strangler-fig-pattern

name	strangler-fig-pattern
description	Migrate legacy monoliths to modern architecture using the Strangler Fig pattern. Outputs migration strategy, routing layer design, feature extraction sequence, and rollback procedures.
argument-hint	["legacy system type","target architecture","team size","migration timeline","risk tolerance"]
allowed-tools	Read, Write

Strangler Fig Pattern

The Strangler Fig pattern migrates a legacy system incrementally by routing new functionality to a new system while the legacy system handles the rest. Over time, the new system absorbs more traffic until the legacy system can be retired. No big-bang rewrite. No parallel operation of two full systems.

Process

1. Map the legacy system. Document all entry points (APIs, UIs, background jobs, integrations).
2. Identify seams. Where can you introduce a routing layer? Typically at the API gateway, database, or message bus.
3. Prioritise extraction sequence. Start with low-risk, well-defined features. Avoid starting with core, complex, or cross-cutting concerns.
4. Build the routing layer. A facade that forwards requests to legacy or new service based on rules.
5. Extract the first feature. Build it in the new system. Route a small percentage of traffic. Verify parity.
6. Migrate traffic incrementally. 10% → 50% → 100% with monitoring at each step.
7. Repeat per feature. Each extracted feature reduces legacy surface area.
8. Retire legacy when empty. The fig has strangled the tree.

Routing Layer Design

# API Gateway routing layer — sits in front of both systems
from fastapi import FastAPI, Request
import httpx
import logging

app = FastAPI()
logger = logging.getLogger()

# Feature flags controlling which system handles each route
ROUTING_CONFIG = {
    "/api/v1/orders":        {"target": "legacy",  "canary_pct": 0},
    "/api/v1/products":      {"target": "new",     "canary_pct": 100},  # Fully migrated
    "/api/v1/users":         {"target": "canary",  "canary_pct": 10},   # 10% to new
    "/api/v1/payments":      {"target": "legacy",  "canary_pct": 0},
    "/api/v1/notifications": {"target": "new",     "canary_pct": 100},  # Migrated
}

LEGACY_BASE = "http://legacy-monolith:8080"
NEW_BASE    = "http://new-services:8080"

@app.api_route("/{path:path}", methods=["GET","POST","PUT","PATCH","DELETE"])
async def route_request(request: Request, path: str):
    route_key =     path  
    config = ROUTING_CONFIG.get(route_key, {: , : })
    
    target = _determine_target(config, request)
    base = NEW_BASE  target ==   LEGACY_BASE
    
    logger.info()
    
      httpx.AsyncClient()  client:
        resp =  client.request(
            method=request.method,
            url=,
            headers=(request.headers),
            content= request.body(),
            params=(request.query_params),
            timeout=,
        )
    
     fastapi.responses  Response
     Response(
        content=resp.content,
        status_code=resp.status_code,
        headers=(resp.headers),
        media_type=resp.headers.get(),
    )

 () -> :
     config[] == :  
     config[] == :  
    
    
     hashlib
    user_id = request.headers.get(, )
    hash_val = (hashlib.md5(user_id.encode()).hexdigest(), ) % 
       hash_val < config[]  

Feature Extraction Checklist

## Feature Extraction: User Profiles

### Pre-extraction
- [ ] Document all legacy endpoints for this feature (GET/PUT /users/*)
- [ ] Identify all DB tables accessed by these endpoints
- [ ] Map all downstream consumers (who calls these endpoints?)
- [ ] Document current behaviour including edge cases and error responses
- [ ] Write characterisation tests against legacy (record expected outputs)

### Build new service
- [ ] New service passes all characterisation tests
- [ ] API contracts match legacy (same request/response shapes)
- [ ] Performance baseline: p99 latency comparable to legacy
- [ ] Feature flags implemented for traffic splitting

### Migration
- [ ] 1% canary — monitor error rate, latency, correctness
- [ ] 10% — run for 24h, compare metrics
- [ ] 50% — run for 48h, full monitoring
- [ ] 100% — monitor legacy for 24h before disabling it

