-backend",
  format="json",
  include=["decisions"]
)

# Example: Architecture decision
get_engineering_guidance(
  query="Should we use microservices or a monolith for our SaaS app?",
  mode="orchestrated",  # default: auto-selects 2-5 relevant personas
  session_id="saas-backend",
  output_format="standard"
)
# Returns: Synthesis from Pragmatic Architect, Snarky Senior Engineer,
#          Product Engineering Lead with consensus and tensions

# Quick mode - fast answer from just Snarky
get_engineering_guidance(
  query="How do I fix this API bug?",
  mode="quick"  # Only Snarky Senior Engineer responds
)

# Crisis mode - emergency team
get_engineering_guidance(
  query="Production database is down!",
  mode="crisis"  # Incident Commander, SRE, Executive Liaison
)

# Request specific personas
get_engineering_guidance(
  query="How do we reduce our AWS bill?",
  specific_personas=["finops-optimizer", "pragmatic-architect"]
)

consult_skill(
  skill_name="security-sentinel",
  query="Review this authentication implementation for vulnerabilities",
  session_id="saas-backend"
)

# List all personas (standard format)
list_available_skills()

# Detailed format with examples and metadata
list_available_skills(format="detailed")

# Quick format (names only)
list_available_skills(format="quick")

# List by category
list_available_skills(category="operations")  # SRE, Incident Commander, etc.
list_available_skills(category="specialized")  # Security, FinOps, Database Architect, etc.

# Example: Working on payment API
get_engineering_context(
  operation="CREATE",
  file_paths=["api/payments.py"],
  description="Building Stripe payment endpoint",
  session_id="saas-backend"
)
# Returns: API contracts, security, multi-tenancy, idempotency standards

record_decision(
  category="architecture",
  description="Use PostgreSQL for primary data store",
  rationale="Team expertise, ACID guarantees, proven at scale",
  session_id="saas-backend"
)

validate_against_standards(
  design_description="REST API with JWT auth",
  focus_areas=["security", "multi-tenant"],
  session_id="saas-backend"
)

get_session_summary(session_id="saas-backend")

list_sessions()

query_specific_standard(
  section_name="multi_tenancy",
  session_id="saas-backend"
)

check_consistency(
  proposed_change="Switch from Postgres to MongoDB",
  session_id="saas-backend"
)

analyze_changes(project_root="/path/to/repo")
# Returns: File changes, diff stats, and suggested personas based on file patterns

# 1. Get multi-persona guidance on architecture
result = get_engineering_guidance(
  query="Should we migrate from a monolith to microservices? We have 5 engineers and 10K users.",
  mode="orchestrated",  # Auto-selects relevant personas
  session_id="saas-backend"
)
# Personas consulted: Pragmatic Architect, Snarky Senior Engineer, Product Engineering Lead
# Synthesis includes: Consensus points, tensions/trade-offs, recommendation

# 2. Consult specific expert for follow-up
consult_skill(
  skill_name="finops-optimizer",
  query="What's the cost impact of microservices vs monolith?",
  session_id="saas-backend"
)

# 3. Record the decision
record_decision(
  category="architecture",
  description="Stay monolith for now, plan modular architecture",
  rationale="Team size and user scale don't justify microservices complexity yet",
  session_id="saas-backend"
)

# Crisis mode activates emergency response team
get_engineering_guidance(
  query="Production database has 10K connections and is timing out!",
  mode="crisis",  # Incident Commander, SRE, Executive Liaison
  session_id="saas-backend"
)
# Returns: Immediate triage steps, communication plan, root cause analysis

# 1. Get context for the feature (legacy standards mode)
get_engineering_context(
  operation="CREATE",
  file_paths=["api/webhooks.py"],
  description="Stripe webhook handler for subscription events",
  session_id="saas-backend"
)

# 2. Record key decisions
record_decision(
  category="architecture",
  description="Use idempotent webhook processing with deduplication",
  rationale="Webhooks can be retried, need to handle duplicates safely",
  session_id="saas-backend"
)

# 3. Validate before implementation
validate_against_standards(
  design_description="POST /webhooks/stripe with signature verification",
  focus_areas=["security", "api"],
  session_id="saas-backend"
)

# Load relevant standards for review
get_engineering_context(
  operation="REVIEW",
  file_paths=["api/users.py", "db/queries.sql"],
  description="User management PR - check multi-tenancy",
  session_id="saas-backend"
)

# Check consistency with past decisions
check_consistency(
  proposed_change="Add user_id index without tenant_id",
  session_id="saas-backend"
)

# Get observability and debugging context
get_engineering_context(
  operation="DEBUG",
  file_paths=["services/payment_processor.py"],
  description="Investigating payment timeout issues",
  session_id="saas-backend"
)

# Query specific standards
query_specific_standard(
  section_name="observability",
  session_id="saas-backend"
)

git clone https://github.com/amarodeabreu/sensei-mcp.git
cd sensei-mcp
pip install -e .

pytest tests/

black src/ tests/
isort src/ tests/