### Post-migration
- [ ] Legacy code path disabled (not deleted yet)
- [ ] DB tables still populated for 30 days (rollback window)
- [ ] Legacy tables retired after 30 days
- [ ] Legacy code deleted

Database Migration Strategy

-- Phase 1: Dual writes (new service writes to both DBs)
-- Legacy DB continues to be source of truth

-- Phase 2: New DB becomes source of truth, legacy read from new
-- (via replication or sync job)

-- Phase 3: Legacy DB retired

-- Sync job during migration
INSERT INTO new_db.users (id, email, name, created_at)
SELECT id, email, name, created_at
FROM legacy_db.users
WHERE updated_at > :last_sync_time
ON CONFLICT (id) DO UPDATE SET
    email = EXCLUDED.email,
    name = EXCLUDED.name,
    updated_at = EXCLUDED.updated_at;

Anti-Patterns to Avoid

Anti-Pattern	Problem	Fix
Starting with the hardest feature	Failure early kills confidence and timeline	Start with well-defined, low-risk features
No routing layer	Can't incrementally shift traffic	Build the proxy/facade first
Big-bang data migration	High risk, long downtime	Dual-write then cut over
Skipping characterisation tests	Don't know if new system matches legacy behaviour	Record legacy outputs; test new system against them
Deleting legacy too early	No rollback path if new system has bugs	Keep legacy running for 30 days post-migration
Migrating shared database tables	Creates tight coupling between old and new	Separate data ownership before migrating logic

10 Rules

1. Never start with the core or most complex features — start with leaf nodes of the dependency graph.
2. The routing layer is built first and tested before any extraction begins.
3. Characterisation tests record legacy behaviour — the new system must match them exactly.
4. Traffic migration is gradual: 1% → 10% → 50% → 100% with monitoring gates.
5. Rollback is possible at every stage until legacy is retired.
6. Dual-write databases during transition — never migrate data in a single cutover.
7. Keep legacy running for 30 days after 100% traffic migration — then retire.
8. Each extracted service reduces legacy footprint — measure progress by % of legacy routes migrated.
9. Team owns the proxy layer — it is not a temporary hack, it is the migration control plane.
10. Define the retirement date for legacy before starting — migration without a deadline becomes permanent parallel operation.

---

Deep Reference Playbook

The sections below extend this skill into a complete operating playbook so it can run end-to-end inside Claude Code, CoWork, or any agentic tool without further prompting. Pull only the sections you need for a given engagement.

Inputs the skill must collect

Before producing any output, the skill confirms:

1. Objective — the single decision or artifact the user wants out of this session.
2. Context — system, team, customer, product, or domain the work sits inside.
3. Constraints — time, budget, headcount, regulatory, technical, political.
4. Definition of done — what "good" looks like and who signs it off.
5. Audience — who reads or consumes the output (engineer, exec, customer, regulator).
6. Existing artifacts — prior versions, related docs, dashboards, tickets.
7. Risk appetite — how reversible the decision is and how much ambiguity is acceptable.

If any of these are missing, the skill asks targeted clarifying questions before generating output. It never invents constraints the user did not state.

Operating workflow

The canonical workflow for Strangler Fig Pattern runs in five stages. Each stage has an explicit exit criterion so the skill knows when to advance.

Stage 1 — Frame. Restate the problem in one paragraph. Name the decision, the deadline, the stakeholders, and the success metric. Surface assumptions explicitly so they can be challenged.

Stage 2 — Diagnose. Inventory the current state with concrete evidence: metrics, quotes, screenshots, configs, tickets. Separate facts from interpretations. Identify the two or three root causes that explain most of the gap, not the long tail of symptoms.

Stage 3 — Design. Generate at least two viable options. For each option, capture: what changes, who owns it, what it costs, what it unblocks, what it risks, and how it could fail. Recommend one with a written rationale.

Stage 4 — Execute. Convert the chosen option into a sequenced plan: milestones, owners, dependencies, gating checks, communication cadence, and rollback triggers. Anything that cannot be assigned an owner and a date is not yet a plan.

Stage 5 — Validate. Define how success will be measured, when the measurement happens, and what action follows each possible result. Schedule the retrospective before the work starts, not after.

Outputs the skill produces

Depending on the request, the skill returns one or more of:

• A one-page brief suitable for an executive reader.
• A detailed working document for the delivery team.
• A decision record capturing the choice, the alternatives, and the rationale.
• A risk register with probability, impact, owner, and mitigation.
• A sequenced action plan with named owners and explicit due dates.
• A measurement plan tied to the success metric.
• A communication plan for stakeholders inside and outside the team.

Every artifact uses clear headings, short paragraphs, and tables where comparison helps. No filler. No restating the prompt. No hedging language when a recommendation is warranted.

Decision logic and trade-offs

The skill applies the following heuristics when choices are not obvious:

• Prefer reversible decisions taken quickly over irreversible decisions taken slowly.
• Optimise for the constraint that bites first — usually time, attention, or trust, not money.
• Default to the simplest design that meets the stated definition of done; add complexity only when a specific requirement forces it.
• Make the cost of being wrong visible so the reader can judge whether the recommendation is proportionate.
• Name the people, not the roles, when assigning ownership; ambiguous ownership produces ambiguous outcomes.

Anti-patterns the skill refuses to emit

Anti-pattern	Why it fails	What the skill does instead
Generic best-practice list with no context	Reader cannot act on it	Tailors recommendations to the stated constraints
Recommendation without trade-offs	Hides the cost of being wrong	Names the price paid for the recommendation
Plan with no owners or dates	Cannot be executed or tracked	Assigns a named owner and a date to every action
Metrics theatre	Measures activity, not outcome	Ties every metric back to the user or business outcome
Boil-the-ocean scope	Nothing ships	Cuts scope to the smallest valuable slice
Buried recommendation	Reader misses the point	Leads with the recommendation in the first paragraph

Quality bar

The skill self-checks each output against these gates before returning it:

1. Can a busy executive understand the recommendation from the first 150 words?
2. Is every claim either evidenced, labelled as an assumption, or removed?
3. Does every action have an owner and a date?
4. Are the trade-offs of the recommendation stated honestly?
5. Is there a measurable success criterion?
6. Would the author be comfortable defending this artifact in a review meeting?

If any gate fails, the skill rewrites the section before returning it.

Worked micro-example

Context: Migrate legacy monoliths to modern architecture using the Strangler Fig pattern. Outputs migration strategy, routing layer design, feature extraction sequence,

Frame: the team needs a defensible recommendation within five working days; the audience is a cross-functional steering group; the cost of delay is higher than the cost of being slightly wrong.

Diagnose: the dominant constraint is decision latency, not analytical depth. Existing data is sufficient for a directional call.

Design: two viable options surfaced. Option A optimises for speed and reversibility. Option B optimises for completeness but slips the deadline by two weeks.

Execute: Option A recommended. Plan sequenced into a two-week sprint with named owners, a mid-point checkpoint, and a clear rollback trigger.

Validate: success measured against a single leading indicator at day 30 and a single lagging indicator at day 90. Retrospective scheduled for day 35.

Cadence and follow-through

A one-shot artifact rarely changes outcomes. The skill recommends a lightweight cadence to keep the work alive:

• Weekly: owner posts a five-line status (done, doing, blocked, risk, ask).
• Fortnightly: steering group reviews leading indicators and unblocks dependencies.
• Monthly: retrospective on what the data is teaching the team; adjust plan accordingly.
• Quarterly: revisit the original objective and decide whether to continue, pivot, or stop.

Closing rules of thumb

1. Lead with the recommendation; supporting analysis follows.
2. Treat every output as a draft that will be challenged; pre-empt the obvious objections.
3. Prefer one strong recommendation over three weak options.
4. When the evidence is thin, say so; do not launder uncertainty as confidence.
5. Optimise for the next decision, not for the perfect document.
6. Make it easy for the reader to disagree with you in a structured way.
7. Ship the artifact; iterate against feedback rather than in private.